rock_paper_scissors_TLearning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "rock_paper_scissors.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/soumyadiptapete/Coursera_TF_specialization/blob/master/rock_paper_scissors_TLearning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "it1c0jCiNCIM",
        "colab_type": "code",
        "outputId": "3d4cb8dc-84d3-4192-f51f-0d2211d8aa10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip \\\n",
        "    -O /tmp/rps.zip\n",
        "  \n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip \\\n",
        "    -O /tmp/rps-test-set.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-17 11:43:28--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.141.128, 2607:f8b0:400c:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.141.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 200682221 (191M) [application/zip]\n",
            "Saving to: ‘/tmp/rps.zip’\n",
            "\n",
            "/tmp/rps.zip        100%[===================>] 191.38M   182MB/s    in 1.0s    \n",
            "\n",
            "2019-05-17 11:43:29 (182 MB/s) - ‘/tmp/rps.zip’ saved [200682221/200682221]\n",
            "\n",
            "--2019-05-17 11:43:30--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/rps-test-set.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.141.128, 2607:f8b0:400c:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.141.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 29516758 (28M) [application/zip]\n",
            "Saving to: ‘/tmp/rps-test-set.zip’\n",
            "\n",
            "/tmp/rps-test-set.z 100%[===================>]  28.15M   170MB/s    in 0.2s    \n",
            "\n",
            "2019-05-17 11:43:30 (170 MB/s) - ‘/tmp/rps-test-set.zip’ saved [29516758/29516758]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnYP_HhYNVUK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/tmp/rps.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/')\n",
        "zip_ref.close()\n",
        "\n",
        "local_zip = '/tmp/rps-test-set.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/')\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrxdR83ANgjS",
        "colab_type": "code",
        "outputId": "402b25a8-899a-4d89-f097-5d1f9d834e58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "rock_dir = os.path.join('/tmp/rps/rock')\n",
        "paper_dir = os.path.join('/tmp/rps/paper')\n",
        "scissors_dir = os.path.join('/tmp/rps/scissors')\n",
        "\n",
        "print('total training rock images:', len(os.listdir(rock_dir)))\n",
        "print('total training paper images:', len(os.listdir(paper_dir)))\n",
        "print('total training scissors images:', len(os.listdir(scissors_dir)))\n",
        "\n",
        "rock_files = os.listdir(rock_dir)\n",
        "print(rock_files[:10])\n",
        "\n",
        "paper_files = os.listdir(paper_dir)\n",
        "print(paper_files[:10])\n",
        "\n",
        "scissors_files = os.listdir(scissors_dir)\n",
        "print(scissors_files[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total training rock images: 840\n",
            "total training paper images: 840\n",
            "total training scissors images: 840\n",
            "['rock03-025.png', 'rock01-053.png', 'rock02-019.png', 'rock05ck01-118.png', 'rock02-026.png', 'rock07-k03-090.png', 'rock07-k03-071.png', 'rock03-105.png', 'rock06ck02-041.png', 'rock04-092.png']\n",
            "['paper01-004.png', 'paper07-067.png', 'paper01-008.png', 'paper07-070.png', 'paper07-059.png', 'paper02-027.png', 'paper07-052.png', 'paper07-024.png', 'paper07-064.png', 'paper02-108.png']\n",
            "['testscissors02-035.png', 'scissors03-034.png', 'scissors03-018.png', 'testscissors01-095.png', 'scissors03-028.png', 'testscissors01-075.png', 'testscissors01-023.png', 'scissors01-072.png', 'scissors04-074.png', 'scissors03-023.png']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jp9dLel9N9DS",
        "colab_type": "code",
        "outputId": "08f941b0-1a62-4755-eb1a-c7ecd2d752c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1529
        }
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "pic_index = 2\n",
        "\n",
        "next_rock = [os.path.join(rock_dir, fname) \n",
        "                for fname in rock_files[pic_index-2:pic_index]]\n",
        "next_paper = [os.path.join(paper_dir, fname) \n",
        "                for fname in paper_files[pic_index-2:pic_index]]\n",
        "next_scissors = [os.path.join(scissors_dir, fname) \n",
        "                for fname in scissors_files[pic_index-2:pic_index]]\n",
        "\n",
        "for i, img_path in enumerate(next_rock+next_paper+next_scissors):\n",
        "  #print(img_path)\n",
        "  img = mpimg.imread(img_path)\n",
        "  plt.imshow(img)\n",
        "  plt.axis('Off')\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HtDZuZJo5fvgffz8+//Dn71mG6iuNV/P5bsniCsfRCy/gI+9/H06ObqJ2Q++OnBKHgyJQ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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f+R7z4yd8+Td+lXd/9GNcIyoRQiYQMAe8oKGlxIcxTV7GZshWm4ZRUaIq0xyZ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\n/wPI82a5JHsSRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UvhxQTtQYOx791jdLcNTA3Kued91mEhFc8DTNHl07JXaRg4M9YtuVOBAyUDQk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\nuctNclcWd7nLXW6S/x/CmcSc2465zAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EQAtKynRb5oBswJbDs743B9c1KmgJB21Oybxa8FjmI/MYx5GUIeaycYwxlH0h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\n+COefPwhxhh6D5Dp/YLV6ha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xHwHqvGcYd7i8L1eCziWSC3jfg4Fh2OGsw5KLuSPaXCO3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\nGwWscVOv97hO45ZWs75riIUVF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RPWuLCElYBjORzAVXLNhOCI0bEfn+Ja5v7+nv3+nug8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\nCf5/HQH6GA/VIh8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J9dyEtT+pBalPAllcizkLbVQW9AcuCV/f00ajhxtawDKPjjayXRkV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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4xz52zpxa4jONIHWaJSSh4y002q21npqCVrslH/T02r/Rn+2OTnnMnuxvzdd3\nY3rdiS6MsLgTQ+0Ko5V5aGbVz2n7Ug62dFjJe/KjJ8dSnZbSWQLmTgypn7Pgrm6Pc444joyhOQNL\nqdTahEI71yHiPATfVkT8rKFrqfMGs4qvmdvXr7HZbM61RQq3TnIDk66j7AMpOLUGlG35ztPf5Nat\n21QCIURi8AQfmkCg4n1oL/31DpxrdQ4DNefmi3GVWiqXH32s3VdC1VIY8iN3ffa6WlGY8tnuc1hS\nYnpSy4ms+9aqm6yPzMdCnf0dp++HoIALIix0xJpkND24+tuSoEvaTQ6ozmvJkdWf6xK8X5PMI/Pp\nv5cmgCbJdEvIZddvTbJ9wzhSq6PWwhDDvFLgCMFRamWIkXFoEy76tiuz+QYD4HB4bl55i9tHt87V\nVfZPJ+2J18JO923/LSeXbOMzX/sqMQ6UWjjJc+i2m/f7UGe/RbM6vPcE76lz+DquBWRRC5/4PZ9a\ndNIujUdvg7wmN2jJ/u5purKxYlD6725myP7SiHYJVXbUIpda+30teCVfL5lR3ytdCGFhRbD131pw\nLEE4S7j0ezKN7HQtaGQHLwmTTh0C9gGyIkCtOmrSqMViGN0mmad1vT/zwMMfapGMw0iYEUb0MA6B\n4CGVQioF3+KaiDGAg1Jyg/k+cP3aDV558UVTiOo+1gLaar9uq2R4mcdbb7zB1VdfJbgMHlbRU70n\n45pAi5FyKmQqKc8nk9cKDWiQponHf/BT/OSf/FMmouv/5djrfu310xNxKb2FuGT7ZVu7cuub1nRd\numDRpoxcft11Bka/ptv3XulCCIsluA3vMuYuu8uCshIFaOlvweT+24rMk/WRv7t0twZDB2XJusp8\nrPgQjViWlvu0kJT5O+dY7x/MLwCebWnXZtE2VXAO5x1xiHjv26lTrp1j4Z2fzZNArXDtypUz9ddw\nXffXLsEu270E5XPOfOe556jAVH2LwKxAKcTgyKXOO2EdlELwAQ/UOu+Prc0USKXyfZ/5EQ4uXTpT\nf43StBADO65Chmb3PHTkqUSVGilr/4OlYDSyqbWe21Mil1etvpZzxUJD75UuhLCA86sN/ZpkLP27\np4F3d9VpCS41l/W8FlB6d96dIP+ugdjlMLUmurwny7fqJe+B7dMJcWBY71Fds/VDCG1i+eZNb9Vq\nAgLAhdBOnHIeh6M6B7Xy2ksvmn1hCdtd6EKnXTIHbt28yde+9EUGX3F1XkbEk3MlZ3DOQ61NwJ2e\ntelwVGJs53JSK8N6zcd+4JPtNC1jvGW9pPlqoU4dNwG791tI3pMv/9EIRS4ly3wt866jWdlnug76\nuS6kPjDI4k52pFxvvpNPwsqPDfFUAAAgAElEQVRr6Tkr/dLk1TD6btqxJPx03XRZ+v9SuVqjae10\ncOky+w88SPCOKWW8D20/iA+UUokh4p0jV4/zbeMVLhKcb+HUFbwPvPnSS2f6weoXPRbyt440XBq3\nTv/fX/9rfPPz/4LtZkvebpk2E9N0gq+JvD2mTBtyTkypbcN2tVJLptRKrYVSEjllfvLf/dN88lO/\n51x9LJRoxV5I4SEnuOYlORlleLb0SfRnpbNUIikd/KZ5oKOLGOMZASTrJYO5eij40urLe6ELISz0\ncqKeGBKy6cljMZ4mLSDkNc0csnwtJHR9l9qh22LVaUk4WgOr634nzXaKNmJgPDhsLxF37cTuGPz8\n31FrO0av1EpwkEtbHZlybisLs9Pw+OqVdg6nIqmFLT+P7uNOltNNQvXnnvwylIxznpILNaUmNHIh\nl8zJZourhRbyXZjS1M63SE1g5JwpFD72fT9A8GcPGt41Hpq3dP3681LoSd7U5qIO7ZZ1sMrXsRgS\nmerDeq3Njl2ghBDO1CWEcMax+l7pQggLLWU1HJPmg/VsJz2xNfNq+1KeC6Dr0tNb5WiHnCRtDliw\nV5cj0+1CWBo5WBpIUowDBx/6CHNEND5ASluCc9RSWlBWqUQfKEB1hULBhYr3tR2p5yBtN2eWT2XZ\nWntaQlWPyxIsLqXw1JNPcvvK22xPNty6cZPN7SO2x7c5uXmb7fXrnNy8iauJ7ckxm6MjpuNj0rYJ\nj1IyOU2U2vaNPP7xj+N2+LKWyFrhgrOTXzrle7stdGWhS4k++n09+Zfeemat5DnnTs/alCaTFGz9\nvSL3QhdCWMhO1tGVS52+a2Dk9T6IlhnSBVOHdjq/pYluwdhelhRqFgLSQkeWqZlDXpcfq636GQDn\nPePe3vxcC+lury2c8N6zmTK1OvAeXDu4NwZHKfPqQmlnYDgfOD4+Ptenuu+XtHavm4UyZD63j474\n7d/8jcbgpa3cQCHnCV8zrhTIBZcy1OalKDlRcmGaJpyDXNoOUz+sGMbxTP/KPtemm76mEafeaiDH\n0MqrkxYKXRDose0TXR5hYAXjyTpJc6eHictnU0pnYl/ulS6EsOiQyZoYevJpiQ7LqxawvHdBL9dq\n5tCQVGubPrhykujfvc6WELDqKmlJYFlaUcPh0wkI7O0fzE7AtsrhnGOMkZQz0bdVj5rb4TilZErO\n82pIO1LP+RYVeevmjXN9tasNS3XW/dLHIaXEqy+/zHNffxJHpdRCmjeMudCWQkNoqx7bKVFSoaQt\njkytCajk6YTtdkPJicuPfsQUBLp+GilYgtCasHKi7hLklgnbN5VJ86U/r0/ylnXSSs8KX+/Xpbmy\nhMq/V7pQG8m0k8lCFVLyyw7QB/P29LsmqzRx9HKXZKaldW45WBJVyDx2aVrdRiutFlqyvksa6DQv\n5zi4/ACuZCiZXKCWyqZmwJNqgZzx3kEqTXPUeuoQdaKe3/nWszz+sY+f7km4EwTX7ddCX96vtfL2\nm2/y//70/8HbL70wH7zrcaHtMp1yIbkyO1xHckpsS8a73CSi96z2D9qqjwukkvlDf+yPm5NfKgU5\nkfV1WWcdYHY3EZFaoOqlYnn4rnbeS0UklVQI4fT8TdkWjZZk/E8XHB+ocO9OesJotGHB3l3IQuch\nn+3pNfPKSSnRjNaQu9BMv26lkRpV121X3TXT67wtE2ZcrfHDmm2ayGnCO4dznjS/Y4MK0zbRIj0d\n0ByhnopzzTEafeSNl18mq/DhpYkH58/flO3VKK3Wyssvfpe33ngN7z1DjPgQicMK7xzRgXOl7WFx\nE8Elgi/gYAwtrDvljKuFbU4cXrrMY098fHEs7paWTGJJcmx2CaL+Xzt35cTWQlgHWPW03bSQ6KS3\nU69+WG8xe690IYTFkkbV31KbSpKawNK4Op0WPjqN1ihLk7P/XtKYskytibXzT+dptW9JKGhmlc/F\n1Yq9yw81gZAT282GaXOCy4W8LeTNlpIKt28fszm+Tdocz7EN7YzOXCq5Vl587ltcufLOuXItgaed\ncLoPLaT4S3/7Zxm2R1xaRQ72Bi7tRaJ3eD8Q44phWOEcpFqJY6CG2AK0nMcH2lF7FVyt/MhP/DE+\n+sQT51CetTQp+0z6F6R5seQ0tMbfiqWRvAGcC6jScRxSgEjh0POxkKY1Lrqv75UuhBliTTYpMeV/\nCybK33KwlqIj9W8rzVI5+llL2MgytMDS6Sy71ILPFqTXAshqS4yRMI5Mmw01RLxP1NJMiZozuc5B\nX2kC2ipJrbDN7VUAvf4nt25y/epVPvLYR8/VqZfd4bOFKmRQmdaiOSduX3mLw/399lJj3/rl5GRL\n2k44An4Y8OPI2kGqDl8ztVkmVNpJ5NRC9IFHHvsowziaiqWVl8/xlEZMus+tcbSEZf/fnYvSdyD7\np4+9ND0sISL7Vb4iU5oW/Vm5WqPrvt1uz/XD90oXQlgA5wZlVxSbZEJtCy6ZFBbctyBxvy5Jaw4N\n/Zb8LBrBWAhCawzLtJD17kx+N0uAACFEfIyEUsFlNqmAn899cK697s816L9NjeG2UzM3gpvLdVA2\nJ1y/epUWOh7P9KnuS0tIa3tctu3k+ITBVdbzGZsueKZU2Bv2KNMEzlGmiVoLBY+PA96vqJujlocP\n7XBeF8gl86FHH8WL+kihr8de96FGGhoF9fHu9zuvyjHuPCIPsbFedgVnX3vZn9H8Lc0QrcQkH+n2\n9N/b7ZbVarWTT+6GLpSw2LUMZQkE+eySFllCEPCutNdaUJerNbqevBbklnW2zAZdJwu9WG2wtFv/\nlm05zc853LAmTRlX2onXMRS2KZFKxfvIlLekUijAVKC62o6oS+V0r0Watjzz5Ff4vb//xzg4PDxX\ntpwsu0jD9JwzX/3SF1kPkRBXDDFSfYDgCGkzn34VqasRFwamCtO0JaUthUpxnsE3X0ucnaEffeKJ\ntnyiytUrblb/yvpLoayFQU+7y/SQim+pLrI+eplVCx5L4fT8+j3pMO0CxnL+vxe6MD4Lyeia4SzT\nQA+ghJVysDSy6KQRSk+vl8KWBIllYliBOn3Ad01y2R55zUon8971vNYuAHnK1Bne59RehVxp6KKd\nedE+bTNZpVKZ5jBqFwZuXrvKdrs1x2lXfayJ2P9vTk547snfbuaSh+rb9nLvHOM4MowrwhwHsp02\nQKbUggu+7ZKt7Z2t1DIvuVaGYTw3QeXOTs03UsB20lpcanM9aft/GW4ty+okTWprd6jOd5qmM6aL\n5AnJazJuo5NEckum8PdKF0ZY6I7tpKEdnI8A7FLZgmVwdvee9CCfTiIB/ZYQjcy35yknjGU69WtS\naOk0u1CHZGhdP82onSxktNrbIwaP80BK1FIJNGYsmyNy2lBLYQgOTyGlfDoBa2mTcLvdcv2ddzi+\nffvMmOlxsvpOCuB+vf+/fu0qb7z4nfY2NGhh3LWFpePaoTfOBwiROI4tTWg7Yl0YGeIwj2l75eJq\nb59hHMzxsfpcmgNyoi6dAi7HUKMUOYG7j0EqIgsdW6sg/flhGCilnEZfts1/76KHLnBkKHcXWJbg\nule6EMJCD4a8BufNjN7xuqO1ltCD2QWKFCzAOW+zLt/6L+GlZsx+Xz+n0Y82b5a0tfVbCyn9u5P3\nnmHvkJI3uLxtKCJPpJNbuHJMLRPBOwYHpIlV6NvV27kWIcxtq5Xj61d58/XXztVJ9vPSOOm+8d6z\n2Wz4zc/+M7bXr+Lw5OqYcsL7tgeklEJxnurD6aE2znmGEMG1076GcdUExXbDlDIf+eSngHcntexj\na+x6nbXmtiaX1v7aB6L5VwZE5ZxPJ7XkUen3kH0qD+7tgiGldPpxzp3ZVCaRjaWY3o84iwvjs4Cz\n0F4iCa11tTS/E8SXafUSmPSTaK+yLMsKfOpkLaUtIQFLm2hB1K/JCaidbVbfmX0KDDGQtict6rHS\ntPHQJqEP8xvJnGdwAxlHyY5CxdfUIL/3jKGlv3XjxrnylswNDbHlGNRaObp5k3defQUP5FIZhtg2\ngpVCHFYthJvaXmXQhReBzVSoeErN+PlVizG2zS+PffwT5/pQCgPJC5IPZFskmpUCQKbR46h5SLa3\nT/pxHM+hWBkfoSe7vK6FsFaC/V2mS2bL+/Fi5AuNLKxAEg2tdBotSfWk1hBf5yef0yZATyPr0r+l\ntl9qj5VmFzxcgs6yfyxEIp8JIXB4+TKrvRX7h2sODlas1+0lwdVVqitUx3ympWMcIqtx4HA9tLMv\nnCPlRAE2x8e8+K1nzrVRw3qr/f2eHK+vf/lLvP7sN3A+EoJnSpnq2jb57ZQJwwp8JBU3v2DIsU2F\ncRgZfGAVB6D5MyqVSx9+jD/4R3/yXJm6D+W4S0GvUZCcuLKdculTT279fhApcPqZmJLPel76uL0+\n4Xf5u3qdYoynkZ3TNJ2m6eXBu36Ne6ELgSz0gC5pVJ1Gr54saXX5f9f30oqErpOVv65bf36pDv2a\nRDtLzGmR1h66HpLW+wetzFpJuTSbf1yBgzBEKJWc59OoAE+ej9tzUCvVeaorDEPgyltvmnVZEpC6\n7fL/i88+Q/ShrbbkAt4z+LZlvuJPj/sPbl4eLYUhDuSUwPl2DmeoTNkTg+exj32cw0uXSdPUJhnv\nThLZz7q/NA913tJL83riyqXNPhZtQ9t5Hu7pez7dlNBoQSKRjihijKcmzdLeEcvU66H5+t57pQsh\nLGSnaTtQTjxrrbp3hpTE8lnNJLpDrZgFzTh64krzRaMTCzFoZpTXdZ3g/BmglgCxBt+qb62Vcb3P\nar1HcG2JsfiBnBO+FvANYaRpCxUqHu883tMOrvOOCNw83jLGyLW33jrTd8AZBtb1kaQ1+mvf+mYr\nw3nwkSG2rfI+RmquhCFQc6biSLkwhNmzH9qS7maaqBVKzuSSeeCRR7l65Qpf+eJv8fbbbxGc5+FH\nHuGxxx/n+3/w9/DAgw8yDMMZwSXbISeu5CcrcEq2Cc4Hei2tcmiTx+qXXlY/h8JagenPacd/COH0\nOTmnPlA+iyWJLSfH0inQfQD0eYM6rYZv/RnJLHqyW/4FCT/lQFq2ohYOFnKRDNUH3BJSskzLi24J\nNYBLDz7ING0pzhF8ZJq2VB/xwzibGYXVetXeu+FcW2Kt7Vh9lzObsmUcIpttoly/dhrko80O2WYt\nePvvPrYnx7c5PrrFKg54B0OM8w5XR5oywxDZbqe5HW2HbKUv7QZSbv4KfKD6ytHtm/yNn/qfuXHr\nJof7l3AxMHra29NjZD20vSYPP/EJ/pO/8N/y0cefONdv1jhrftBoZGlcrXTa4aiPguzUx1bzs+ZT\nrez67362heaNe6UL4bPQQS5LGtfSzHJwtb25hAzkfT35ZX5SEGkND2cPLNHOUpmfrJsWirJdcvA1\niloSfJo0ynGumRzrvT1W6xVxCKwPDhj39olxIMw7O4tzhOgpQHvDYaWWTMYxDiucc6zHyHaaODk+\nPue91+Mk2yjr2vv6zdffIDqPczDlSqbtQxniuwcSudYgamnmUy0FnOd4c0JpB1oAbak1bbdcPzpu\nL05aj4Q4sFqNjOt9YoztHI/tlteff5a//7M/w3ee+9aZZXQJ7fvY6WV2PXE1Pywph6UYIok+OmKx\n0HFfDVlCpRKVSPNJOkw/UA5OC85bnaMnoYZ5Mr9dWsGy9yUM1GXqtPJb/7b+a2eYLMtCCEsH9ML5\n4C+dRk/WEALegfcOF+fgpJwoacM0JfCOShMYUEkFCoFSHKU6UqkMoS1fDtFz88aN2Xw4K1h1v+jx\n6ddKzrzy3ReotVBx+OAJrm2N36ZpfmlQM4GChxBa3VuhlRhb0Fiphc32hFwmjk62bE+OZyFTGWMF\n12JLwjDgg6fMm0m+/Vu/zmd/7u+Z/CK1dx+jXStRko/k89ZqHryLZGVkpVx6lz6PXq5GrFopSXOw\n5y95pdb6vjg4L4SwAM50WidLGEgk0J8DW7P3PHo6+Yy8L7WfFjxSu2jNsQsFabJQiyX0elv0pLfq\nrwWspdlKaRvCVpceIueJwUHAMQZPjCtWqz2GcT1HQ2baWwJkv3tCHHA+MMdp8cXf+Bds5khOWa5G\nTfK6vPfqyy/z+V/4udNTt6Pvb0JzBByltnMqcp5aGHpJVBKlZqa0pTL7DnIihkAqiZffeBNfmpM0\nhMDBes3h4SGXDg/ZW69Yr9cM40AYAikVnv7NX+OpL//26SqC5kPtg5HCW7dL8yacXQqVz8HZyEwZ\n8+Pc2QNtpCki0/b7Peir/5avNNQBiO8HXQhhYWlwOREtzaufkxLYmjy7Ok1PeAkx+4TT9evptY9C\n1kd+a5L2amdQWdc7IRUteCyYK//H/ct4aKshzLEWcSSESC4VZsEQg6ekhKMJmegdtUAq724D//bT\n3+Do1s2dAtIS1r2er738ErevvoP3YUYQHuYDhXNt5k/JaZ4c7fBgB5TSvmtOzfE5Q+08TWxu36I6\niJSWTwEc7K9HhmHEx8h6b49xb38OZ4d/+blfY9puzyC8PkF3Haa0ZHrJMV2K3dDPyIBAiRKkANHO\n9E7d1OhCKYRwGuUpV1R0fd4rXQhhoeGahSik/dev3SmWQt/rJEN5JUnNLv/ridlJCzmLISyG63Ww\nTKX+WwsTeW+JWS3zqaf99B/9N3HjPrhACZ7sA6vVHi6uWK8OiG6AEhiIrMeRECOlZKYyH95bC1Mu\nrMYVLz/9NL/9hc+bQlCO1xJa+9w//gc4GkwZh+bYLADzMX7OOdJ8UE/OszYtGe84nRxtZ2whTRM1\nt9caHBwcUOPIECC6Sj65za1bR3gylw73GNcjqzGwPryMj5EXvvwFnvytL5ypozXGS2aIdEQu+XCk\nL6QrM8l/Mqqz56XHe0lQ9G/Z53Knq2Ve3QtdCGEhBYOEcjpNJ2nL9XtSmGih09NYwTQ9Xc93Cc18\nL3XX5oGuoyzDiu2wmEOWY8WeSJLP9j564NHHiAcPkSnzu7zm06VO60qLZzjta0d2LewbHM63FxOn\n3I7oe/G5b51DD7IPZZ9ICF9K4eTqO7gQ57a052Pw1DpreBy4QEqZ4Ga/Qa3kPO9r8cyIZ15C3W4p\nDnwt7A/h3dcdzCst02bDNLWw9nG9B7USxzV52nDlzTeYhEmlzUUZTi3HQwsI+Yw2Uzrvdd6UryW0\nNrhpn0PPv71A6exJWNpJriOSexpLsH+vdCGERScLumvtqyez/PTJp1dFOsnBs+738uS3/i3Llvd0\nvnA+itRqh6WJred0n1h+G1k//dzepctceuxjtN1ktDDuttwwh3SDD74dkEubkL6WORzctVcI0k4A\nXwXPS888bfahVVeJHE9OTuYwbtpmsFJxtZXgnZtfFNRtdSjzokedA8faZMvzcf9tleR4mwjetx2q\ncSCEyGq1Ym81sFrvUcNInSa2xxvylFivDwjeEVZ7vPPGa5ycnJhjqM0TSdJMkWZCj4uQvoSel7Xk\nLhVGv67N3p6mmx3aPyfrqoPINL/eC10IYbFr8lrSW5Ke1FoDWOXcTSyEZBw9eJbmXkJDlvbVy25S\ni2gmkHkttVte03n2//sHh3zqD/0kce/SvEmsgKtApdYM3uN8+93fWlYrpCnN0D/h5vdx1OC5+fZb\nbOZJZrXZQom1FJ7+2lcZh4EhRkqBGNqrFXG+L3Y05BADIXhiiLRwoMDgPXU+k6PicWGkOM8r71wB\n5yk+EIYVcdzDhZG4usTe/iEHhw+wf/gw6/1DwFHzRIwryJk3X36Jo5s3T/tfTkwLceo2wvlXZ8p3\n4GoUq/lCooEeHyGRQc+nR2P2zWOdh2KMZyI1LTTxfggKuCDCAs4PiKXN9X3Z4Rq+W9pbl6Phox7Q\n/m2hDf3M0gEj1kBZiEALvSV0odNaSEzn3Rnp8sOPMK722U4JSqIlqS0gqj3dGM5BBcY4Q3Cg0JyQ\n1TlqbU7JW7dumXXUZlj/nVLi2994Cu/q6Qaw9pbESkpbUk64mkhp9uqXFnvhnWv1c/MkmSfFZkoc\nH5+Qjo8YPXgcvptTFVzNbFJ75yu+HQIchpHiKtvppL0dtTZkBef9Xb1N3Wmox8fqc61kaq1ndpDK\ndJ10UJV+r4hGLhJd9CAsjUp0Hd8PujDCYmmiyvtWGq3RrQHXJotVdh9ArVWs8q36WfWxlly1xr+T\nUNT5W/W26mSZPqv9A9aPfIRKIXg/myFuPjGr7dFwrr8oueK8J8zOzeDBz/4OHzy5FK5fu2ZqXAsF\nee+5cuUdXn3h+cbYFZyrcz1ci6OA+c1oswCeubOtlgSqi/gQcK5tVXdUjm4fceXWEdVHxuBItQsN\nR8Ez+ErN/ewHhw9xPpavvYxkGFen0ah9/PSYyDGVpoPmT+l/yDmfChkr5qLnI8+f6Pd7eV0IOHd2\n+7kMvJICQZ76LQMGNZp+r3QhhMWSf0D6Hixzw1pilBBOrjgsTcL+TE8nO9zKV9ZlF0KQg24JGm1X\nymcsoWjVo7cT7F2Fum/G9ZpP/ugfJvjmACw5Ef3sK8gZ5wKuurYsmTYzE7fXAtQKU6Zp6Pl1h5//\ntV9lc3JyxuZeChg7vn2bz//KZ7n16osE7wmhCYHqHJwmb47VENpmMucDLg5U71t4ug9M2eFjBOfZ\nbI65duUtVtETfdtfMg4D7S1rA55ILp5aodS2Uc7Vip9R0nZKPPzYE6zW69P+0jEWGllqhdKFgTy7\nQn56fhYy7vwmUekSP2v0Jk1jaYp0waJRyfuBMC6EsAAbrkvprQOVljpAeq61g0pr8iVnkGQYbW/q\nOlvowmqPru8SstgV5n0nNKLNMP2Mc47LH3qkvRc0l+Y0LBlX2+oIc/SjD54wrmCOb8i5RVoG76nV\nkVIhhMiLzz7N0dHRuTrovgC4fvUqL37rmXfNnNL6Ofp2lkVbHi3E4Mm54H2L6ehlUzOpZJyvlNyO\n+ttMibdvHjX364xOSgXvPKVkSoWVdwyhra7kUhlCJIZADAHvYbvdEPzZWAcN4bVZoQP1JKKQY9KF\nSB/H/r/nrX1Xvf/k8mn/1mdwSv7Uzm69LP+BWw3RUK9/6wbrpTlL8oPt47DS929pF0pvtpTiPV/5\nLetooQVJGm3oAdfPWhDfEmJL6MsKJrv88CNMpZLyFl9Se5HwLDC881TvILS3p0dXWsh1XBF8C9AK\nDrwPjN7x6gsv8OSXvtS2jKvyNcL47D/6OW688kILOXduxhAwpdKER313V7F3ASpzcJTDuYoLzb+B\n94ToyTXzyltvM6VMHCKXxoHB+7Yz0rfTvmrOTLmS5jiMEEZSdWQ8eI/3A9Q5EGzBTNQCQ0Ze9vFa\nWgLtafozGrlI/4Pmecm/Pf9a62k4eD9yT/KMVqZW6Pm90IUQFrJh/b8kKdWliaAHsv9esu+tibZk\n8y8hCy1wdBnWPSncrEHT0FVek7RkllkCzKpHM80izrVAK+/bS5C3JeNCO6XK1Up7G/kG7wOrITLM\nwqOUQi4Famaqlen4iO8+963Td1JoyCzpu898k+gcbjZpmtPUkXGkPEdm4shlvlcKzs2RnTnPkacV\nX0tz0AK3bt1sbysDttUx46O5nrOCKaW1qTq8m1dRgJxnIYE73ecilx81D2jTTyMC681f+t4u9Aec\nCd/23p97YXe/JhWo9F9oAWcJqHuhCyEs4DyU1gyn0YP81uaAtp8t5tXPWs5IS6jIulh5akaSde5k\nRZ7KsiXj6nospbfgvxaatVZ8CAzrfZxz8+pDZogR5lMua3NmUEoLpy5lDiWu7VyJQGmvMcyJVXC8\n+tKLXLt29Uy/a2icc2Z789ppWHfp4eW+2exDaNEd9DHBQWkTeUqFSju4t1TgdKIVbty6xeg9VIen\nx2gUPO3TTB3HlFsEKKW0977OS8POO9J2M79YqZ7jFzlRl5RD5zO5kiEFhOUD0Uqv593P19RCq6fr\n9/oKiBYkPa8uZDRv3CtdCGGxS1v231ooaDQC57cZL0F6nZeenEuMofPR0lpDWOuefM6C7fo5y9lm\noSEtZJcEmnOOBz/xQ9RUmKYtuUx4l3F+fls5mRjCvC29vWWdmtrJWa5pv9XoGUJgW+Clb3ydz/7C\nL5wTXKcmWc688NxzUBLONd3vXdvF6n1Lm0pb5kw5kUsmzYfZ1PquRs6l4IM7XSk5OjpiSpUJx94Y\nWzCZAx8CU43EMMymQWCMYS4XXG3nYTh8M3W2G46PjwHO+AqKEiCWmSh9Yh3qy8Nqep79cKCenxYm\n8hQsPYZdOPT70gGv00lTXQu9D5QZcieNqDW+FgZwPh5eP6dtuv4tJbiFUpbqvEvb6GtLpsJSG602\nWe3RWkrXzUIvlz78MVJuk28YYovOLPOGJA9t20bBucJmO+FCg/fdbJmKo7gwxw9Uvv3VL5/mr5fz\ncs4889TXib5pfucc0Xu2uW1Wq7n5TVqId0d37dyKWkqLyZh9GiUXSqnUUrl16zbjfP5GpjbEQmPo\nVfCUktrSaGwBX9H3kOmBbZpNnAonJyenE9earL2/NdTv9y1hLgWM5ofeP12A9CVWicbkad39We24\n733b+9lavet1sA6Nei90IYQFnIV0SyaJ9b2EBuSzerJZZetnOlnHqElksKTl9aDK8vVzlg1rQUgZ\nuWcJmqX/Ov/DBx9mfXCZVArMYdPeeWJsR+oxT6SCI8YZdXlHcbMd7cD75uj0IfDSd77L9evXTSF7\n9epVvvmV354P2akE12IhhmEgFwc+zkKnCQLnHM6HGUm0JVRmVNDNjJQmbp8cEx0crMbmfHUQacFY\nsR0OeLpHxPtIdS1sfZvnoK2SAX8qwOTk7n0t29JXMaRZ0dNrP4Vc9ZDoo9/TIeHawSl3kWq07Zxb\nRBf920ISHyhhAe8yuRWObU10PcHkdT3prQmoUYZML5lmSZhYWkg/rzWRZi6wD17V0FJqFSkALfNF\nanYd1QfwxA9+mr0PfZTBt81aXWOnVHG1af4QAq7ktk8kOMgZX9sW9zCs8X6YzRJPrIW/8F/8eX7+\n7/893njj9VNNfXJ8zGQjQZ0AACAASURBVK/84s9z5bvfIgZHjHMgmAuAp+T2sqOcCyFArZmcC1Nq\npkIzRdruUuZl1Vrh6PiYb73wXXyI7VyO4ClhIMaB1TCSaztxq84O1NCDv3CsoifnCR8C4+Dnw4rT\nOd7q7+nofSdDuPu46g1mGunJcZM+BblfxJrE/TktoLRgWULjFjp9PxycF+oMTmuC6d+S+qDoGAlr\njRyW15uX8tf3Lcaw6ijX0XctdVp5WtBVm1wWaljSHJJpe/phHFk/+DA3X/0OBEfJCR9WQKHOJkmt\nBRc95Ab7mTecxRA52W5aiHZ+932q77zyCn/z//wrfP2rT/Ln/tM/z/7BAa+89BLPffVJgo9QC6U0\nxBLiPB7Mm8JyphRwzlNq2y/iHPhKW9VwFefmU6Cc4/joiJQLl1Yj21I49I7BBarzbHJhLwSq9+Ra\nm/kzIxPvHe290K69Nd4HhtXIer132vdHR0fcPjri7bfe5Oo777C3WvHQox/m8Y997JzzWgpiOO/P\n6N9LYyzHUjrYpSLQqKOn0Xyt/SR9HnRz5/3wWVwYYWFFmknJrO3EJbNCB68snTwNu0PDNXRcEjRW\nfaUPRMNZaaJoISTr3fOTwsPSYBJBaASjzZrehvX+AT/w+36cd57+ElNOxDjgfDsyL6WKo8xH7Lu2\n16zk023j25RwHtI248NAqAlqYD2sOJkyn//sZ/mNX/olBgcPHB7yoYcf4IHDfWp1hHlvR1sbzWzT\nCRCaiVAKPrQlzurcLKACwdOO2StQS2VKE8+98hrTdsvhow+B8wzrNXEIrMaBIUQKDfFU13ws3jvq\nvFoyBMeWgKcy5UwcV/jg+ZVf/mf81q//Ot/8/G/gnSMOI/sHe7haWa3XfOoP/EH+jT/1b/MDn/r0\nmXGRJ1RJvpJjIp2Y8r7cM9J5FdprCrVAkZvF+hh3HpPzwTl3+rrDJR5/r3QhhIVkbGnnaQ2t/QXA\nued0x+j1Z2ti6vL1b8uu7GVbKMFaht1Vd2lWSMF2J7tToxCNRuS1nldv06WHHyHlTPD92P93X7NX\naVGVtDWDU4cnVMZh4ORkwypACZ5SAo7MED2u5nZojg+c5Ewc28uMoisEH6mlkByE9v5znJsVREnz\ne0JyExTU+f0g8+arkls5rp1wdXx8TPSO41y5tD8QY1tZ2aTSzuf084nlY2jvUC29rBavQc3g2ntd\nV6uRmzdu8tP/01/i5OiISwf77K/GtpTrAnFs+1Ge+/IX2R4f8+f+y/+KYRhP+1eeSCX7XfJkf/GP\nHIda6xm/hl4lkUFYch50QdVNoP6snDeaX3Yhz++FLoTPYonJpa9iSWta2luS7iitbS00I8uX+Vpm\nkCZtAsl8dX4WAtACTNdR9oeM0NNtWxKG/fm9g8MWYDXHTTRmy83+r01YtDiLln4cAs5DKRMFT64e\nT5ljH9q+jIRnfxxOz7xYrwbWAUIMlEKLraAtp9ZSKNUx5QzzQbrz3tI2IXLC+dDeIeIj1fUX+Hhu\n3Doiu8B6iAzN2dEcos5xe5vAtVcyplJxtbQXK9cWol76uM0C42Sz4Z//8j/j9dffxNVK8G1J2MWB\n9djOHsVHXFjx4rPP8N1vf7u9UFooNE0S7cnAqX5NIz05Zh0VyFceSkdoPwBHChA5NyTy6GbSB0pY\n6ElhSUfZyfqjbcc7BdNI0rBuV8feSejo56VWl8Kht8eaxJZTs38vObe0E3ZX/TrtHxwSxnULxCqZ\nWuZt4bXgS2bl570geYLSHI/eMaORpuGrC/jgWa9HgnfsrUayGwnjivU4sjfMx+RVxzhr/FzmV/il\nArkybVtIdnNyRqKPxDg0YeTn1Y3gqTnhvOPk5DYpTRys14zjmhhGQlgRQiTMDtTgW5i6n3ehplxI\n85mcYQ4Ac64tB3/7q0/yM//rX+ZgPVJc2zMSY8SHAeKa1fqgrbYMbWXor/+lv8iXfuNfnHkVoWVe\ndn7txwDK8daCoyMMGZAl/Q9aKGgUbfGcFFR6i/x7pQshLGB59aGTDIXVk9kKztIwvP9eEgSywzWc\nsxCCzEdqjg7lZZ6aqXq6o6Mjrl55h9deeZk3X3uNN994g6Nbt8z186VlNi18dBv7f+kTcq45Jdf7\nl0m5zlvSG5roR+uVCilNVGrbx+FaVGd07ZQqRyEEzxgCU8owR3+GADFE9oYIzI4158ilbW9vCyuZ\nXNshwEOMM6pop3jnOp/y7ednXKuXc80kSimxXu0xwfzKguYoXQ+B6B2X9gamWbAxL59G33aytnY5\nSmmHC6bUhOStzbatmARYjUN750iMDDESQ1sybmPQNP9Lzz93xrSQfCYFQu9rvWoix7CjhW5aaNJm\nphQmsgx5Cri81/nwA+XglL6BTpZmteIMdJTcEsST95bqYC0z6Une0/Zvbbr0uuhJK8u5dfMG/+jv\n/B1efv45rl15h/3VyPrSAzzy4Y/w+/61f50nPv4JPvToo+dQifR77HJe6TpbplHcuwTX3mC72TAO\n+8QYmDZ5PvzG0TZzNSuBCoHMVMHVSq4wBM82JajzJjSX2RQoaYufg6G67Z1KEzAlJ8DjfSBlYH5V\nIt4xVQiumRTBzftE3Kxxa4s43U6Jw/XI5UsH7eRu34Kxbm8Te6NjM008tHfQ+o1Mri0uJPi2otMO\n02mBXzEEbp1sWcXA7VS5vIJtLkQHPjRh5ecXH3kXKWlLpfD6C89z/coV9p544hyiAM6hg37NMjv7\nPYkWJBJYChSUZUrk0RFKd4j29O+HGXIhhIWlCS1oLyWpTKMnp74vn+n5aeeg7thdk01Le/m8VZZu\nz1/+7/87rr/y4uleAO88b1+5Rn3lNV5/9mn+5a98lvUQOPzQo3z6R3+MP/ATf5Qf+pEfAc5C0C4w\n9Fq8FLK9DrJe/f7DP/jDXHvlWWrOuFLmHZ0RnwPbbZugwVemaQPz+0ZLhUrbWp5q24wV/LxBy8Ho\nIK5X7bTt/X0cHtediiRKyVDjHHQ1MuXchAWxrXw4R62F7OfdpXk+tJfKlAsn24mDSw9yeX/dkEr0\nTPOZG7lU9mIgVwe1MrjmSnW+HfDTV3Ucjlodm5Q5OTpiO2150DvGMMyH8Xg2qeBiIrqRIThy9uBH\nyBtuXrvOtatXeUwICziLGKTikf4G+ZIh6XfoY2uhxp6287lED/05OcbjOJ6bM9oMfS90YcwQOCtp\nNQKwJnYnaTbIPDrp8wa69JYkJ5/uXEsAWGaKhp9W/teuXOGdF17gocMDHnvkIR5+8AHWq5GDvRXj\naiQDB6sINXPjzdf5wj/5ef72//6/8ew3v8Hx8W1yPs+Quo4WipAIpV/fOzhsENvByWZDLqUdglMh\n13YMf04bSoVS8rxqUom+xT5UB+MQ2xF8AapzxBDeTUNbJQ1tRzi5zBu6aO/1yHmaYyg8ea5qLm1F\nxNVKSlM7v8K/G7m62Sb21mv80M7fzKUy+LZXJboW+9HPy0i5UJ1v/oo5QCuXPJtElegqJ6UyR7jP\n4w/OtfqG+Z0pJed2AFDJDMGz2luzWq/NcZbnV/TvrtB6v/cVjj4umpdlXEQXEhq9SN7T80UjGymE\n7oUuBLLQKALOow39klhJOu2SJpX597Rdyi+hG4s0apBQUN6XeaeUeP4bT/H0Z3+JP/Kv/KvtDMsJ\nSk3kdItXXn+NbcpstoHtlBhDYDNlgsu8/fpr/NR/81+zPrjEZ378j/Af/mf/OXEYgHchrG63rrvV\nr/uHl3EhkrYnrONE2TraCVQbqI7oa3ulYdk25OFaOHilUPCEUsk1MMaBzVQYApSSGIaR1Wpoy61+\nPkzHBVzxTJkmZELlJMEweKZKQyqpMAyeWhLJB/y87JlTIk0T2+Pb5FJ56PIhcRjxJMpsNsQGJhiH\nZv/jHFTPUDJ4yNXjSiHlhuQy7VWBt4+P8K6/QKkSYmgb3RyQ2yrRdlNh9mlUPPsPPMyDDz10pl+l\n9u8+J9nnUqHJ1wx2X4U0L6Vg6GaF5GXNf7IMjXo1yrwXujDIQjbwnDOO8wJB/rYcifK/tVIi7Usd\n2wGYk24J3SyhDK0Jrr31JhHY29tnGCKHe2vWq31Wqwd59KFLPLA/sF41z34746GeLvfFEDm+dYsn\n//kv882vfY1puz0DRZdMkF319j5QUiKEwHZq7wlNOeNCIPo28Usp87H77WRvXzM5JVzetjM5a3v9\nn3ceciI4xyo2Tdj9D7m2ctsyZ1tJSbW9Fc3Vpr29cwyx+QlKbciktjXcJnRqIQ6R9XrFuNprp367\ndupVqlCcIw7trIcYPOthaJGaJZNL2wtS6uwEpDlRT3Ll5GRDwpPq7AhmnvzzuxpzSgyuEOb9JlPK\nDKvVaR/Kd4t2pabRgtwb1MegL5HKoC45Zj2P/npFGZm8y3zWyPb9EhRwgYQFnJWqcN4P0ElL7X5N\nD5IV1CT9GBaa0R0ty9PaWjOAFm5SIEGbHONqjfOOMQ6sY2Avelbjir29Q/b399hfD6zGkTgOFFzz\nG4SI8xCjh5r5x3/zb/Did75zpl4yYEdqGdkOWS/nHFOaZn9DC3/OaQvzs9PUHZfNkdkEAqRpC6Uy\npUKubV9F8BVXp3aqFW2zVw+8GkIglRY3EWYN7uZjr3Kup/6P2pdi5wBP5uswH1pTKlNpjtTWqEKe\n31G4GlpQlg++TWkH0OIoKm0V4//n7t1ircuuM6FvjHlZe5/zX+riutpVdrkcxy5bjhPHOO1EDek0\n0IRWWtAPPHF5QbzxgHgBCR54BglEq9+5vYCg00DoNBARQQsiASGEbqWjAAKFELtjOnFs13/2WnPO\nwcMYc59xxln777iqHo5q/vq199l73fZaY445Lt/4BpFYUZq5oASIdNREYGIccsJ1LRCBNo0eHVov\ns2r/V86AKLGxIjzL2ZLw3BJ7i0TM4vnvfMMhP7wS8tmUvUVxjpgc+KgVxoNRFpcE2o8YM7g0KS6t\n/nFiXbrxXjHs+fp71+i38XGPqe2ZGe9+5cfwwttv4/pRRV0SMJr62iAs5SU8un4Nb7z6Mp5cL3h0\nPKAeFlCulikg3LQOSRm//zu/hb/0L/+L+P3f+z1DJ97Pscf7tHcfbt5/X3EIAkgbWE/P0LYbkHT0\noV3We9sgw2ozhEBlQT4U5EMBM3BY9LVmjQrkrKxaSIxkZePZmhZtXbQ1opWMJx4QdAi0UAxjoDcg\nEaNtOln7GMpq1QcyJxRLay4l49Gh6O8T5bEQYlDSfiINBTxTptKg3BhicZOh9sMY+M53f4CC6cqJ\n1aYUkGFISsnq0cgAJ+13QpywbdsZazEt070MmAdG+f/eki2l3OOqmDI6LZC4cEbcxexzCuBOwN8f\n68OOB6MsvN8fhTv+7X30Pfdk78bM72awyPdmuJSeuqSN99wbP6KCmdd5/eQJ3vzCexiDIMxISYOI\nbH0/tZFOxfVVxWEpyMwaUMsJiQlLKSACiAmjbfjVX/pP8d0/+qN7Qdk9oFn8XUTKErU141BgJbop\nmVESKQgKBBpqgo/RwcTgsgBJV9XMpPELS4fmZQGzNgciYqzdOouxoj2XrM0EZtewNtTVEqgbAhEQ\nbsvSFZWpWRbNYghKtrJzArZN2clBpO5BSmoBAMAYpuQAITYLTYFmQ6OW6L0ZSxcjJUYXQp74jtGV\nBnAMJHRkjZqCGXqParmjnKOyiIuTjy3FBcb3/YjPzSuE+d9/PxXDtFD8fpfk84OOB6MsgPuaeH72\nPLchZkL2HgZwF5MP3Eat/Wd717LXqDau5PGcl9wbZsbLb34STUy4esdoN9aTg1A5Q6SiZkZOhEMt\nmlVIydidbGVMDEkJ/8uv/HX8jV/5r/C+MWxPxXepjsX/hjEGTjfPwBgoTOhkK1LXFXiySgFaFl50\nTlotSdaCr1RARlHHKYGJUUoCEVBYUHNCKcWaHTNAgpoZSylY6oJatH9HZgJIQVeJAJCCwPoQtNFh\nvgTWZuzdrFBu/R2aqVB0qFaZ6oqsfUHauZXB0PJ6JhROIBKcmhL5XtWs1Hti5j8JiJWnk7kASBBj\n+GpN06+j9TuTMcaOvBz52JJ/Fj4AHuVxBj59dsXzfHqFMAGLPvbhF6uPFfnNXgzCf/68yRg17bw5\n/hhey8ZJc8mS8eeNN/pSXMMPL0j+WGVZ8N6f+3nkp0/AiZQKf32GzACxAGAca8ILj454fCh4+vgR\njssRpWTkUiCctbyLGDdrw1/79/8d/Kv/7D+Dv/Otb91L28U4S3SNTjfv47SdsPbNVqgGhiBxwWE5\nYDkuqEtBqgekwxVSTiDb5mCmczkckUrFYamoJeNQEyglCBeNQxi5jiI+gZSBshSUkg1VOUCkcQ1h\nAiVVCIrONFJhslhETgAIiQiAIGUgZ4KMDcdEVqHK2Mb8zaKtGvsthmQbHWq4EE7rCUutoFrx5OoA\n2MrdOmnPESGsXfEdJNqCoHVFshLTHVmbsYU9t8FDvue23g2ZC5J3HUop5++9pRgXqNhzZM+aiFbz\nBx0PQlnsuRJzPO9H7lkOe27FnlUyt90LTPptLgVNo3KLASiPBI3nfvHV1/Dml74CkYEBoGFAUsJy\nOKIuBy0ZTwWlFiTzkzXImVCXg5rkpCk+koFn3/8efvE/+Pfwf/7vv3OnI3i8h1Gxtpv3rZWgAKYE\n1P9l1KUi16zKIjFIlNdCjNh3wOpDxlDcQ86qAEo9n4s5oaaMREN7lzKhJC07rzXDPAhFiwJoomXk\niRSWTQC2PtOg1g2NcA6UiuEfamY0EWQrKiusFsXs6SrDsjXQidcF6KIVnbVm1JwhxDhkbXVwKBlL\nzSiZUYuS6qg1xSg8Ze5WTn3l6Swy23MrInYigrF8Jel0K6aS2aPYi8N/7l8/VjiLOemjVtybxHsm\n3J/0HPN1DzbuJ7r/+xL8fG4TP9+D4MbfKUR4+wtfxB//nW/jW3/rb+L6+gg+PMYYgrye0P8w4+oA\nEAjP1oa6LNjAYFa+yPebrXBgCAtKJvzm3/hV/Mav/td46fU38S/9m38JV8cj2AHPomIUEfRtA3NB\n7xuSNCXubYx6TNgYONIB3E/gPMBCaKkoyEmAWpJCwTMB0EBg0rADuHQkGihkVHpQ9GRORWMUnCyz\nYSsyE0S010cfAKQhM2Ft6howdaxNkKShsLZOZBal6kOHtI7l+gpCGTkpxKoYJiInzfooQFUAYhB1\ngBR09fj6MUpdcCxFGzSnAmHCqQ8sKWHrTa8n1XPZ/OOnL9xb4b0s7lmx0dpjZqzreid25l0Kb436\nuqBpSURl5GXXy7d/9h92PAjLwvtV0fyfn8UbMG9cVCI+vuE1uj9WRHTuxUSiBRGvd09pXAoo7bks\nRITP/vhP4MV3Pwck1tTl2LCNDTnpNedStON4Lbg+qOlekgYgS9aAJCfGgPq2pWT84bf+X/zNX/+f\nz/n5+NvuQIxFeR6IlfdhyDAWKzEa/YRDrVhydgup4idINKWamTQOYQVYKRUstSDnjMIESEdOZGlY\ntYhEgGrd07OhJJVF3NKzXVGdM+g6YCzjrGhRsmZHNDqSpU5ztuCvqOXTez83PO7G9KUWmQKyiBj1\n+FgxL3WBiJo5mclcQk0pMxFyrhrsJdHAdGgXGBeGaPHGGNJ8FrNR0B4yMy40XlFMOfP4IL9wze2i\nm/Rhx4NQFgDuKYr5mb/p0ef2WnovvjBvqg9S7plpUTHFQp5Lrke83ghP3wsqej/0+slTvPvjX8PT\nT30ao3f0dUW/+T6Kmey1ZNRlwdVS8OhYcX3QOAEni64TgShjtIaaE8boSInxK3/1r+D3fvd3d6/3\nznX2pgHOUjCIQJmxrSuYEkpKqDWDkro7mTR4WHIGka7YAs02kPY/tJgFo9bF8BTWy5PZGMTNlYCg\nbc1AUwJird8YMhGOGSJ0Tgu30a0yU0CJUTKfeTYzq5WjSmrRSWzXBWlovVnA9PZ556QG9fVScHU4\nWq9Q1obJJj4yulXRCTLN1o0DEGXOiriWGZeYshgbE8dKZt/oeDYOisxZXkF4JGdMm8Y542XQK44P\nOx6EsvAT3mtO/92e9RCP4ZUHcJ/1Ctg3x+Pk99j8vQm3t090YeYxLim4KRQvvvoqvvpzfxbLi0/w\ngz/6A9DpD1FLMf6HhENJONaCY2UclozjQZv/HpaqvjQBNWcMq+QclPC7v/W/4i//a/8K/tgYt6My\nO9+nsqBvKwgDtVSUeoVar3AoBYW10vLq0TWOT55gqRp7OCRBrQli8OdEAojgWAg1DyTuKBmolXE4\nVixLQi6aBh5dkZOFgWzuCRNpgdcYir1AP/NlImdsw9CjRLi+PoBn0RozlkVTzMtS8fTJFY5LUT4K\nYlM4HdtoAJPyXSTtRkZjYGsdV4cDrq4PeOHxNZZaMIZgE3WzNMahwdRhJfXE2sLgcP0Im2v4411P\nz3Phh8c7RBnycubp84joDsdFxEt4uYoIz+jKfGwsC69544+7pBXjJL703sdD9uIT/thRSfn30dSL\nx9773J9v7xrnfilnfPGb38TVi48VgLVu6H1gyUBlgIauKLVkXB004KirtZrznJT8fiIlCQnf/c53\n8Mv/8X+EP/j2t88rkhdSPV7RWoreMYSVHDclbL0rwxRUEShpDJ/LvElEWxrapNSOZgTpQ3EPGMiJ\nYDQzGF3xE4yBtp2wnTb0rWEIUJICrVg0RcusOApVQqYooILaBFhKRS1KUlOYlHk8JbVqjkfUWrRy\ndloyMgyYJRAreQfPWIAGKocAaze3KiXDhw0L6nZgbBCZBWiE083NvQxFlL35vU93+krT+Rxqref9\nZjDT4zK8ZetHdF325HXP2vgw40EoCz8J4yT2SuMS2Cgqgb3K0bmfVxTAvvXhz+GP762fv5dZ53+L\nNx39sfxnT19+BZ/68lexFNbVzAhmIA2JFNBUrFpq0t/XWjDAyrItA2y+eBu6/a/98i/hv/zF/wR/\n8O1v31n9zv+JMUgZqc6rEQDhBM6KoejQJry5LmAiA11lWLMwGBWF9R3JRrefQQBkCDInJGPebkM7\njTE60Df0pgqDbCInEUC0r8fazNIgAo+BTAPVYOGYsQQWgBTkpd3TgcPhiKWoqzSslcEYyisqopaM\nKmkCJc2CLDOFLYLRNySGNYBWbIVAa3NyUoWX+BaOPUvPPRmOryqN8QwA5/SqR4FO5eADmFGevFzt\nfe5jZ5cWxA8zHoSyiJbEfI2w6UsTPa7iMZbgP790vnk8v/3c1u9zycqJx46Wkg9KeVfrvMIsC77w\n9W/inZ/5eYyxgmlgu/keuN+gYEVvz0CjISdNbSoewCZUTrDSSYgMpJLRR8N68z5+7T//K/jX/4V/\nHu//4Pt33CsiwuHJCwpptjSmiHJY1HpEXh6h1AOO9QhOC5brJzg8fhFIGcSE5bAoIW/VuhWIIraJ\nFE2ZJ2ZhW7GuzyAkIE5YDgcM0muuZdFUaspIGZYeYYiVnaN1FAEKM1ga+nYC+gqSDZUHpN/GQBKp\nEj1OUFjRSlRtikTIrK+TkFgEwACOhyMGMVICrioZUlPTrtQ3sDTUpLEKjZMO/PF3//jOs2tWjOfl\nyLsOMaM2t5mxivk+jr0q0vkaFYM/x4SO+20/Nm4IsO9uRFyAf53v/Y2LCiW6NZfiDnPspUmjm7IX\n5/DovZgT37um+duiYkw5460vfAklZ1Tz33s7obdV6yxauzX9S0bKBZQTivXy7A7yO2CKNBX84Pvf\nw2/++q+jWYZknnNyTQ4B2KpbYWlOzQYIRIBk5eK1VtRlQc7lTHyrPY4ZKSfkkiFmiRAxRt+wthUp\nJ7VWasXaBakW1ONREaKjQyDKYwEBkWI5ZHTrQjbNbQIxIxlHaCKrHk1JXYshkN6xbpvWm4jCzGdh\nmfRNeTlJDJ8BCGVIFwCG0iRVKDUpT4YY0raLBjxJlJPjdHNzvtd+0s5Fwdd5ePmJvBQRITy3j+5H\nnAcROj4tkjg//Dz62HFw7lkP87u9937s3VjgLhlJtDjm5I4jWhsxku0n+p6v6jtOXQpI7Sk4ADhc\nP8ZnfubnkesC2U4Ypx+grRv66YRMhL4pL+a6NWPaEkAIQxgi6oYwWes+ThiieIz/8N/6N/Dv/uV/\nG3/3O985n6u3DTlVcFpAaUHOR/TB+MF6wjY2CAkEQ0lsjJE7J+16XhKj5oqSK66WBTlpDICtTSFE\nLDtR0EU/p5SwXC3IJUEYkERgU3SFoas6E5BgfgBBSEvla81YEpBKBaGDeVg7RLMsIGAMoG/g0bBt\nK2pdUGuBhjUF0tuZqCfRQBZ1JRZL4xYmZBhCczSMtgEgMFftkcIE5gzp1oTJTXhvPfoJ7S0LT747\nlcm0LuZ+/n+U0/m3z5BMGfKytxcv27NcftjxIJTFXgBxj90q/u33iTd6jhhQmu+nUokR5j33xbsn\nMZjpzx3fe9P0UnzF4/nntbz+I+/h5c9/Fa13rKeTwpWHUucfjguING04LEIPq1sgIktHaiCOx6YQ\nbmnYWsf/9mv/A37jf/ofz+fWln4AISk7FSfDUwienW4wZPJBaBFZzVmh1sRgVutBhgCklkdiwmHJ\n4ETKtG0uhnoXGbCakvk7q/XkSFbHkQAYKgvZ2g8mVlxJYuWpSJYqhYgRACfUnDX9Cg1KsvlEnFiD\ntJbZyCmBRJm42gBqTsgwBjDpIBGABzI1JDVWLD1tCqDL2fWZPU/2Frn5fl3XOy6GJ532qVPvmsRU\nfTwHgDvKyaNHp5LySmsqq4+iqOxBKIs5LmlS4L5Ced7+9wJ5wW3wr3N77/vtuRp7vmM8d7ze52VX\n4j4+T3/96DE++xPf1FLoNjCaxipKLWii5nEXQU6Evq1a9Sla7gQLNo7ZLwOE3htG37A9e4Zf/c9+\n8eyOtHU1yvykPTtoZgYaUipKzDuU3k6LvBQgxSlp5WVmlFJBlgLN1jtVfxzOx861ogmDKWv5eR9Q\nXgjjnrDFYQztqp6NsyIxmRUBwDqrEykOQxVImnYPOKUzt0Y2JSjWKElXeYWHq3IUO682fx5900pb\nacAQVCPRORwWLO2p7QAAIABJREFUJGMkA+tqrmS+SUmCLjzvOXFrrXeyIBMK7jE8cfHy8gbchXB7\nK2EqlGhlRAv7o4pXAA9IWey5GpFSH7h7g+fwOWYA93w6HyiN+IkYQ4iTeu7jJ7h3L2IQyh8zKpc9\n92MvLSYAHr/0CXzlH/vn8OiVN9H7hmVZwBAlyyna5Qujo1pTIEi3UnetEAV01R+GUxi9YfQV3/m/\n/g/88l/9Rfzg+99HW0+A9deo9QBBhghDBmHbFFswrMybKSFRQSGdiENUMYH6uRdI6xpTyXUB5QRO\nWV2JlJByhRADXCBsfBeJwZZhmIzixAwWAbr2Vb0+LMi5Ki1/VowJlwNSvcZy9dTQpbdp3yGCm3UF\nQ4uxpkvGGBASQLTdAKijt/eRcQLQNcsxOqpsGOsN0ljPjZxLZuPAGGgysK7rPfTltAy95TBJcabM\nTJfDuwRTtvfkaC8u4Vnso4z5ffewSh92PAhlETWfn8DxJvjtY4DQf++tAe/SRAslpk49H2JMd+7B\ndn12IyqbeJ17v3WuQr4mYP5/490fwef+gb+AfLyCmub5FoLMiijcRKx9n1LDAbY/YO5CxrIc7Xdp\nBOK//+v/BX7nt/822nqyc5nrMgWS1dzX5uViFaAD3ch2x1Cqfr0f0IrORIo8TYzeOmSQXUNS64MA\nogSBuix9DLTp0s1rJUIm7U9ackFmAXHWCkxocJJZsRCcVBmVw1EViKhF1Yf+FhBj64QBrTgFBpjk\nDCufFod2XevIrCAzkLouZJmUlDU9TfoD1ApjPgc4Y19SL3N7i4xXMD5jMeXLy+1eDM8rFf/fB02j\n4pr7fdjx4JSFvwF+7MUVIi7i0grvA4sxwHkpljC/99d3KQbh3++Zk3uuT7Qw9iDph+MV3v7Cl/H6\ne19TcxNqQisPBKFZFeS6als/QLC2jk0UCAV1LiAAWtfJuvWOP/7W/4P/9q/9Ek7v/0AzKWaSU8pA\nyhBRE15Ema8xBtbe0GxlzVkn3xDBOkSzKMQaBMwZtSxYSrH6El2Va8mYiobsXyn5DK8evaH3DUOs\nheIYyuFhXBelKvBrQIOlXSGV4FwxuACcgVlDYi5QSRpz4KR1srByeRkDJDjT61UWtLEaq3gHkaJO\nt67M3rMJEwBsveNwvLpnicY0v09T+yB7tHhnTMHLsz9mlJUYF4kp2hiD8zVUH3Y8CGURb8p87//H\noE+cWH51j+bYnnKImj26MjHQ5Pe/ZAJeCqbG64wBq6jA/O+5fvIUX/vz/wQoa6l6LRUEYCm64nYR\nLEWDiodDRS4FBCBzxxgrWm84rQ1EAvQNRRrWbcNv/nf/Db71f/82AE17aiwgodQFACHlfCatGQKs\no+PUVnRbqTsRUlHk5ERIjr6hbatZEQqxzimDoYHSUgs4Wal4KmhWVZoTgUmZwIkFqTJKZaO1K0rs\ni6RKkpQqr5SsPKXM4Kpd4FtXlCWYQVmVay0ZrCYOVPUlJFbQFqeCZTmiLgsKJ1QWlFy1xyllpJwU\nDUqEhAaIBk0fv/AUdVnuWcDzPXAbS/DvvQLx770C8XLpsx3RUtmTJW9Z+PhbjP990PEglMVevGIv\nABkn2PMUxxyXUJnRtPMpsKiootUS3ZOosKIF8zw3a+96o4t0OF7h8etvAxDkBKXZyxklE3Kt1pgj\noRkoqo2Btal7wZxQMqFBmwIRMzA6SiLr1MUWl9DAKbHyZm7NwEus/UJhwVNAYxNsFaXSNs3WkFZp\npsTqrkhXol1oloWJdZUmtXeyuR2AnLkrMmsKU/kqtLKWIFiqwrtTLorhgICz1oewEc5QycrmObrG\nTXLWrEeXc62JPh/9ndkg6oAgpQKhBLBaRsLKYZHZArBTBiDI2baR214gPmDpA+WttXvEu3N7Lz9e\ngfiJP2Un9kCNlq53w/dibx+FogAeiLKYw5tVMTbgt9lTLsC+a7BXt+G3Be7DuPdiIj/Mb/Bafe6/\n51rt/Qb/9/namfH1f/yfwhtf/klNUybCccl4cn3Ak6sF19dXWhPBhKtFg3ogQhNSCLMM7TFKpBDv\ntACGpmyjG7mOcUratabE6gIwIdeiOQej5h9dKfIxjKRG1LUQAd5fO4QYwkoYw6zWRSlaicpcNBgi\nw4rGBkoScFK3BElX/T4EpRbUqiXvbExYkAEhrTAFoCXppKlQrhWSEjhr3IlzxhCDtQ8yEhstyU+y\n4VgARgMgONQFpSxIh2twOYDrQUFkrWPrA9vphNE2CBFefvV1EN2lwpvAJ7+wFcukRECU74Q+v/f4\nnCgHETvk5WwOb8nM7/es4w8zHoyy2FvF4w2JftqeIpmvEbDi990b3vyL5/P7zeuM8Y6ojKLlsYfs\nBO5bPnsCIiJYrq7w1ntfxfH6MSZr9XEpuF6UQzIRAQI8W9XnvzoeUDJrGpGSFn8ZS/iQgVyK9YhV\ngSZo6pOJtXhNrFiMWZUBkaEa7ffLbbBxxiCIGMdaNRDI2us0J8Y2BkAD4AlvNl4KAjInZa4amsZM\nzKBUUOty5tncmjVcFVH3iGAAMYaMbqngjnVdMaDpXg2aqjslY/JZWv0Fw1KuoqQ8Fs/ISS2V5bDg\ncLyCDFFMR980mwTB05c/gavrq/PzmhkPHxvwK350TaM77a3Qvf6mXramUtlzmfd4Kz7IYve88WCU\nBbDvWvjvLimAuGrv1ZTMz6PfB+De5PfnjJM6XkO0cuJ1RSGJwwuDv+4oYMyMF197A0/ffudsomt3\n8KRCzgmlViNuYaxb0+Y65j50JACMmpTajhnIuWq2w3APBEYx+j4ZgnVboZ3QSfkiiro/szdq7w2K\nvFATnTmBuFhWQpsTtd6gPZIJpSQIOk6taVCUk/7HLZ2cQJnGYYpEwV5KK9jPHBdGADya1oaYpaIu\n0kApGcPwHNpL1uSHgCEasFyqZjpKPRg+gzEgyKWi1kUJgUgDxWwLxRCCUEI9HO9M1OnGRtmdC8xM\nmV6yIL0cxEXGIzK97PnhFVW0zD9KhfEgaPWA+2YVsI/ejAHMve3i6n7JsvAPIR5jjtih3a8Mlyyf\nS3GSPYUxH7RfZWKwaiq55eoaX/jmn8Hv/+1fB/WBq0PB1rV+Y2vA++kGGAPESkpbuKN3Um5qBgCC\niGIanj59BIalKxNritFqIspyMFyA1mZ0AEKqFmZKdtuUKn8bZp1QQuKCrW2GxeggnqXehN4Inbv2\n3mAtX5/M3YkJtQIdYjycAxAGsjYPGtb/NGd7TqIgNIGgtxWDNDYCUyJb7xgDgGy6+nfR9om5mAWh\nRDtEgrIskOWobROhYDdiQeaCvmml7AAwWBm+PvflL+Px48d3nrV3dX3cwctNXMwmZsK3AdhzQyKO\nwy9sewufx3XsyeCHGQ9CWezFKPwkmebeXnGOf38p6usjz3MfvwJcUkB7lsK8xvjw92Ihe8fzD/GS\npTPPsZcOu3r8FMwJLIJUCgYaehcstaJ1wU0dGKcbXRl71y5dIshWpt3BEGbjqtTRekNJ6pawQDuV\nQTCkqOnPGczauWvr2r5Pu6UJUmYkztqnAwNLLdhGQ2ZjCycFSmVixTuwmv9DCNtQZ2CQNhGaDZEh\nsOMSNPCquIcxABldC9OG1mcMSw8r9HziNlThiGZdLR6QtZiMMkBakg5OFvhUi4NzRskHsKWOZyq3\niULtOSdcPXnh/GymWzlla11X1FrvyegY4wzimtaCiBIGT5xGrBL1i5dXFHFBjHMFuHVXniePH2Q8\nCDfkeZbAjB7vRXmfdxxgf6UHLrsSe5aN/x8b3j7v/Z7Z6dvceQHw1xSVSrRSUikoy9W5ZiFZOz0i\nhSkfl4pc6rn1HxFDYIFNUZaqwsBhqdC1WSfaGKKEtZzsWjfAYhUl64o9K1EHoJR2dYECregMsBI2\nTgxScJVWpc6V3xCWxuGgFo1lYQjWlUxAFrtY24a1WdoXAyINJRPIsj+9q6tCrJWzNJmtKKHWxXqY\nzHsgVgIP1Fq0wpSVu7PUBSkv6J1AQzu8t75BhsHSUzFFl/DqG2+g1HqvLJ2IzvDu6AZPReFlzAMA\n5/O+BCyMysPL516MwhcyflRWBfBAlMXeJI3muw8EXVIYceWPD8H7knuWRPQ193xIf455TV4BxAfs\n30cm5wjJja5NFJRpen72Gz+Lw3KFmhg1E5ZacKiM40J4dCxKYb8sWGoGJ9bXXK2nKOFYE66OWima\nUzKqO7I1Wqs4tc+xrqxra+DMECakklFyxTaA0bU/attWHGpSM75v1iKgY5AWk3FiSE5oALQ9QMHJ\n2hPO39k7Y0gGcYZwgjCDEgDpprQEzFCXYBvoWzechijNHmk3sgn52toAQYOvOSv5MaeMslQosjUh\npYouGjOph2s8evQCmJXLVP+bZXvGbQhef/OTZ0vBZz+ijPhn5xcJjxD2w2dHYowiLihe3iNSM7op\nHztlAewjKONN37tp0TK4dBw//IruJ+meZTK3j0phUqDNhxPPv3dNwD6k15/XWy8xTjK3eeWtd5CW\na6SUlEk7JRyWikPVJj81K7aiC50DmOgNbQz0AaRi3qfw2YTvAxBRglqtsYD120jIuZ4zEzAUJaAk\nvEwdjIbT1iAylYVAcRkKxhqkfUTJApAkAuW6UV7OdbVCOJuYQspvsW0KILs5rdqpDFroNkDa+pAT\nWhecTitmX6EhyodRklozWmZGBufWmAwlux4a2h+kKqepQLMsTOq/tAFsg6xpEaEcj+dn5WNZMXYR\ng5HT4tizaOcCEq3WaIHObSKOZ89d8XGLjwLmPceDUBbzAXj/fG/i+Unvb5S3CPxDuxRLuKSQ/Kvf\n1k/evf39w9sLgPrt/W+65FdGq8qvXGMMvPDq63jvz/6CIguZwaRFV7kuOB4OuDpW6xim6Ezt/GWt\nB6Xh6mpBWSoE/Vw9SQSIthCDICGXA4RZMQbbCX0AXYpWoW4r0De8/73voZ1uIH21FoEw/5+QqEDA\n2AZw6gPr6BASMKnLUXLGtmmAcykFCg0ZkL4Bo6NtTZVKriiloA0CcdHaj94VCGa3LTEg0tBHR87W\n7zQV5c/UO6xtCnJBXSoSsxL91ophz/VmXQEZZ2uoj4FSK1JiRYEmxpe+8dNanGYyGtm8ozKYE9dv\ntxcfi02Rvdz5xWQeb29x2nOVP7aWRVy596yKuFrHz2MQ6FLw0J9zzxf0w+e0o4Kaf0cfdY5L7sse\nnmMeZ0+J3curp4SXXn9TQUxQ33/6/G2m0VICBAZwBkQGTlu3rmaLCafiKER3vaW7B0AkGkQEsDbN\nriSr61ZmzoFSKko9IucKkWFsUqLcGxaATJzURaCEJjijQ9uwOAUROhRhmQhnvEPOGgwl0j4inKqm\nY4dAumjTY1hgc/JeZMdEBbGAp1hgdqCPoazccxICqLVqbAMD3VK9p9MNRm8W52AIMfJyhTc//Znz\nRI3Bdu/2ztcI5441IP4Ze4US3Qpgn2Jvz7qI1P9x4f0w40EpC+CutvTEMMBdEx64/8Dmcf4kroj/\nPm6/B8CKx/cPP/6G6MP6Y3ntHwUgrgR7SnH+r4cjluP17QpmUX8mQs0Jh5otfClY+7BUqAYYrw4L\nZuFZVwYdLTu3CdxHQ29NIdBQCLiIwaaJzszbKpgwtwSADO1K3jd1Y4BzL5A+VPn0YS0b+8AmA4OA\nYYjTwQSyTvHSlTpv8oz2cUtoy8xaL0JkCknJdvsQkEG9m5HoEBGIlVtTWxEqaEz/qykkohkRGRsS\na2VqYoLRakDAOD59ik+88uqdZ+if3ZRXryAifDtu72UgNhCa2/oeqREI6I8bXWu/wH1U1sWDURbR\n7RC5xd774SdpvGHz7718ddTQ85xxu0uxhD38frRk5jF9sCpaRP643iqZx/aC4a8pgn6IGZ/66k8j\nlQNERH1yVjM/JUVuPjpWpKxENVyUE+LJk2vkpH1Bhqhyka7ZCRBh6sXWmzY2hoCkIWEzcBWj5EXT\npaZkSAQsQDUffvQO6SedMNB+pQLCkIQxVAGc+oaRB0ZqGNQwmJTrkgjr1jCMKjCljEHKptVEkaOt\nN6RE4KzBS2JCLkW5MyBoo4GT3WNAyX+GQAaQKSOnCqGELoy2ndC2E3prGjCdzF+5oFkD9zEGXn7j\nTRyvru6kTONiE+UkWrZ7xWBxMscFxyM254gxCy9H/ryXgIAfdDwIZbG3Cs/XqBU9Ic58jav0pVV9\nfubfR+URrZE9gdi7rvh79hCh/pr2Umner/WKKFog8/NPfPpdPH7lNUUfiq0oUEcBplCmxVGyNv25\nOlYN4IkJpAGZxIqw8mzCTFNAlVuzlIytNYV+iyCzsmfPQisA2rdkKMIS07RO2SDhbMhRrc1QWr4E\nQQaTslxpXEWRpmQAsImanN3LBcPcJRgJj678zfq/CgicMrahNTEC7cROIIymsRCMDukDTGzWk7KA\nEc172K2QTtCanvcTr71+p9Tbu6Dz79jRfD7XuDicrcGd7Mbcbq/F5p5VEmU77vNRuSDAA1EWXkPO\nv+eIn3utCtyNU8Qbc0mD+/cxVx7P65VD1NjxvFGBeKtgnsubjfP6437ThL1kWc3PHr/4El761OfA\nxtLNzEaRNwCC9hYpBYfjAY+vKg6HgqurKysTx5m/U011dQUgwLbdgBk4bZsyaJOyPiVizYGIoHfr\nxzEUns3MIOnWL5WNEEdh4wNaPj5xC0SsLQFRUFM+fwYR9DaQKKl7xJpKJS4QrtZ2kACIuh2iZeZi\n5DSJDQYOJf9l1jhL7wqqAmfUuihsHGrBiIhynTbrVj4GtqHHJ1JawjYEy/EIuGc3XSJvhXpOksh9\nEjMT8W8v1/4Z+zhGdEv35CcucM9zw3/Y8SAQnMB9P2vvNW6/N+I+e26AN9di3CEqGL+/D17tPQy/\nbwRczfcezOOP5a9jClFMz8WYTS4Vn/2Jb+D7f/T/4Vu//RsYon1DBjpKJTzhoxZo9Q4wQCmjloSy\n1HM/VYIes48OIkYTI9KRgWp0WQJCTRrnIGoYSbMDqWtjHWLCzakhl2oWQkGXjoSulapCKMaOPcZA\nwkCHIA9Bk1v/nynrZO3WXdzK5kcf6hYggdAsc6IuSm9NDRsikACtd4CtEtZqVDhVbeacGTkTRgPW\nrSPzgAygt47Wm6Z2wWp1dTv3aEgkeO3NN8/PwC8W2pC63FlQ/LOaIzKwzecfYxvz+3ieqBDiOfz3\nflH72KVOvaKYf8fv92IUfsJGH85XeMbjeb9uzwWJx45KJz6MqLVjfME/0Mm/GM3UaILGDEi8vnkM\nLhWvvvMjYE63Zv5cAZNOjlwSjscDro4Ho83TCU4YaL2DOZ19cz0PnScaAC1IcwClxAxtSaYxhHVT\nQuHRm5LdcEZOBaMrqXA2JTFEkK36c557iGhBGjG6FYMJBtpoIALASrKz9g2AgJJaUcSM0YeFcAXS\nO/oQ7V1CCYNmz1CdpA1aNfv+ugKsrOLD3W89P1RRzGctiuWACK6vH53vu7cgPDjLP69oNUxLMcqM\nlzHvmjwv7emVipeluLBGef6w40FZFsC+GRXNrj3TPKYd5zHjTfJuRBxRuURrISoZH/3217EHGttD\nbEaLJubY4zZxxdEMR8Yb734Bf4s0tpCL0tuXLADXczMgIYZ6GprTzwzjrGBrzKPAo7Zp1/EOrflQ\n0t8OyQCkI5HWSjARWt+wNntGhVFLtfYEMB79jMSEbdNWhV00Q9NHsyyM1pskzhAZoN5BBAzWidtG\nB6WMdV2RUwIsNTusGA6UIFYAkoykR8vcNT1LMGwJa+Ykp9vU7xDl0FzbAIRAZMFZA6ZhyhwROiU8\nevz4npxNWZhs29MSnM9yjy1rfu5TpV6+vAzGeMcexsLPh0uux7SkP+x4EJZFHP7HRnNu7/28QTHF\nGUcMFMbjzb/96hAnPrBvDexNbq/o/HH34h6XtvXb+Gvw+6VSUI9XIIJ2Ec8JEDKqfTXXl5yQJsU+\nAZo2HRBRLEU2MzkxnVmkzgzeAEQ2CzJqAJFTAkgtllQKSlZ+T8hQGPicGJzUDSgVZVLoJ+0TsraO\nrSlXBBnOQ6Hnar2MPs6FY+vphNGUqr937YNCpMzbORO2tukKn7SvqlpPOilhZekT39GHVYSK6rRm\nsQptT6Bl8UM0VpEIuH76wj1F7yegiJyrR30dUwxiz/cRbzGfq5epPUURZWVPUcTFbc9N+aDjQSiL\n6Pf7/zECHc3waL7vbTeHz2VHF2S+er8zPoC4ffQ991wlvwLMEV2gCAGPSuV5v3EK3qNX3wRzVkas\n3pTr4tyYJyGngmS9NowZBpiCC20XoIv1LTGNWgADxSojRUQtFOPlPCwax5gdyyEKtU45mZsjmkGx\nIKoAxsplnddFAVNEVsBG6g6MPjDWDdIbetNKWAxARIOws5WiDGXnbltDZsGhKkCrSTe3BRqsHOZf\nkIHFoDGJ00kbGOeczf2YsQPN8pSUcGqCF1997Z7MRbmcwz/v+ewusVjFFf9S/MwrmEsyFK/Dj48q\nbvFg3BDgvga+FDeIE/jSpI8PKE7K+fmeS7B3/L3r3Hvo/hg+iu3NyHje6MvG3zn332NEIiK8+/U/\njd/67h/gB3/4d3F9PAACbZBDCW0ojX6iArFJS0aJX1gn/6mtIGY0sfQrQTt6WcFYTh2w6lQQK48E\na8Wp5ie0w9hp68jQ9oZk116sCEtLvrXZTltvIMbdmazfx+z/UZIFBNPsmTosiDjQxgbCwLrdoHDB\nEMFxKThtADghsWirRZBW2VYtpydWtrCUGNtpVcuENeU6lYP0gd4HUlawl0BQjwt+7E/99J1n612O\nKFOxHaFvFeCfrbcsAY2D5Jx3wV4RNxRlcG7rrZq4mHxsLAvgvvDv/Tg/wfxK7kdUGHP4m7YXm4j7\nA/dNQX9sf9zoJvj/0Uz0QrIXHPXXEfeJ98af9/GLL2N58TXkkq1ZsMYwlDh3gBlGkKu9L1RpiQU2\nu5Z603TXLXA4btPLszNZYlLiXeO/GL1phWnvaK0jG+HN6JphYIIqiaGuw2irpVih5zc8BzlMBzFr\nzUZr2NbtjC7VWpdiaFCL2STl2OhIaKK1KL0LSKxjWdceJ+8/e4beB27ev8HWBjglaxykCM+2nowZ\nXAOcyvLV8fjlT+DlV165Y+lO2ZhWibcivKx4AFcMRk5k5vwupmEvudt+YYoLSoyBeAvnoxgPQllE\nXwu4D16a2/nt4zZRyUT//5K2jUrAK6O4kvvJG10G/xoReFPQIslrTJ0+75r2/Oa5T1kOePHNz6DD\nMAC5ADByWp5M2jhDtm/dEPXj9f2smRALVWiv0r51aIZEU5MlMWQ0lFygBkE6H7O3cQ4OMozDk6DX\nAoLiODYrPFNqvt7W24rTickAQCKomW1CM07rhm1d1SIxRvCtdeMGVdeIoS0GMQaGQFOr1jFtNG3b\nWJJeh8h8hhb4JICg2R22grJPfuazOF5fn7NYMU4Rmbr9s43PccqTX/AihDvKVpTpKCOXXOS4uH1s\nApx7k9x/Fx9E/NzfqGadoqIVEt2BPTMuHmsGOKM7FAOpe9aE328qAo+x2HOp4sP3JuqeKeuVITHj\nrS9+BZ/4zI+i1oplqYrGnEpvCLKlSxmCRKSQaeO5HNCaCIYo0Em0sEt6B3pDhhabZRYQGEs5oA+x\ndGgGieIcmPQ1ISEJsD57H9I7GAWHXDG6lqbrT7OYSlmUxbN3EDpA1ueUM8ZQ94CsTyqB9f3k5CQN\nRi45nStRmYCcE3oX1FLByNBsh55zbdrOMSkpJxjmTo6hfKTE6G3gxTffxle+8VNn8poYd5r//bPx\nVoKXp/k6q1bjArFnKfsFYk9e99wZf30f9XgQyiJaCncmQbiZfp+94/g+knHiR3fBH8c/lPiAYrrM\nP1ifxozHjb/BT3afe/eri+fJ2LtWf23Rl63HI154420suQLW61SMDIahcGwIga0atDUllQH5owwD\naBlga3QMELYuNoEThnSlwRsDJWX0QUi5aj9RiyXKaEa7pxZOIpwV+dmkz6qUMJTTIhllf2JluSFO\nGEjgspiLZJMR2oqgW2ZEZKCT9lAZYGxdWzom1vjMzc2NolrHUDZvvuX5EBH9fNa7Q69nyMDnvvQl\nXD96fJ783iqIVute7Cq6kJfkYabVo+vhwVqX5P381IL8eRmLLvkHHQ9CWQB3teXe6umr7+LEizfq\nUl0GsG/eA/cJdfxn3kKID7YZRdx86NHlmfv5mhYvNHsP2QuRX7niahGVLBHjrfd+DAMFKVWtyZAB\noGksgxWG3YUgoiS4bUxQVFJWb9FycSsqMddAy+AxoLUSELVEEiNzQiLSQqyuzsYYHSACsZwDqls7\nmTukfJciglwW5LJggJFLBSW1HkZX3IO0E8ZoKJppVXfGwFPMhMKi3JqUsXXt7zGgaFFAWyMyOlAI\ng4ZyfQ4NXJaqqMshA9tQYiCwEvRuotmhL3zlq+feH96S8FmO6BZ4l9PLzyWZnc/PK/64355Mebn0\ni6k/vreMPzZuCHB39Z9//0n8QECp1mO8IR5nD8wVzxNdiWgWxp6Re/5kPJd3g+bf0Zrwx4rU7/41\n+rTz1QtCqQvK1RXACUICYq3N6GNY60Gl0oMMw2FY5oMAyG30fuZPm1kUrW3K7TC6TuCsQchtDO2R\nSjqB50QWA24lu94+Zrf1AfRuxWiKFKWULb04kMttsZlA6ftP60kthZyMw0JjEWMIYM2aiUj5OaGc\nFOvWwEk5NAhKCAzSAKqcAVhGFzBEtxmwpssZvFzhcDzekYM968Erdi8L3sWYi918hl75+/39vl4+\nL8Up9qxPH2Pzi9VH4ZY8GGURR9SUwH3cwRzTnJ/b+Enl3QQ/aaMJ6SHa8TPg/qSMiiVejxcIb01M\nC2muWF5YoiDswdL9+XddHCIcHj9VYBZpoJMsBqD5BkBdjVnSrMFMGVroNXo3SHcC+rC0p11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ULDRVm8tNOZXve2nYyPw4KLuWrndhEQATI6ai3IWWn1NMcqWv7OivwcQ6HjAkWwatCWMHOmM439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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWTisYLQM1aM",
        "colab_type": "code",
        "outputId": "cf4ed570-72c1-49e5-dca1-a438b3070a6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras_preprocessing\n",
        "import keras\n",
        "from keras_preprocessing import image\n",
        "from keras_preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Flatten, Dense\n",
        "from keras.models import Model\n",
        "\n",
        "TRAINING_DIR = \"/tmp/rps/\"\n",
        "training_datagen = ImageDataGenerator(\n",
        "      rescale = 1./255,\n",
        "\t  rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "\n",
        "VALIDATION_DIR = \"/tmp/rps-test-set/\"\n",
        "validation_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "train_generator = training_datagen.flow_from_directory(\n",
        "\tTRAINING_DIR,\n",
        "\ttarget_size=(150,150),\n",
        "\tclass_mode='categorical',batch_size=100\n",
        ")\n",
        "\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "\tVALIDATION_DIR,\n",
        "\ttarget_size=(150,150),\n",
        "\tclass_mode='categorical',batch_size=32\n",
        ")\n",
        "\n",
        "pre_trained_model=keras.applications.inception_v3.InceptionV3(include_top=False, weights='imagenet', input_shape=(150,150,3))\n",
        "\n",
        "for layer in pre_trained_model.layers:\n",
        "  layer.trainable=False\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2520 images belonging to 3 classes.\n",
            "Found 372 images belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUSXJG7loaVL",
        "colab_type": "code",
        "outputId": "9364db14-d3b5-42df-b26e-3e5a310ea116",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 11172
        }
      },
      "source": [
        "pre_trained_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_6 (InputLayer)            (None, 150, 150, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_447 (Conv2D)             (None, 74, 74, 32)   864         input_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_447 (BatchN (None, 74, 74, 32)   96          conv2d_447[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_445 (Activation)     (None, 74, 74, 32)   0           batch_normalization_447[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_448 (Conv2D)             (None, 72, 72, 32)   9216        activation_445[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_448 (BatchN (None, 72, 72, 32)   96          conv2d_448[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_446 (Activation)     (None, 72, 72, 32)   0           batch_normalization_448[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_449 (Conv2D)             (None, 72, 72, 64)   18432       activation_446[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_449 (BatchN (None, 72, 72, 64)   192         conv2d_449[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_447 (Activation)     (None, 72, 72, 64)   0           batch_normalization_449[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_21 (MaxPooling2D) (None, 35, 35, 64)   0           activation_447[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_450 (Conv2D)             (None, 35, 35, 80)   5120        max_pooling2d_21[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_450 (BatchN (None, 35, 35, 80)   240         conv2d_450[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_448 (Activation)     (None, 35, 35, 80)   0           batch_normalization_450[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_451 (Conv2D)             (None, 33, 33, 192)  138240      activation_448[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_451 (BatchN (None, 33, 33, 192)  576         conv2d_451[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_449 (Activation)     (None, 33, 33, 192)  0           batch_normalization_451[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_22 (MaxPooling2D) (None, 16, 16, 192)  0           activation_449[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_455 (Conv2D)             (None, 16, 16, 64)   12288       max_pooling2d_22[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_455 (BatchN (None, 16, 16, 64)   192         conv2d_455[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_453 (Activation)     (None, 16, 16, 64)   0           batch_normalization_455[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_453 (Conv2D)             (None, 16, 16, 48)   9216        max_pooling2d_22[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_456 (Conv2D)             (None, 16, 16, 96)   55296       activation_453[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_453 (BatchN (None, 16, 16, 48)   144         conv2d_453[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_456 (BatchN (None, 16, 16, 96)   288         conv2d_456[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_451 (Activation)     (None, 16, 16, 48)   0           batch_normalization_453[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_454 (Activation)     (None, 16, 16, 96)   0           batch_normalization_456[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_43 (AveragePo (None, 16, 16, 192)  0           max_pooling2d_22[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_452 (Conv2D)             (None, 16, 16, 64)   12288       max_pooling2d_22[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_454 (Conv2D)             (None, 16, 16, 64)   76800       activation_451[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_457 (Conv2D)             (None, 16, 16, 96)   82944       activation_454[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_458 (Conv2D)             (None, 16, 16, 32)   6144        average_pooling2d_43[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_452 (BatchN (None, 16, 16, 64)   192         conv2d_452[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_454 (BatchN (None, 16, 16, 64)   192         conv2d_454[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_457 (BatchN (None, 16, 16, 96)   288         conv2d_457[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_458 (BatchN (None, 16, 16, 32)   96          conv2d_458[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_450 (Activation)     (None, 16, 16, 64)   0           batch_normalization_452[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_452 (Activation)     (None, 16, 16, 64)   0           batch_normalization_454[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_455 (Activation)     (None, 16, 16, 96)   0           batch_normalization_457[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_456 (Activation)     (None, 16, 16, 32)   0           batch_normalization_458[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, 16, 16, 256)  0           activation_450[0][0]             \n",
            "                                                                 activation_452[0][0]             \n",
            "                                                                 activation_455[0][0]             \n",
            "                                                                 activation_456[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_462 (Conv2D)             (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_462 (BatchN (None, 16, 16, 64)   192         conv2d_462[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_460 (Activation)     (None, 16, 16, 64)   0           batch_normalization_462[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_460 (Conv2D)             (None, 16, 16, 48)   12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_463 (Conv2D)             (None, 16, 16, 96)   55296       activation_460[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_460 (BatchN (None, 16, 16, 48)   144         conv2d_460[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_463 (BatchN (None, 16, 16, 96)   288         conv2d_463[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_458 (Activation)     (None, 16, 16, 48)   0           batch_normalization_460[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_461 (Activation)     (None, 16, 16, 96)   0           batch_normalization_463[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_44 (AveragePo (None, 16, 16, 256)  0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_459 (Conv2D)             (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_461 (Conv2D)             (None, 16, 16, 64)   76800       activation_458[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_464 (Conv2D)             (None, 16, 16, 96)   82944       activation_461[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_465 (Conv2D)             (None, 16, 16, 64)   16384       average_pooling2d_44[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_459 (BatchN (None, 16, 16, 64)   192         conv2d_459[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_461 (BatchN (None, 16, 16, 64)   192         conv2d_461[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_464 (BatchN (None, 16, 16, 96)   288         conv2d_464[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_465 (BatchN (None, 16, 16, 64)   192         conv2d_465[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_457 (Activation)     (None, 16, 16, 64)   0           batch_normalization_459[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_459 (Activation)     (None, 16, 16, 64)   0           batch_normalization_461[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_462 (Activation)     (None, 16, 16, 96)   0           batch_normalization_464[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_463 (Activation)     (None, 16, 16, 64)   0           batch_normalization_465[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, 16, 16, 288)  0           activation_457[0][0]             \n",
            "                                                                 activation_459[0][0]             \n",
            "                                                                 activation_462[0][0]             \n",
            "                                                                 activation_463[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_469 (Conv2D)             (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_469 (BatchN (None, 16, 16, 64)   192         conv2d_469[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_467 (Activation)     (None, 16, 16, 64)   0           batch_normalization_469[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_467 (Conv2D)             (None, 16, 16, 48)   13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_470 (Conv2D)             (None, 16, 16, 96)   55296       activation_467[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_467 (BatchN (None, 16, 16, 48)   144         conv2d_467[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_470 (BatchN (None, 16, 16, 96)   288         conv2d_470[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_465 (Activation)     (None, 16, 16, 48)   0           batch_normalization_467[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_468 (Activation)     (None, 16, 16, 96)   0           batch_normalization_470[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_45 (AveragePo (None, 16, 16, 288)  0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_466 (Conv2D)             (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_468 (Conv2D)             (None, 16, 16, 64)   76800       activation_465[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_471 (Conv2D)             (None, 16, 16, 96)   82944       activation_468[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_472 (Conv2D)             (None, 16, 16, 64)   18432       average_pooling2d_45[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_466 (BatchN (None, 16, 16, 64)   192         conv2d_466[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_468 (BatchN (None, 16, 16, 64)   192         conv2d_468[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_471 (BatchN (None, 16, 16, 96)   288         conv2d_471[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_472 (BatchN (None, 16, 16, 64)   192         conv2d_472[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_464 (Activation)     (None, 16, 16, 64)   0           batch_normalization_466[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_466 (Activation)     (None, 16, 16, 64)   0           batch_normalization_468[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_469 (Activation)     (None, 16, 16, 96)   0           batch_normalization_471[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_470 (Activation)     (None, 16, 16, 64)   0           batch_normalization_472[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, 16, 16, 288)  0           activation_464[0][0]             \n",
            "                                                                 activation_466[0][0]             \n",
            "                                                                 activation_469[0][0]             \n",
            "                                                                 activation_470[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_474 (Conv2D)             (None, 16, 16, 64)   18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_474 (BatchN (None, 16, 16, 64)   192         conv2d_474[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_472 (Activation)     (None, 16, 16, 64)   0           batch_normalization_474[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_475 (Conv2D)             (None, 16, 16, 96)   55296       activation_472[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_475 (BatchN (None, 16, 16, 96)   288         conv2d_475[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_473 (Activation)     (None, 16, 16, 96)   0           batch_normalization_475[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_473 (Conv2D)             (None, 7, 7, 384)    995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_476 (Conv2D)             (None, 7, 7, 96)     82944       activation_473[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_473 (BatchN (None, 7, 7, 384)    1152        conv2d_473[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_476 (BatchN (None, 7, 7, 96)     288         conv2d_476[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_471 (Activation)     (None, 7, 7, 384)    0           batch_normalization_473[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_474 (Activation)     (None, 7, 7, 96)     0           batch_normalization_476[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_23 (MaxPooling2D) (None, 7, 7, 288)    0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, 7, 7, 768)    0           activation_471[0][0]             \n",
            "                                                                 activation_474[0][0]             \n",
            "                                                                 max_pooling2d_23[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_481 (Conv2D)             (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_481 (BatchN (None, 7, 7, 128)    384         conv2d_481[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_479 (Activation)     (None, 7, 7, 128)    0           batch_normalization_481[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_482 (Conv2D)             (None, 7, 7, 128)    114688      activation_479[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_482 (BatchN (None, 7, 7, 128)    384         conv2d_482[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_480 (Activation)     (None, 7, 7, 128)    0           batch_normalization_482[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_478 (Conv2D)             (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_483 (Conv2D)             (None, 7, 7, 128)    114688      activation_480[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_478 (BatchN (None, 7, 7, 128)    384         conv2d_478[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_483 (BatchN (None, 7, 7, 128)    384         conv2d_483[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_476 (Activation)     (None, 7, 7, 128)    0           batch_normalization_478[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_481 (Activation)     (None, 7, 7, 128)    0           batch_normalization_483[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_479 (Conv2D)             (None, 7, 7, 128)    114688      activation_476[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_484 (Conv2D)             (None, 7, 7, 128)    114688      activation_481[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_479 (BatchN (None, 7, 7, 128)    384         conv2d_479[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_484 (BatchN (None, 7, 7, 128)    384         conv2d_484[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_477 (Activation)     (None, 7, 7, 128)    0           batch_normalization_479[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_482 (Activation)     (None, 7, 7, 128)    0           batch_normalization_484[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_46 (AveragePo (None, 7, 7, 768)    0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_477 (Conv2D)             (None, 7, 7, 192)    147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_480 (Conv2D)             (None, 7, 7, 192)    172032      activation_477[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_485 (Conv2D)             (None, 7, 7, 192)    172032      activation_482[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_486 (Conv2D)             (None, 7, 7, 192)    147456      average_pooling2d_46[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_477 (BatchN (None, 7, 7, 192)    576         conv2d_477[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_480 (BatchN (None, 7, 7, 192)    576         conv2d_480[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_485 (BatchN (None, 7, 7, 192)    576         conv2d_485[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_486 (BatchN (None, 7, 7, 192)    576         conv2d_486[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_475 (Activation)     (None, 7, 7, 192)    0           batch_normalization_477[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_478 (Activation)     (None, 7, 7, 192)    0           batch_normalization_480[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_483 (Activation)     (None, 7, 7, 192)    0           batch_normalization_485[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_484 (Activation)     (None, 7, 7, 192)    0           batch_normalization_486[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, 7, 7, 768)    0           activation_475[0][0]             \n",
            "                                                                 activation_478[0][0]             \n",
            "                                                                 activation_483[0][0]             \n",
            "                                                                 activation_484[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_491 (Conv2D)             (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_491 (BatchN (None, 7, 7, 160)    480         conv2d_491[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_489 (Activation)     (None, 7, 7, 160)    0           batch_normalization_491[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_492 (Conv2D)             (None, 7, 7, 160)    179200      activation_489[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_492 (BatchN (None, 7, 7, 160)    480         conv2d_492[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_490 (Activation)     (None, 7, 7, 160)    0           batch_normalization_492[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_488 (Conv2D)             (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_493 (Conv2D)             (None, 7, 7, 160)    179200      activation_490[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_488 (BatchN (None, 7, 7, 160)    480         conv2d_488[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_493 (BatchN (None, 7, 7, 160)    480         conv2d_493[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_486 (Activation)     (None, 7, 7, 160)    0           batch_normalization_488[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_491 (Activation)     (None, 7, 7, 160)    0           batch_normalization_493[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_489 (Conv2D)             (None, 7, 7, 160)    179200      activation_486[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_494 (Conv2D)             (None, 7, 7, 160)    179200      activation_491[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_489 (BatchN (None, 7, 7, 160)    480         conv2d_489[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_494 (BatchN (None, 7, 7, 160)    480         conv2d_494[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_487 (Activation)     (None, 7, 7, 160)    0           batch_normalization_489[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_492 (Activation)     (None, 7, 7, 160)    0           batch_normalization_494[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_47 (AveragePo (None, 7, 7, 768)    0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_487 (Conv2D)             (None, 7, 7, 192)    147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_490 (Conv2D)             (None, 7, 7, 192)    215040      activation_487[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_495 (Conv2D)             (None, 7, 7, 192)    215040      activation_492[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_496 (Conv2D)             (None, 7, 7, 192)    147456      average_pooling2d_47[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_487 (BatchN (None, 7, 7, 192)    576         conv2d_487[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_490 (BatchN (None, 7, 7, 192)    576         conv2d_490[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_495 (BatchN (None, 7, 7, 192)    576         conv2d_495[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_496 (BatchN (None, 7, 7, 192)    576         conv2d_496[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_485 (Activation)     (None, 7, 7, 192)    0           batch_normalization_487[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_488 (Activation)     (None, 7, 7, 192)    0           batch_normalization_490[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_493 (Activation)     (None, 7, 7, 192)    0           batch_normalization_495[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_494 (Activation)     (None, 7, 7, 192)    0           batch_normalization_496[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, 7, 7, 768)    0           activation_485[0][0]             \n",
            "                                                                 activation_488[0][0]             \n",
            "                                                                 activation_493[0][0]             \n",
            "                                                                 activation_494[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_501 (Conv2D)             (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_501 (BatchN (None, 7, 7, 160)    480         conv2d_501[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_499 (Activation)     (None, 7, 7, 160)    0           batch_normalization_501[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_502 (Conv2D)             (None, 7, 7, 160)    179200      activation_499[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_502 (BatchN (None, 7, 7, 160)    480         conv2d_502[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_500 (Activation)     (None, 7, 7, 160)    0           batch_normalization_502[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_498 (Conv2D)             (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_503 (Conv2D)             (None, 7, 7, 160)    179200      activation_500[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_498 (BatchN (None, 7, 7, 160)    480         conv2d_498[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_503 (BatchN (None, 7, 7, 160)    480         conv2d_503[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_496 (Activation)     (None, 7, 7, 160)    0           batch_normalization_498[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_501 (Activation)     (None, 7, 7, 160)    0           batch_normalization_503[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_499 (Conv2D)             (None, 7, 7, 160)    179200      activation_496[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_504 (Conv2D)             (None, 7, 7, 160)    179200      activation_501[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_499 (BatchN (None, 7, 7, 160)    480         conv2d_499[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_504 (BatchN (None, 7, 7, 160)    480         conv2d_504[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_497 (Activation)     (None, 7, 7, 160)    0           batch_normalization_499[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_502 (Activation)     (None, 7, 7, 160)    0           batch_normalization_504[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_48 (AveragePo (None, 7, 7, 768)    0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_497 (Conv2D)             (None, 7, 7, 192)    147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_500 (Conv2D)             (None, 7, 7, 192)    215040      activation_497[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_505 (Conv2D)             (None, 7, 7, 192)    215040      activation_502[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_506 (Conv2D)             (None, 7, 7, 192)    147456      average_pooling2d_48[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_497 (BatchN (None, 7, 7, 192)    576         conv2d_497[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_500 (BatchN (None, 7, 7, 192)    576         conv2d_500[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_505 (BatchN (None, 7, 7, 192)    576         conv2d_505[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_506 (BatchN (None, 7, 7, 192)    576         conv2d_506[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_495 (Activation)     (None, 7, 7, 192)    0           batch_normalization_497[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_498 (Activation)     (None, 7, 7, 192)    0           batch_normalization_500[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_503 (Activation)     (None, 7, 7, 192)    0           batch_normalization_505[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_504 (Activation)     (None, 7, 7, 192)    0           batch_normalization_506[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, 7, 7, 768)    0           activation_495[0][0]             \n",
            "                                                                 activation_498[0][0]             \n",
            "                                                                 activation_503[0][0]             \n",
            "                                                                 activation_504[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_511 (Conv2D)             (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_511 (BatchN (None, 7, 7, 192)    576         conv2d_511[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_509 (Activation)     (None, 7, 7, 192)    0           batch_normalization_511[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_512 (Conv2D)             (None, 7, 7, 192)    258048      activation_509[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_512 (BatchN (None, 7, 7, 192)    576         conv2d_512[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_510 (Activation)     (None, 7, 7, 192)    0           batch_normalization_512[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_508 (Conv2D)             (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_513 (Conv2D)             (None, 7, 7, 192)    258048      activation_510[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_508 (BatchN (None, 7, 7, 192)    576         conv2d_508[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_513 (BatchN (None, 7, 7, 192)    576         conv2d_513[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_506 (Activation)     (None, 7, 7, 192)    0           batch_normalization_508[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_511 (Activation)     (None, 7, 7, 192)    0           batch_normalization_513[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_509 (Conv2D)             (None, 7, 7, 192)    258048      activation_506[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_514 (Conv2D)             (None, 7, 7, 192)    258048      activation_511[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_509 (BatchN (None, 7, 7, 192)    576         conv2d_509[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_514 (BatchN (None, 7, 7, 192)    576         conv2d_514[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_507 (Activation)     (None, 7, 7, 192)    0           batch_normalization_509[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_512 (Activation)     (None, 7, 7, 192)    0           batch_normalization_514[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_49 (AveragePo (None, 7, 7, 768)    0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_507 (Conv2D)             (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_510 (Conv2D)             (None, 7, 7, 192)    258048      activation_507[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_515 (Conv2D)             (None, 7, 7, 192)    258048      activation_512[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_516 (Conv2D)             (None, 7, 7, 192)    147456      average_pooling2d_49[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_507 (BatchN (None, 7, 7, 192)    576         conv2d_507[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_510 (BatchN (None, 7, 7, 192)    576         conv2d_510[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_515 (BatchN (None, 7, 7, 192)    576         conv2d_515[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_516 (BatchN (None, 7, 7, 192)    576         conv2d_516[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_505 (Activation)     (None, 7, 7, 192)    0           batch_normalization_507[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_508 (Activation)     (None, 7, 7, 192)    0           batch_normalization_510[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_513 (Activation)     (None, 7, 7, 192)    0           batch_normalization_515[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_514 (Activation)     (None, 7, 7, 192)    0           batch_normalization_516[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, 7, 7, 768)    0           activation_505[0][0]             \n",
            "                                                                 activation_508[0][0]             \n",
            "                                                                 activation_513[0][0]             \n",
            "                                                                 activation_514[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_519 (Conv2D)             (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_519 (BatchN (None, 7, 7, 192)    576         conv2d_519[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_517 (Activation)     (None, 7, 7, 192)    0           batch_normalization_519[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_520 (Conv2D)             (None, 7, 7, 192)    258048      activation_517[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_520 (BatchN (None, 7, 7, 192)    576         conv2d_520[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_518 (Activation)     (None, 7, 7, 192)    0           batch_normalization_520[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_517 (Conv2D)             (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_521 (Conv2D)             (None, 7, 7, 192)    258048      activation_518[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_517 (BatchN (None, 7, 7, 192)    576         conv2d_517[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_521 (BatchN (None, 7, 7, 192)    576         conv2d_521[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_515 (Activation)     (None, 7, 7, 192)    0           batch_normalization_517[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_519 (Activation)     (None, 7, 7, 192)    0           batch_normalization_521[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_518 (Conv2D)             (None, 3, 3, 320)    552960      activation_515[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_522 (Conv2D)             (None, 3, 3, 192)    331776      activation_519[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_518 (BatchN (None, 3, 3, 320)    960         conv2d_518[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_522 (BatchN (None, 3, 3, 192)    576         conv2d_522[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_516 (Activation)     (None, 3, 3, 320)    0           batch_normalization_518[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_520 (Activation)     (None, 3, 3, 192)    0           batch_normalization_522[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_24 (MaxPooling2D) (None, 3, 3, 768)    0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, 3, 3, 1280)   0           activation_516[0][0]             \n",
            "                                                                 activation_520[0][0]             \n",
            "                                                                 max_pooling2d_24[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_527 (Conv2D)             (None, 3, 3, 448)    573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_527 (BatchN (None, 3, 3, 448)    1344        conv2d_527[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_525 (Activation)     (None, 3, 3, 448)    0           batch_normalization_527[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_524 (Conv2D)             (None, 3, 3, 384)    491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_528 (Conv2D)             (None, 3, 3, 384)    1548288     activation_525[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_524 (BatchN (None, 3, 3, 384)    1152        conv2d_524[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_528 (BatchN (None, 3, 3, 384)    1152        conv2d_528[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_522 (Activation)     (None, 3, 3, 384)    0           batch_normalization_524[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_526 (Activation)     (None, 3, 3, 384)    0           batch_normalization_528[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_525 (Conv2D)             (None, 3, 3, 384)    442368      activation_522[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_526 (Conv2D)             (None, 3, 3, 384)    442368      activation_522[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_529 (Conv2D)             (None, 3, 3, 384)    442368      activation_526[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_530 (Conv2D)             (None, 3, 3, 384)    442368      activation_526[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_50 (AveragePo (None, 3, 3, 1280)   0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_523 (Conv2D)             (None, 3, 3, 320)    409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_525 (BatchN (None, 3, 3, 384)    1152        conv2d_525[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_526 (BatchN (None, 3, 3, 384)    1152        conv2d_526[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_529 (BatchN (None, 3, 3, 384)    1152        conv2d_529[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_530 (BatchN (None, 3, 3, 384)    1152        conv2d_530[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_531 (Conv2D)             (None, 3, 3, 192)    245760      average_pooling2d_50[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_523 (BatchN (None, 3, 3, 320)    960         conv2d_523[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_523 (Activation)     (None, 3, 3, 384)    0           batch_normalization_525[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_524 (Activation)     (None, 3, 3, 384)    0           batch_normalization_526[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_527 (Activation)     (None, 3, 3, 384)    0           batch_normalization_529[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_528 (Activation)     (None, 3, 3, 384)    0           batch_normalization_530[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_531 (BatchN (None, 3, 3, 192)    576         conv2d_531[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_521 (Activation)     (None, 3, 3, 320)    0           batch_normalization_523[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, 3, 3, 768)    0           activation_523[0][0]             \n",
            "                                                                 activation_524[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_8 (Concatenate)     (None, 3, 3, 768)    0           activation_527[0][0]             \n",
            "                                                                 activation_528[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_529 (Activation)     (None, 3, 3, 192)    0           batch_normalization_531[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, 3, 3, 2048)   0           activation_521[0][0]             \n",
            "                                                                 mixed9_0[0][0]                   \n",
            "                                                                 concatenate_8[0][0]              \n",
            "                                                                 activation_529[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_536 (Conv2D)             (None, 3, 3, 448)    917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_536 (BatchN (None, 3, 3, 448)    1344        conv2d_536[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_534 (Activation)     (None, 3, 3, 448)    0           batch_normalization_536[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_533 (Conv2D)             (None, 3, 3, 384)    786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_537 (Conv2D)             (None, 3, 3, 384)    1548288     activation_534[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_533 (BatchN (None, 3, 3, 384)    1152        conv2d_533[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_537 (BatchN (None, 3, 3, 384)    1152        conv2d_537[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_531 (Activation)     (None, 3, 3, 384)    0           batch_normalization_533[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_535 (Activation)     (None, 3, 3, 384)    0           batch_normalization_537[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_534 (Conv2D)             (None, 3, 3, 384)    442368      activation_531[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_535 (Conv2D)             (None, 3, 3, 384)    442368      activation_531[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_538 (Conv2D)             (None, 3, 3, 384)    442368      activation_535[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_539 (Conv2D)             (None, 3, 3, 384)    442368      activation_535[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_51 (AveragePo (None, 3, 3, 2048)   0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_532 (Conv2D)             (None, 3, 3, 320)    655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_534 (BatchN (None, 3, 3, 384)    1152        conv2d_534[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_535 (BatchN (None, 3, 3, 384)    1152        conv2d_535[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_538 (BatchN (None, 3, 3, 384)    1152        conv2d_538[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_539 (BatchN (None, 3, 3, 384)    1152        conv2d_539[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_540 (Conv2D)             (None, 3, 3, 192)    393216      average_pooling2d_51[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_532 (BatchN (None, 3, 3, 320)    960         conv2d_532[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_532 (Activation)     (None, 3, 3, 384)    0           batch_normalization_534[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_533 (Activation)     (None, 3, 3, 384)    0           batch_normalization_535[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_536 (Activation)     (None, 3, 3, 384)    0           batch_normalization_538[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_537 (Activation)     (None, 3, 3, 384)    0           batch_normalization_539[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_540 (BatchN (None, 3, 3, 192)    576         conv2d_540[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_530 (Activation)     (None, 3, 3, 320)    0           batch_normalization_532[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_532[0][0]             \n",
            "                                                                 activation_533[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_9 (Concatenate)     (None, 3, 3, 768)    0           activation_536[0][0]             \n",
            "                                                                 activation_537[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_538 (Activation)     (None, 3, 3, 192)    0           batch_normalization_540[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, 3, 3, 2048)   0           activation_530[0][0]             \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_9[0][0]              \n",
            "                                                                 activation_538[0][0]             \n",
            "==================================================================================================\n",
            "Total params: 21,802,784\n",
            "Trainable params: 0\n",
            "Non-trainable params: 21,802,784\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQ4Ai-EooOSx",
        "colab_type": "code",
        "outputId": "23fd336d-4737-4fd1-992c-b9ddbf913cba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1177
        }
      },
      "source": [
        "from keras.layers import Dropout\n",
        "last_layer=pre_trained_model.get_layer('mixed10').output\n",
        "X=Flatten()(last_layer)\n",
        "X=Dense(512,activation=tf.nn.relu)(X)\n",
        "X=Dropout(0.4)(X)\n",
        "pred=Dense(3,activation=tf.nn.softmax)(X)\n",
        "\n",
        "model=Model(inputs=pre_trained_model.input,outputs=pred)\n",
        "  \n",
        "\n",
        "model.compile(loss = 'categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit_generator(train_generator, epochs=25, validation_data = validation_generator, verbose = 1,steps_per_epoch=30,validation_steps=15)\n",
        "\n",
        "model.save(\"rps.h5\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "30/30 [==============================] - 38s 1s/step - loss: 8.0884 - acc: 0.4325 - val_loss: 8.8358 - val_acc: 0.3932\n",
            "Epoch 2/25\n",
            "30/30 [==============================] - 22s 731ms/step - loss: 3.1476 - acc: 0.7009 - val_loss: 4.9504 - val_acc: 0.6709\n",
            "Epoch 3/25\n",
            "30/30 [==============================] - 23s 753ms/step - loss: 1.5929 - acc: 0.8149 - val_loss: 7.1941 - val_acc: 0.4518\n",
            "Epoch 4/25\n",
            "30/30 [==============================] - 22s 736ms/step - loss: 1.3848 - acc: 0.8347 - val_loss: 3.5349 - val_acc: 0.6218\n",
            "Epoch 5/25\n",
            "30/30 [==============================] - 24s 811ms/step - loss: 1.0113 - acc: 0.8623 - val_loss: 4.3464 - val_acc: 0.6261\n",
            "Epoch 6/25\n",
            "30/30 [==============================] - 23s 771ms/step - loss: 0.6086 - acc: 0.8996 - val_loss: 5.0053 - val_acc: 0.4808\n",
            "Epoch 7/25\n",
            "30/30 [==============================] - 23s 778ms/step - loss: 0.5758 - acc: 0.8982 - val_loss: 2.8555 - val_acc: 0.5548\n",
            "Epoch 8/25\n",
            "30/30 [==============================] - 23s 756ms/step - loss: 0.4494 - acc: 0.9094 - val_loss: 2.0447 - val_acc: 0.5449\n",
            "Epoch 9/25\n",
            "30/30 [==============================] - 24s 807ms/step - loss: 0.5886 - acc: 0.8917 - val_loss: 1.1864 - val_acc: 0.6603\n",
            "Epoch 10/25\n",
            "30/30 [==============================] - 23s 757ms/step - loss: 0.3528 - acc: 0.9154 - val_loss: 1.6518 - val_acc: 0.6603\n",
            "Epoch 11/25\n",
            "30/30 [==============================] - 22s 733ms/step - loss: 0.3411 - acc: 0.9258 - val_loss: 3.3615 - val_acc: 0.5066\n",
            "Epoch 12/25\n",
            "30/30 [==============================] - 24s 790ms/step - loss: 0.4027 - acc: 0.9233 - val_loss: 1.3619 - val_acc: 0.6667\n",
            "Epoch 13/25\n",
            "30/30 [==============================] - 23s 767ms/step - loss: 0.3366 - acc: 0.9200 - val_loss: 2.6719 - val_acc: 0.6090\n",
            "Epoch 14/25\n",
            "30/30 [==============================] - 23s 751ms/step - loss: 0.2696 - acc: 0.9343 - val_loss: 5.5676 - val_acc: 0.3803\n",
            "Epoch 15/25\n",
            "30/30 [==============================] - 23s 755ms/step - loss: 0.2575 - acc: 0.9420 - val_loss: 1.1491 - val_acc: 0.6732\n",
            "Epoch 16/25\n",
            "30/30 [==============================] - 24s 800ms/step - loss: 0.2825 - acc: 0.9383 - val_loss: 5.7852 - val_acc: 0.4744\n",
            "Epoch 17/25\n",
            "30/30 [==============================] - 22s 735ms/step - loss: 0.2553 - acc: 0.9357 - val_loss: 1.4284 - val_acc: 0.6453\n",
            "Epoch 18/25\n",
            "30/30 [==============================] - 23s 755ms/step - loss: 0.2270 - acc: 0.9450 - val_loss: 1.1327 - val_acc: 0.7201\n",
            "Epoch 19/25\n",
            "30/30 [==============================] - 26s 850ms/step - loss: 0.2538 - acc: 0.9433 - val_loss: 4.1503 - val_acc: 0.5000\n",
            "Epoch 20/25\n",
            "30/30 [==============================] - 24s 784ms/step - loss: 0.2171 - acc: 0.9458 - val_loss: 3.8133 - val_acc: 0.4915\n",
            "Epoch 21/25\n",
            "30/30 [==============================] - 23s 753ms/step - loss: 0.2380 - acc: 0.9454 - val_loss: 0.8859 - val_acc: 0.7030\n",
            "Epoch 22/25\n",
            "30/30 [==============================] - 23s 751ms/step - loss: 0.1969 - acc: 0.9503 - val_loss: 1.5091 - val_acc: 0.6047\n",
            "Epoch 23/25\n",
            "30/30 [==============================] - 24s 800ms/step - loss: 0.1603 - acc: 0.9583 - val_loss: 1.1832 - val_acc: 0.6689\n",
            "Epoch 24/25\n",
            "30/30 [==============================] - 22s 731ms/step - loss: 0.2691 - acc: 0.9430 - val_loss: 3.9574 - val_acc: 0.5406\n",
            "Epoch 25/25\n",
            "30/30 [==============================] - 23s 752ms/step - loss: 0.2142 - acc: 0.9540 - val_loss: 3.8168 - val_acc: 0.5128\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-68b4a4d2b7e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rps.h5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/network.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[1;32m   1088\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1089\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msave_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1090\u001b[0;31m         \u001b[0msave_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_optimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36msave_model\u001b[0;34m(model, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[1;32m    380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0m_serialize_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_optimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mopened_new_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36m_serialize_model\u001b[0;34m(model, f, include_optimizer)\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0mlayer_group\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_weights_group\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0msymbolic_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m         \u001b[0mweight_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbolic_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m         \u001b[0mweight_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbolic_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mbatch_get_value\u001b[0;34m(ops)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \"\"\"\n\u001b[1;32m   2419\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    927\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 929\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    930\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1150\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1152\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1153\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1154\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1327\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1328\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1329\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1330\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1332\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1334\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1335\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1336\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1317\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1319\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1321\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1405\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aeTRVCr6aosw",
        "colab_type": "code",
        "outputId": "34822919-dedd-4369-bb7d-6dc3b0999f14",
        "colab": {
          "height": 306
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GMCrSb0je8/JlXnpDJnU3jGcY6HSGXv1QjWYLD7/wFbfeMIolMztfo6QrX50o5Y3dp/nd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\n1y2Df0OjGYtVGlC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RbzDz6ZFi9h+7QJPJwrJ+bALijmYkhSFJ9jXwe1JR20ReiZ5Z48NF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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f620ed6b350>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f620e844e50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZABJp7T3VLCU",
        "colab_type": "code",
        "outputId": "17a35fef-b8d2-4278-cb4f-dfab29abaf1d",
        "colab": {
          "height": 2317
        }
      },
      "source": [
        "import numpy as np\n",
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        " \n",
        "  # predicting images\n",
        "  path = fn\n",
        "  img = image.load_img(path, target_size=(150, 150))\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "\n",
        "  images = np.vstack([x])\n",
        "  classes = model.predict(images, batch_size=10)\n",
        "  print(fn)\n",
        "  print(classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0d1390a3-9cc0-4236-8a81-d32c569e6a1d\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-0d1390a3-9cc0-4236-8a81-d32c569e6a1d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML at 0x7f620dbb2fd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving paper2.png to paper2 (1).png\n",
            "Saving rock-hires2.png to rock-hires2.png\n",
            "Saving scissors-hires2.png to scissors-hires2.png\n",
            "Saving paper-hires2.png to paper-hires2.png\n",
            "Saving paper-hires1.png to paper-hires1.png\n",
            "Saving rock-hires1.png to rock-hires1 (1).png\n",
            "Saving scissors-hires1.png to scissors-hires1.png\n",
            "Saving scissors9.png to scissors9.png\n",
            "Saving scissors8.png to scissors8.png\n",
            "Saving scissors7.png to scissors7 (1).png\n",
            "Saving rock9.png to rock9.png\n",
            "Saving rock8.png to rock8.png\n",
            "Saving rock7.png to rock7.png\n",
            "Saving paper9.png to paper9.png\n",
            "Saving paper8.png to paper8.png\n",
            "Saving paper7.png to paper7.png\n",
            "Saving scissors6.png to scissors6.png\n",
            "Saving scissors5.png to scissors5.png\n",
            "Saving scissors4.png to scissors4.png\n",
            "Saving paper6.png to paper6.png\n",
            "Saving paper5.png to paper5.png\n",
            "Saving paper4.png to paper4.png\n",
            "Saving rock6.png to rock6.png\n",
            "Saving rock5.png to rock5.png\n",
            "Saving rock4.png to rock4.png\n",
            "Saving scissors3.png to scissors3.png\n",
            "Saving scissors2.png to scissors2.png\n",
            "Saving scissors1.png to scissors1.png\n",
            "Saving paper3.png to paper3.png\n",
            "Saving paper1.png to paper1.png\n",
            "Saving rock3.png to rock3.png\n",
            "Saving rock2.png to rock2.png\n",
            "Saving rock1.png to rock1.png\n",
            "scissors-hires1.png\n",
            "[[ 0.  0.  1.]]\n",
            "paper4.png\n",
            "[[  2.77571414e-38   0.00000000e+00   1.00000000e+00]]\n",
            "paper6.png\n",
            "[[ 1.  0.  0.]]\n",
            "scissors1.png\n",
            "[[ 0.  0.  1.]]\n",
            "scissors3.png\n",
            "[[ 0.  0.  1.]]\n",
            "scissors2.png\n",
            "[[ 0.  0.  1.]]\n",
            "scissors9.png\n",
            "[[ 0.  0.  1.]]\n",
            "rock7.png\n",
            "[[ 0.  1.  0.]]\n",
            "rock9.png\n",
            "[[ 0.  1.  0.]]\n",
            "rock8.png\n",
            "[[ 0.  1.  0.]]\n",
            "paper-hires1.png\n",
            "[[ 1.  0.  0.]]\n",
            "rock4.png\n",
            "[[ 0.  1.  0.]]\n",
            "paper5.png\n",
            "[[ 0.  0.  1.]]\n",
            "rock2.png\n",
            "[[ 0.  1.  0.]]\n",
            "paper9.png\n",
            "[[ 0.  0.  1.]]\n",
            "paper1.png\n",
            "[[ 1.  0.  0.]]\n",
            "rock6.png\n",
            "[[ 0.  1.  0.]]\n",
            "rock5.png\n",
            "[[ 0.  1.  0.]]\n",
            "scissors4.png\n",
            "[[ 0.  0.  1.]]\n",
            "rock-hires2.png\n",
            "[[ 0.  1.  0.]]\n",
            "scissors-hires2.png\n",
            "[[ 0.  0.  1.]]\n",
            "paper8.png\n",
            "[[ 1.  0.  0.]]\n",
            "paper-hires2.png\n",
            "[[ 1.  0.  0.]]\n",
            "rock3.png\n",
            "[[ 0.  1.  0.]]\n",
            "rock1.png\n",
            "[[ 0.  1.  0.]]\n",
            "paper3.png\n",
            "[[ 0.  0.  1.]]\n",
            "paper2.png\n",
            "[[ 1.  0.  0.]]\n",
            "scissors5.png\n",
            "[[ 0.  0.  1.]]\n",
            "scissors7.png\n",
            "[[ 0.  0.  1.]]\n",
            "scissors6.png\n",
            "[[ 0.  0.  1.]]\n",
            "rock-hires1.png\n",
            "[[ 0.  1.  0.]]\n",
            "scissors8.png\n",
            "[[ 0.  0.  1.]]\n",
            "paper7.png\n",
            "[[ 1.  0.  0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}