Improved_accuracy_to_89p5Test_Valid_91p1_HybridModel_04242019_.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "Improved accuracy to 89p5Test Valid 91p1 HybridModel_04242019-.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
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        "DCCl7-vOfSZ9",
        "TEkg52f_HxEg"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/KProj/blob/master/Improved_accuracy_to_89p5Test_Valid_91p1_HybridModel_04242019_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "kdTAcjYec3s7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Load standard libraries"
      ]
    },
    {
      "metadata": {
        "id": "oCo6LRTTcxmC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import gzip\n",
        "%matplotlib inline\n",
        "from keras.models import Model\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.layers import Input,Dense,Flatten,Dropout,merge,Reshape,Conv2D,MaxPooling2D,UpSampling2D,Conv2DTranspose\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.models import Model,Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import Adadelta, RMSprop,SGD,Adam\n",
        "from keras import regularizers\n",
        "from keras import backend as K\n",
        "from keras.utils import to_categorical\n",
        "from keras.datasets import cifar10\n",
        "from sklearn import preprocessing\n",
        "from keras import losses\n",
        "from keras.models import model_from_json\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.datasets import cifar10\n",
        "from keras import regularizers\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Sg_ZApCOPN9u",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oG3txkjndHWQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "keras.backend.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8rDrYIgZc20A",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Load image data"
      ]
    },
    {
      "metadata": {
        "id": "85Dlxs7adKVS",
        "colab_type": "code",
        "outputId": "7c628b3a-ca12-490f-93ac-66290f37bb92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot\n",
        "from scipy.misc import toimage\n",
        "from keras.datasets import cifar10\n",
        "def show_imgs(X):\n",
        "    pyplot.figure(1)\n",
        "    k = 0\n",
        "    for i in range(0,4):\n",
        "        for j in range(0,4):\n",
        "            pyplot.subplot2grid((4,4),(i,j))\n",
        "            pyplot.imshow(toimage(X[k]))\n",
        "            k = k+1\n",
        "    # show the plot\n",
        "    pyplot.show()\n",
        " \n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "show_imgs(x_test[:16])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `toimage` is deprecated!\n",
            "`toimage` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use Pillow's ``Image.fromarray`` directly instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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O8NF2er2620g5XXfgeE5cX60xHA39KCJ8t8/127cBmE7G5EJIU5YlzVZM8RHA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\nvdxDuLu8zOOPPwLA0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c+VRdkF7nXkQczKNn1DO7O2do/z52VeHNvaz/PpmNHaoM0ipbMr\nRpeoz0jF8JAVfCx3QFnhYdmDBnM7z4WlaUpq8oUFr4gdGAg6pSma+f0otlm6KWmKs77LmTOTOVTd\nSZU0y3jtqkzCzM7OctFUXx2LnOvLcx16puF9Ze0ekYHci5OIODZGt9XKYRJt28ZSsvnYls7xaxPf\nyyf2VCbtZADojLbBG+73IjodWfRJltIfTKG12jQN2E8xCCgUiiircxwqOVJJYlhtyNrdCUsUVmXt\n/vxCjaLJC46O1CAR3RUqAVFfdFq6/DBff30FgKs3m/hKrt+12li+GJXU8TBRLAXdpTYjz8DZc5O8\ntS6G8+zCHC0kTeaXijmbBdrCpGvpp0ViA2BTrFQpFvyPgobpvsR2HAKDi2EpC8+W57BUnuSxZ8Xh\neb2xy6bpdPncz32eVkfqNVsrO6TLop9SxWZ0SjZxy7HyNk6fGG1YPebGJjk1Kc5VsrqBuvKOOYsm\nfYMxfOM71/i2I69Xx0dQakArlJCaHGKoHPR7iEZ/vAxTB0MZylCGcszygD1aG21CnL2wzUYsO3tl\nsoYyIWhjb4teV7wuslFGRqW66z38CHZLwoudrT16ffmsoxLGx8wxfoEkZ2Q9UMlW+ztPkiZ5k7iy\n7Bw8WVkKXAPaXCyxekdG+fpxk0baIolPdngbRREvvfIqAH/7zDxBIKmAvd0GFRPy7+w26IeD0OoV\n/uLrfw7Af/13fiHnurp7927eoTE/P0/FjEwr2855wixgfdWEp+02n/ussKyWyiXu3pUixfLyMl1T\nXIzSmPjAvL1rim0jo6PUKjU2G82jVsfRi1LMeXKeQZYx+5ikZqZGi/zHr/6lHNOfZmlJvKiFhYU8\njZDGET/zrAyH3Fh+hw0DgUhikXRFF46VUHfEEx2vZiQmggqKHonBmBgtVfAM3OdYtUDPrN1yqokM\nclvqJ7jGq7Ycj0rBPfHA9anOOH1OCng7S3dpGlTH0CpTNwSfF2vTLN6TNGIw4fPMz34GgOTdHcqm\nF3ZdddnZE53XihUCEyLcW1zklFm7eqfDrTcFy2Ptziq9VH6sWnHpbsnrm75H/6c/DkC/v0NjS9Z0\naFtgxnEjMmzn8DgdD9TQohRt06qyuLVMS8sDbQcOTskQ2bU3aRvgDBXBxLTka0frVXaXhErFS5fw\ndyT/EoYJg2RslLroQVU9aZKadhBHe3k7Eii8PI9rkQ4oX+KEyFCS44HlmCbxXpdilmGd7MKtuT5Z\nTI7jsWoQ9z3HpmowNz3Xyx+6bqfNV7/6VQAefeQhPvFxIVK8c+cOi6bRO8syTs1Ki0wx8PE8yRui\nLd55R0Kuxm6DZ575BADjhXFefFHax65ceXOfbTTbr806tpXPlCdxQqlQ+KjJBA8l7R7smVRAv9+n\nZDq9Go09ksgwr3Y77GxIlfzC6TMsDRrm1zf5zNOfAuDZh8v88fdlnY0WE+Y8WdPvdOfp2rIhNnod\nqgb6rxH26SLr9e52wkMPyzGzE0XWB0bFt3HLJu9QH0Ensjl6vgdoTrp6e1HIzTXZoB6bP8vdVQnz\nNxodnqpIp0FidyhfEGP8ncYS9dOSXjhz5hKPnJbp0TfvvcvbJuW3tHiL0rx0Ds3PnKI0IenFnXbK\nNzckjbPjpQRjsr7bnYTdabFN9XKFkbLUfV5bvc6m2TAt18cdEEHjk9mH56444bdgKEMZylB+8uV9\nPVql1GngXwFTyGDv72itf1spNQr8HrAALAJf0lrvvs+XsWwI6xa3Vuh7ZrRN6xxubPHuHd4x8Inz\npxYoG9g9tzRKeVI8ASsoYa/Kzr5x9xaDonW7HYPxXK00xCmbApjW+VhpoVDAMi5/RkIyGMfd2WZ3\nXbxA3Wowbsb0Kq5Hu9/L+bKOUo5StxoZpQXY2tri9u1FAB65fImKuRY/8HMvE6VYXZNQ7OaNm5w5\nLZHDV77yRzno+bu33+WZT4i3emp2hrExw8fmBtwwfGCNxi67u3JqhUKQ05w7jkM0oHXO9nmrskzn\noMrNVotWq7V/TkcsR6nfTNucvyQV6tmZKpEpzLx6fRsnkPXkBw4/9YxEBlMjI7z9thwTuBbLpmF+\n6W5CGMr7sZXRM2mETMWURkzqKnKompC20t5jftbQbHdHKRdEj/MzFlOm24FME5te5Harj+0OoPz6\ntDs90Eev36PUbTsO+drbQpR7dmKey2cWAPjuqy9ybWkRgMkzM0Smi+VTj32C127LUMO1xbs8VDkL\nwIXJU4wH0oO7OTvLxrrBNMFhdFz6nvfqCfFPPQlAMUo5W5E17SuPDUsMSafX5dXrQny53GrSGyCl\nRSlFM8of2BYqO7xNOEzqIAH+sdb6FaVUBXhZKfVV4O8DX9Na/6ZS6teBXwf+yY/9oiThlrn4ZhoS\nDfImqSYwAwiW0+Zb3/82ADMzs3zGhL1BEKB8MRhO3aJm0gLKCVCetLA0m20y03hfK/rYZkqnM3mS\n+gAAGWlJREFU027nMIm2ZeVzz6mOaRt4xrtvv0lsiAhHfRcMCIVKM7I0wzoeFoAj061tWUxOSJi1\nsrKOa4AwHn34kTw0r9frTBjyylsvvMiu6dy4eu0qn/qUNGtvbm5y7Zp0L7z11lW+YyATz87P59/v\neQW+8x25R7bt8NJLLwEC3vOFL3wBgHa7wx/90R8BcOXaWzm8XKYzbKPLVqvF8vLye1hJj1iOTL+e\nZ1OuiEGdG3W4uy4P/b3dkPOjYvCWlm7jGTCYeyv30O4+mei3TIP9vc0Kf/OT4+Y7fbYbYkQbuymZ\neRpVpthdk3rErtfHNvCJza17rN+VDe7Nl75H3+RuFfs4qdXREUmnAUHBo1Bw6XePpavj6OxClnH1\nnuRNv/3Gazz3pEB2PnzhHK+/8QYA9rqfY3mEnQ5VE9q3/UW+fvP7AEyqMiMV6TqYnVogNJRVvXZI\n5kg65dKlC6RmMrSxu4syFE4zk+PUDc3WW7dvUzI0QfWgyvqq3ItUa5wRuUlulmK56rBMNu+fOtBa\nr2qtXzGvWwhX+xzwt4DfNYf9LvC3D/mbQzEy1O3xylC/xydD3d6f3FcxTCm1ADwFvABMaa1XzZ/W\nkBDix0qYxGwZwrTEO8C3k+m8QDI5O8HaXQnhn3/hG5xfkH7Q6anZvDOh205pGUQjilW6Bocg7m8x\nIAGKIg/VHlBod3JA6lqtliOb2cqmvWcQqe4uMVkQL7lW3G/yTtOMJMvEEz5G+bC6tW0H25aC4tbW\nFiVzDZubW4ya9Ivnee/pkR3gG7zy8iv83M8KweL4+FjuYW5sbNIw6EmdVpMwlK6GleW1nHGiVqvz\nW7/1WwA8+ugjPPfccwBsb2/x+S98Xi5ga4O7pmLsuVaOeGRZFmmWPpAR0Q+rXyzNH39XPvLn397A\nNewJ+HVM0AXtFn/xTfGums02n/2cNNjPTk/w9pas3VAHPH5JPNqZUpLz1+1+r89abKjHlcJ2RcF+\nocP1t6Rg9sqLr3L2nKR4GnttVjckGhup16nU5H4/8dBlmgbnolorcu/OzWPX74fVraUUPdPr/cby\nuzxyTnqIH56e5amPPQ7ArZs3cQZklE4Bry+R6+npSe5tSgHsevMmtUSKW+lKwoQtxduJYsDilnil\nFz/xSR4NBBPkyptv8PzLAgO6vXCGkTHxhmvFKo+dlTRRp9+hWxZ9Lm2vE5n+ccePqQQxRw6TqJQq\nA38A/COtdVMdyFlqrbUaJPb+08/9KvCrAKWST3/wiwrcQQeWcgZQpzgFi+nTEt7eWV7i+i3JlYyO\nVPNugbDVYvGGTMus72yQmRyfZ3u0WtLJECcpykyM7O3t8fgTcsNG6iM5JmvgFLGRELvd7uGbroNT\n05P5xJPn2KiCh+0cX4PGUejWdV32zKaRJGnewra9vUXvlCy+JE3pmdat8+fP8+xPCe7sd57/S15+\n+WUASqVyjmsrhlh+utPp0jcQlFqTpyPa7TatlqQgOt0uzzwjKYgrb77FqTmZpBodHeXuXfPsafJO\nD8UA6u/YDcGH1u/oaJ2//kkxANvtmMxMC2yvXKe3Ixv92soalpkcKFk2N68vAvDYQ5fwt+WYNEn5\nf/6D1CDKjsb1ZeFvdX2skml7K7g5tVO71eT8xQUAHn3iUToGD2FicoxC2UyPZZqSycM7Ks1BZZRu\nY3G8gD1HoVt/pELRbBSLrS2+/pasRY+M8/MLAMwlMXduyYbz6JkFHh2XDgTdSJg8L8/wm2vv8k4o\nTloj7nGmLJ0Gb60uUj4lHQj9tCfstcDMxBRnzglRaJgldAyUp041bTOR5ofw9AVh4t3e3qXTNROO\nxYww6udgPu8nh3LTlFIuosx/rbX+d+btdaXUjPn7DLDxwz6rtf4drfXTWuunA/9kT1d9FHJUunWO\ncSP4SZaj0m/ZAJ0PZV+OSreumQr7qyyH6TpQwL8Ermmt/9mBP30F+BXgN81///D9vivRGbEaoEfp\nvChiWVY+g6+UhWdCeJyUJZMkf/bpZ1Bm98iimFZDdva3bl6l0xXXPnCdPCztR3EO9p1EMY1vS5g1\nMz3Bw5c+Jt+jUqZNn+jCpUssGxDw3TRm0ngLURwdGqHnfuUodZtlWe7RVqs1UlMUvHNniYq5lna3\nz7rprChX6zz2mOzUr7z0Am9fE36vkdERiobfKtM613kYhsQmRaMs8lRKmum8ELO5ucF1w9n06Z/6\nVJ6m2N7b5cqbUlXWaBwzsDAxNka5XKC9tHJYld2XHKV+y6USv/ZL0lHQ7Mfs7MpwQWPTY2lRhje8\npx7CM3rvNhu8eU/ev720TGTWa12PEyeSDlsNPVrGG3YcqJUNBofukxnvancnZnxM1vGZ82e5+rqg\nU83NzeWjtVeuXMvvh6NStHHZAsvG0UkOCn6UcpS6BY0akB4qxSum4Le5ucbjlwWc/typMxTnTVqg\nXGDMkQLkkwUXAwvB+VPn+NNbkrr57sot3kllrRdmFW+H8p131q9yeVy82MmREc6cWgBgcXmJrqFl\nv7e0wrUr8jysrN3l018Ufrypyhh3GtInbUcQ2kmOffB+chg36DPALwNXlFKvmfd+A1Hk7yulvgzc\nAb70fl+kVYbGVJjVPuQYKXm3gOu4gywCge/RbErFVGubxLRTJMCEwYsd27tDst3Pv6haEa1XVbBP\ngpe6bNwRpf/FV/8MIrnsmfkFWi0xTi3tsZOJgXllcZPHzprTjCK8bD/cPWI5Mt1mWZYPZdi2yqe7\n7q2s0o/MQ7u7y7aBKwzjiOW7iwAkcYoykJWnT5+jZNgAms1mzpjgFlz6hoZIkBMNEaQDmTWYbvJZ\nWpJ82cVzF/Kc+vj4ZJ6OUJadp2Gm5maYmp7m3sbOYfV1v3J0a1drMIatZMfEvgF3OXeaP/rDrwFC\nNTNSM3nByXH+//bOJTau6zzA37mv4czwMXxJFinJtGzJ8SONLbtOYAdtnD6QOgHadFEkiwJFu+gm\nQLtM24033bVFFwUCtGh2LYoArdO4sJPWdtLARf2UZFWSpUjWkzRJcUjOkJy5c5+ni/PPJWs0lixr\nhmPrfICA0WCG99z/nvnPOf/zD37vGwCUApct6Q4SZw6bW1IMJoMwMXI/dbHJf59fAODSynX2VKTj\n8MQoroR6DflBkcmYpCklCVMMvIBU7Op7pidpivfcdRTDY+dx3Z5sFG6bbDOtaUk36jRNaYucO1HC\ntZOme8L4+VPsl6iXvWNjVCPze3xs7ggVscUedH1+e8aEbjmey/OXjQmiVco4VTdlD189HjB07y8D\nMD11hFDqcaxdrxcdRF599TWuzpvF3/ehLrbwPeNTrMiCGTdC0ry8XW/iBtxQ0WqtX4WfG8XwKzd3\nGcv/h5Vtb7Hy7R1Wth+Nvhv2XFmtVJajuk4sp0wpMLvJSrlMSWJqy3qIYXFcKSCSVS/LUvZMGw/h\n9JUJYkdqI5BTUZJeq3OiUJoDas20NIK8enGe7y3/GwB+7QBLy8YbGcdR0eRR6Q5Xrpod8GfvO8ih\nPTV+/pwaDMyO1uxqGo1GUaJweKRKIjvR5kaTTJIFdJ4VqZlxknBFdqKf/8IXmN1vnFjN002qUutg\nvFYrokRWV9eLHWoOOLJDnd23H6Q0YHsrZM+McUDcf98RatKqvB12it3X6dNnmL82T7vVvv0Cuc2E\nYZu6pInSaRFKlszskUN88VfN0fLixWU2GmYuXl6o01g3O6FDc7MoSaO9+9BhdGSejecHIGX9Kn6H\n106b+d1Jh0hTI5OxasDRXzBe8rWN1wrT2NUr88Xzy3ROU05m1eoIy2tm15VkKcPVoYFPcc7zvGij\nnqYprnRGCZWDI/e7sLXOOwvGAV7yFFOSDn59q1kkHRzecxf7pNj6V2Z9mm0jk5cXjnO+Y16/dOlV\n1Kox6Xxp7kniq8YEdOLEGyxeNr/5+voGTW3Gs2dsjKUV8/4Dhz+DkqiDk0vLdDbT7cipG9BnRatw\npKldRQVUq0ZY1WCUsjQNdBynKIhSmRii5HdDOhy2IjP5dJYQSi54OfPZVzGCjpMY6TpBliaUpPRf\nRo4r5RlLMxWuLJpj3OLKNbQcxVzXNTYJgNxj47z5MaysvsfGgzO0o8HuAqCUKjojtNvtoovExmaT\n1dXtx1yRbhRpmhZNAifHx1l830QFXF9cZPYuoyAvnr+A200qSTPGJfOsUW8WP/I0z9GiOA8dPECt\nbELJykMlJseNCaK12aDatZeHHXLdXWw1G2uNQvkPMteXV/ibv/4OAPv21oq2PnvPnmXuHpN1NHv0\nIL60VZqZOUBVEhY2m/NFBEwW7yGRgkipM46We4/aLUbFWezXqjiJ8SG5ShdKNNeaQOyvC9fmTVEk\noFItE0sbnMsXrzDimefhlmImpu7lleB8T2RyO4nlDB4lMV63oy/bNZaTJCkKIjmdmDWx6TZObvDr\nD0lnhEqJRQnjmh4b4vcfNIV8qp7PP7797wD8dPM0W4smIae5fJ5K0+iXoeU6JdmYjYw4hKNGzr/z\ntaepuSZ6wUk016XYj/ICVK6LcNIbMdhLncVisXwK6G+ZRK2ouGbHM14ZpiJlCfMoJ251Wyo7ZB2J\nHAgjvBHjLIg6bRzxnobtFovvG2N1EkWUpHRc0kmLrgEldwilJc7QU2Syw19eadGQPPUs0EW8aU5S\nxMQ5jo9OzUp3pb5J4/V32Wh1HW6DS9fLr5Ria8scfVzXoSGN7JQyjRsBSkFApWzkc8+BAxx7w1Td\nOnn8OA89aDy9U7UajXWzQ6jHETMz5lg25DtSGQraYUQYSzRCmpGGUsD9/Xl+6ctfAuD5f32ORt04\nvMrlMiUZZ5qmRFFEOOBmGTDRMMt1c8zc7LQIpNj3iXPnWFqUKnSupjZuTCQH9t3FrPRqGx52mZY+\nWCurIaMV8wzGpiYZkbbWOo6ZkOPq9N4O7S1xvFWrbEo1uyyJkNwTap8ZpVyW3m4uBHLyG3Ib7J00\nJ4lyuYpSbhEVMqho9I7qehTNOrWntivAZVkRbq21oi2fv9Rc4uUzZu520ognZkxEQRalhO+a5/XV\nu55AP2T+5vfffo5jsTktzDdPcVCabM7OjDEnZrL9UUbLl9d5wp5xI/RT587SbhlzkO9rtsJkMDss\nuMpjatRktnjKIZJCGGncYaf7rnsE7nQiEmmB0tpsFsfetZVl3rtggr6HKhVqY1KRveTjSq55pTxC\nJAqgoxM2pGnefKNFW5rd4YLuBnTvCMLPs6zbxB2tAlYjTXqTR4TdIs/zQrnC9r34nkvU3i4kk0n7\nkxSHjkQmfO7hh/iPF18E4NKF89wnR+G5g/t5Z9XYp1SmWRV79nC5QjcwvVIbYk08sQsXLzG333w3\n7oQsvm9smkkccc/cHGAy87otcYIgIMtzfiq1EgaZIPDYN2OUqKNcwtCYn3zfLY69G6sdLl8zsnjz\nrfN4nnT48F08iRCojYwwIZ2Xp8dHmagZc8x6mLG0bL67UfUoS5fnrZlJPN8oTtdJGRs2ch8bm2J6\nwsx71y1THTJjmxybKk6zWeagM9AfofjJrqG3swW7YWtpmhbzTCmFkjntEpDkZh4nJY+Lm2aOrr75\nCiv3GPvrM0efYqTbpXYp4os106jxVGU/x1JjirnqJmyERnEurbbpXDOmgyBMODhl5Pnjl35EajKN\nOTAzy6w0K73c2GCp0ykaZN6IwV7qLBaL5VNAn3e0Ljo2uj1WEMdmFdPp9qqrlNt1XKO0R23M7GKz\nPGFDeowtLC2xLI0dD03UUJLG6HkuVdktVcoVXHEQrF5vcP6q2Y01wxSc7nHNKUwHjlIoZf6OC2hp\n1BbpFJ8yg74maa3pSFRGviPmN0tUMfI8y8jEhhJlIT9++RUA/uzbf8LXnvkNAF584UUuXzDe3Sef\nfJLFK5e732ZiQpo2ui7j4+bIleVZ0RGjWiozNW6iQTrtNvUVc0TrxBHNbjF316HVMTuH0dFRlFI3\nHfS9mziOJpCqz5sbLRob5jQQhimueFEnxnw00sgvrdKWEpxrrRaRdOjYbK0wLycDRznFTrfiOXQz\nJ9e8EhOTZqf7QHI/ccvsmmpVl+qc2cXiZbhD5lqloIIvpRo7+TppKL3dEgcncNA9KJN4u+nuVgPf\nJ5VU+J2zYqfpwHP9ouwmpYBAqvK3ww7/ddUkxtSTFkcPmnoFD941Ryk0v4mn736c7Kr5/FpnhdKw\nkfnqQsj6ppnHw05QRMzocpVkXUxyvouWXnHxhQXiil+k/9+I/udtdvPcXYchsfP5foCWocQpuDIx\nRkdd5u6W45GjuCxBxNr1eeBhE5hcK1eKTCPlqMIU0Nqsk2vz/vXlNVYl1zx3A5zuxNMOqih/qEgk\nT93VCi0ZbJkX40lQ+aDTVbBa6x2te3Jcea20KqIFXKU4/64xv/z5s89SlpC6zz/+GOMSLfDwA/ej\nvm6KL/3whRfIpNZBJ03JJAqjHbYpib1yfG+FWGy0axubfP85k5VZbzRZlU689ea6qcWArHEaOtkn\nQNEqGJLkjbzkoEfMfC0FoLopNplDKFky61GLKDcLn+c55NICxQ22i/k4WhXKIwhchiUVdXS4yqQ0\nvMySNl5uns1EFRwpAxhGEbGUT8ziBrHUWPRLFeLUjCfTDr5fulnH+O6hKUwEaZoWDVkdb3tO53le\nfMbJFEPKKMjc1ZR0t9BLgCPlU0/XL3Nu0dRGeOTAvTz9wOcAeOzwUe6/25gR4rwNojsuf7bJ/Hsm\nYeS+yWmeOmJsvaWSx38eNxuPH37vByxJM8d66JCNjxbhjDdisLdpFovF8imgrztaRymqctwJggBf\nAt1LLkitYjY7Ca42+n9qfJiRkvGMNxZXWJNSe3eN1BgbM8fYvBMXcZhJmhGK80w5irY4wxaX60Xq\nouv6OBKnpx2v2NE6Sm2n2eY5NUnlVa5Ls57S6wpTHxelVLHia62L167jUpMiyZ7nMyQV0GrjNVJx\nRq6t1tkjDS477TYX6sZxePKdd3jsUZPff+HsWRaXjKOhE4b4koBQGRqiJBX98zRjad185sx7F8kl\n5vDIww9SXjIOoNW17WL7iu1xDjqu4zNdM/Ha1DIS2XVlWcJmyzhXwihjfsns3HFKhHE3XtjBF7NU\nphRSzRKdKTxxrlTLZaamjdlrcnKUsYq0LVdNos1uMH9WFHQv+WOMS+OyVCc01o1zLkx8UtXtIALV\nYKQ4lg8yXVNAlqXkcuJM47RwjHueW0QjKHQRl0ySkRcObU2qzXPxSh6JMieKE/NnWbxuHLP3Tc7y\n2BFT42N0tFrU8jgSjPGLR40jtxIE1KSY+/V6neefMwlOr791HEd0RBpUUHk8mFEHnucyXpVOChpU\nV6Bhx2TJAKPDJTpSs3Zza51AFGcWxQxjJmW5VCbbkhoISYbfzehSPiWx0abK4e2zpgBHvbGFW6p2\nR4GrzOdzpXaYDjSObPCVCw8fNkLPtxocW33/ExCAtN3pd+fDzx1FKAvRaGWoaA00VqsRyCSenRxH\nyXeiKCKU6IWfvPQyqQRxK8CXAPna2FiRpafznFSyY9pOh448r9GRKpHYry6d/RmdHeFm3aOszsBB\nsZ0pMrh4QcD0tJkTirQ4C+Y6J4mMvyBOt5jZbxaUrXbC+qa50SiMIZVjvlKUSubLJS/AFaUYJzBa\nM4tgdURR8owJxkGTZN3NQIXugu84kIgPwvUqRaiXchwSMd84ykV3QrjJ7KXdQmtNIlmNuU7I8m5y\nkC6KTWm2lbH2chAFmemcVExPW1kCrplzXuIWRasSlbMYmgVw9WqTYwumDY4CIumwMB1M8NVHvwzA\nRDDChTcuAXD8neOcOGPsvjkaPWyeUTbk4QQOfHjzroLBX+osFovlE47qR3X74mJKrQAtoN63i96Y\nKW5uPHdrrad7PZhbZUBlCzcn34GWLYBSahM4t9vj+AB27vaW2zZ3+6poAZRSb2mtH+/rRT+EQRvP\nx2EQ72UQx3QrDOJ9DOKYbpVBvJfbOSZrOrBYLJYeYxWtxWKx9JjdULR/uwvX/DAGbTwfh0G8l0Ec\n060wiPcxiGO6VQbxXm7bmPpuo7VYLJY7DWs6sFgslh7TN0WrlPqKUuqcUuqCUurb/bruB8ZwQCn1\nY6XUGaXUaaXUH8n7zyqlFpRSJ+TfM7sxvlvFyra3WPn2jjtFtn0xHShTFutnwK8B88CbwDe11md6\nfvH/O459wD6t9TGl1AjwNvBbmE6dW1rrv+jneG4HVra9xcq3d9xJsu3XjvYJ4ILW+qLWOgb+CfjN\nPl27QGu9qLWp4qu13gTeBWb7PY7bjJVtb7Hy7R13jGz7pWhngWs7/j/PLk8SpdQc8Cjwurz1LaXU\nSaXUd5VS47s2sI+OlW1vsfLtHXeMbO9IZ5hSahj4Z+CPtdYbwHeAe4FHgEXgL3dxeJ9orGx7i5Vv\n7+ilbPulaBeAAzv+v1/e6ztKKR8jzH/QWv8LgNZ6WWudadOd8e8wR5pPCla2vcXKt3fcMbLtl6J9\nEzislLpHKRUA3wB+0KdrFyhT/PTvgXe11n+14/19Oz72deBUv8f2MbCy7S1Wvr3jjpFtX+rRaq1T\npdS3gB9hWnJ9V2t9uh/X/gBPAb8L/I9S6oS896fAN5VSj2DKXl4G/nAXxnZLWNn2Fivf3nEnydZm\nhlksFkuPuSOdYRaLxdJPrKK1WCyWHmMVrcVisfQYq2gtFoulx1hFa7FYLD3GKlqLxWLpMVbRWiwW\nS4+xitZisVh6zP8CB+zi+GJTgIQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dQQPlF6RdS2m",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create Function to get data corresponding to three labels"
      ]
    },
    {
      "metadata": {
        "id": "GFyk3xzNdYNW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This function takes the entire traaining data and test data and corresponding categorical label and returns the corresponding data in class indices\n",
        "def getThreeClassesTrainTest(class_indices,train_data,train_labels,test_data,test_labels):\n",
        "  all_train_data = []\n",
        "  all_train_data_label = []\n",
        "  all_test_data = []\n",
        "  all_test_data_label = []\n",
        "  for ind in class_indices:\n",
        "    this_class_location_train =  np.where(train_labels==ind)[0] # train_labels[train_labels == ind]\n",
        "    this_class_data_train = train_data[this_class_location_train,:]\n",
        "    this_class_data_train_label = train_labels[this_class_location_train]\n",
        "#     print(this_class_location_train)\n",
        "    \n",
        "    this_class_location_test = np.where(test_labels==ind)[0] #test_labels[test_labels == ind]\n",
        "    this_class_data_test = test_data[this_class_location_test,:]\n",
        "    this_class_data_test_label = test_labels[this_class_location_test]\n",
        "    \n",
        "    all_train_data.append(this_class_data_train)\n",
        "    all_test_data.append(this_class_data_test)\n",
        "    all_train_data_label.append(this_class_data_train_label)\n",
        "    all_test_data_label.append(this_class_data_test_label)\n",
        "    \n",
        "  train_data_three = all_train_data[0]\n",
        "  train_label_three = all_train_data_label[0]\n",
        "  test_data_three = all_test_data[0]\n",
        "  test_label_three = all_test_data_label[0]\n",
        "  \n",
        "  for j in range(1,len(class_indices)):\n",
        "    train_data_three = np.concatenate((train_data_three,all_train_data[j]))\n",
        "    train_label_three = np.concatenate((train_label_three,all_train_data_label[j]))\n",
        "\n",
        "    test_data_three = np.concatenate((test_data_three,all_test_data[j]))\n",
        "    test_label_three = np.concatenate((test_label_three,all_test_data_label[j]))\n",
        "#   print(train_label_three.shape)\n",
        "  train_label_three = np.reshape(train_label_three,newshape=(-1,))\n",
        "  test_label_three = np.reshape(test_label_three,newshape=(-1,))\n",
        "  return train_data_three, train_label_three, test_data_three,test_label_three"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ex0j41PLde2x",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Get data for three classes using getThreeClassesTrainTest function"
      ]
    },
    {
      "metadata": {
        "id": "IpqbOTfBdhHk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = getThreeClassesTrainTest([ 0,3,5 ],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9gK_tiIWd2Nf",
        "colab_type": "code",
        "outputId": "30e5ee44-1575-43e6-cbdf-3d88bf2d72f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5000.,    0.,    0.,    0.,    0.,    0., 5000.,    0.,    0.,\n",
              "        5000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PAb8f90RGrIDvJjnYvh1hWazEAKgjSV4LfB34aFX9etzzWW5V9UJVXcTgU/Sbk6zYU35J\n3gMcraqD457LGLyzqi5m8M3Ju9pp3iW3EgOw4NdNaGVo58G/Dny5qr4x7vmMUlU9A9wNbB33XJbR\npcB72/nwfcBlSf59vFMajao60m6PAt9kcGp7ya3EAPh1Ex1ob4jeAhyqqs+Mez6jkGQiyZq2/BoG\nFzr8dLyzWj5VdX1VbaiqSQZ/j++qqr8b87SWXZJz24UNJDkX2AIsyxV+Ky4AVXUSOPV1E4eA/Sv9\n6yaSfAX4T+AtSQ4n2THuOY3ApcCHGPxW+GD7uWrck1pm64C7kzzE4Bed6arq5tLIjqwFfpDkR8B9\nwLer6jvL8UQr7jJQSdJwVtwrAEnScAyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXqfwHd\n/gESZomJ0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "AEYcIMeqd4i1",
        "colab_type": "code",
        "outputId": "21a9158e-9f58-421c-86b2-6b6100aae05b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(test_three_label)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1000.,    0.,    0.,    0.,    0.,    0., 1000.,    0.,    0.,\n",
              "        1000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qqpuXRnZkDfBwkm8BXwe+VFVfXo4TrbiXgUqSTs2KewYgSTo1BkCSOmUAJKlTBkCSOmUA\nJKlTBkCSOmUAJKlTBkCSOvW/lK9vO4tP9cMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "z4n_WiMNd_Hf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_three_data, train_three_label = shuffle(train_three_data, train_three_label )\n",
        "test_three_data,test_three_label = shuffle(test_three_data,test_three_label )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VT_AKqQmeBk_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "le.fit_transform(train_three_label)\n",
        "train_three_label_cat = le.transform(train_three_label)\n",
        "test_three_label_cat = le.transform(test_three_label)\n",
        "train_three_label_one_hot = to_categorical(train_three_label_cat)\n",
        "test_three_label_one_hot = to_categorical(test_three_label_cat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rWEjK_exeEaJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# split train data and to create validation data\n",
        "train_X,valid_X,train_ground,valid_ground = train_test_split(train_three_data,\n",
        "                                                             train_three_label_one_hot,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mOFKlZWKeH25",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ssi32yj-d1iX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Define the model"
      ]
    },
    {
      "metadata": {
        "id": "uUISHV2weN0t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x, y = 32, 32\n",
        "inChannel = 3\n",
        "input_img = Input(shape = (x, y, inChannel))\n",
        "ep = 10 \n",
        "weight_decay = 1e-4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ich0RbYnebwW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def cifar10Classifier(input_img):\n",
        "  \n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  conv1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1)\n",
        "  conv1 = BatchNormalization()(conv1)\n",
        "  pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "  drop1= Dropout(.2)(pool1)\n",
        "\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop1)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  conv2 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv2)\n",
        "  conv2 = BatchNormalization()(conv2)\n",
        "  pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #14 x 14 x 32\n",
        "  drop2= Dropout(.3)(pool2)\n",
        "\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  conv3 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3)\n",
        "  conv3 = BatchNormalization()(conv3)\n",
        "  pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) #14 x 14 x 32\n",
        "  drop3= Dropout(.4)(pool3)\n",
        "\n",
        "  flat = Flatten()(drop3)\n",
        "  out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "U_0LHS-Xf7aN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Define functions for calculation of DI score\n",
        "getDIScoreOfLayer(layer_output, true_labels), \n",
        "getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels)\n"
      ]
    },
    {
      "metadata": {
        "id": "xsLk-f7sf6zd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def getDIScoreOfLayer(layer_output, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  rho = 1e-6\n",
        "  mu = layer_output.mean(axis=0)\n",
        "#   print(mu.shape)\n",
        "  mu = mu.reshape((1,-1))\n",
        "  centered_data = layer_output - mu\n",
        "  S_bar = np.matmul(centered_data.T,centered_data)\n",
        "  # compute between class matrix\n",
        "  total_classes = true_labels.max()\n",
        "  S_B = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  S_W = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  unique_classes = np.unique(true_labels)\n",
        "  unique_classes_list = np.ndarray.tolist(unique_classes)\n",
        "  for l in unique_classes_list:\n",
        "    class_l_index = np.where(true_labels == l)\n",
        "    class_l_output = layer_output[class_l_index[0],:]\n",
        "    mu_l = class_l_output.mean(axis=0)\n",
        "    mu_l = mu_l.reshape((1,-1))\n",
        "    total_examples_this_class = class_l_index[0].shape   \n",
        "    ss = mu_l - mu\n",
        "    S_B = S_B + total_examples_this_class*np.matmul((mu_l - mu).T,(mu_l - mu))\n",
        "\n",
        "  DI_Score = np.trace(np.matmul(np.linalg.inv(S_bar + rho*np.eye(S_bar.shape[0])),S_B))\n",
        "  return DI_Score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ubrpaIpFgBzn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  number_of_filters = cnn_outputs.shape[3]\n",
        "  cnn_di_scores = []\n",
        "  for filt in range(0,number_of_filters):\n",
        "    outputs_filter = cnn_outputs[:,:,:,filt]\n",
        "    outputs_filter_reshaped = np.reshape(outputs_filter, newshape=(cnn_outputs.shape[0],-1))\n",
        "    this_filter_score = getDIScoreOfLayer(outputs_filter_reshaped, true_labels)\n",
        "    cnn_di_scores.append(this_filter_score)\n",
        "  return cnn_di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MQ5H0p_KjVCw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## define a function to get and plot di score of a layer. It takes inputs - trained model, layer name to get DI scores and returns a plot with some statistics"
      ]
    },
    {
      "metadata": {
        "id": "o9MHI1LFjUZs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def createCNNLayerDIScorePlot(trained_model, layer_name, data_to_use = train_three_data , data_label =train_three_label ):\n",
        "  get_cnn1_output_function = K.function([trained_model.layers[0].input],\n",
        "                                  [trained_model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "  cnn1_output = get_cnn1_output_function([data_to_use])[0]\n",
        "  di_scores = getDIScoreOfLayerCNNLayer(cnn1_output, data_label)\n",
        "  # plot DI score\n",
        "  y_pos  = np.arange(len(di_scores))\n",
        "  plt.bar(y_pos, di_scores, align='center', alpha=0.5)\n",
        "  plt.xlabel('FilterNumber')\n",
        "  plt.ylabel('DI Score')\n",
        "\n",
        "  print('Mean of DI SCore: ',np.mean(di_scores))\n",
        "  print('STD of DI Score: ', np.std(di_scores))\n",
        "  print('MAX of DI Score: ', np.max(di_scores))\n",
        "  plt.title('DI score - Data - D_L, Network -N_L, Conv_6')\n",
        "#   plt.ylim([0,1])\n",
        "  return di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DCCl7-vOfSZ9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## load pretrained model"
      ]
    },
    {
      "metadata": {
        "id": "jqIkmDGkfRtD",
        "colab_type": "code",
        "outputId": "273aab52-c7d5-4b31-c864-6f370b16038e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "cell_type": "code",
      "source": [
        "loaded_model_low_accuracy = keras.models.load_model('lowAccuracyModelData.h5')\n",
        "loaded_model_high_accuracy = keras.models.load_model('highAccuracyModelData.h5')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "z7J98O5qfi8Z",
        "colab_type": "code",
        "outputId": "7a58e276-0408-447d-9ec8-c36960d7296d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "loaded_model_low_accuracy.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.30999894126256305, 0.8993333333333333]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "tzbIXvcEoNMj",
        "colab_type": "code",
        "outputId": "51518cf3-96e8-4646-ae3b-0f2c524dc4a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        }
      },
      "cell_type": "code",
      "source": [
        "loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UijDrRL2khSY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create a plot for DI score using function createCNNLayerDIScorePlot()"
      ]
    },
    {
      "metadata": {
        "id": "Lyk2-GAGaPnV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### On train data"
      ]
    },
    {
      "metadata": {
        "id": "zQYh3TElhl5R",
        "colab_type": "code",
        "outputId": "cdfc69ca-5dd8-4ea8-b244-0c93e0cf901d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "cell_type": "code",
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_2'\n",
        "discores_low_acc_model = createCNNLayerDIScorePlot(loaded_model_low_accuracy, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5925383901075372\n",
            "STD of DI Score:  0.05669853165952613\n",
            "MAX of DI Score:  0.6699778349643595\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WA4sr1p1cuP854HNlxmBmZvVrdkezmZm1ECcFMzPLOSmYmVnOScHMzHJOCmZmlvPU2WbD\nyFNP22jnmoKZmeWcFMzMLOekYGZmOScFMzPLuaPZzMadWgMCxvtgANcUzMws56RgZmY5JwUzM8s5\nKZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmubqSgqQtJL227GDMzKy5BkwKkg4G\nlgK/SMt7Seqs5+CSZkpaKalb0ok1yn1AUkjqqDdwMzMbfvXUFE4BZgCPA0TEUmDqQDtJagPOAQ4C\npgNzJU3vp9zWwLHATXVHbWZmpagnKTwfEU9UrIs69psBdEfEqoh4DlgEzO6n3H8A/wX8tY5jmplZ\niepJCsskHQa0SZom6VvADXXsNxFYXVjuSetykvYBJkfEz2odSNI8SV2Sunp7e+t4aDMzG4x6ksIx\nwB7As8CFwBPAp4b6wJI2Ac4EPjNQ2Yg4NyI6IqKjvb19qA9tZmZV1PyRndQvsCAijgdOavDYa4DJ\nheVJaV2frYHXA9dKAngV0ClpVkR0NfhYZmY2DGrWFCLiBWC/QR57CTBN0lRJmwFzgHzUUkQ8ERE7\nRMSUiJgC3Ag4IZiZNVE9P8d5axqCeinwdN/KiLii1k4RsV7SfOBqoA1YGBHLJC0AuiKirmGtZmY2\ncupJCpsDjwDvKqwLoGZSAIiIxcDiinUnVym7fx2xmJlZiQZMChHxsZEIxMzMmq+ebzRPkvQTSQ+n\n2+WSJo1EcGZmNrLqGZJ6HlkH8avT7cq0zszMxph6kkJ7RJwXEevT7XzAXxYwMxuD6kkKj0g6XFJb\nuh1O1vFsZmZjTD1J4V+AQ4EHgQeAQwB3PpuZjUH1jD66D5g1ArGYmVmT1TP66AJJ2xaWt5O0sNyw\nzMysGeppPtozIh7vW4iIx4C9ywvJzMyapZ6ksImk7foWJG1Pfd+ENjOzUaaeD/evA3+QdCkgso7m\nL5calZmZNUU9Hc0/kNRFNvdRAP8cEctLj8zMzEZc1eYjSS+VtClASgLXAJsBrxuh2MzMbITV6lP4\nBTAFQNKuwB+A1wBHSzqt/NDMzGyk1UoK20XEn9L9I4CLIuIY4CDgfaVHZmZmI65WUojC/XeRNR8R\nEc8BG8oMyszMmqNWR/Ptkr5G9rvKuwK/BCh+kc3MzMaWWjWFTwJryfoV3hMRf0nrpwNfKzkuMzNr\ngqo1hYh4BnhRh3JE3ADcUGZQZmbWHPV8o9nMzMYJJwUzM8s5KZiZWa5qn4KkK9l4WOpGImLA31iQ\nNBP4BtAGfC8iTqvYfhRwNPACsA6Y5yk0zMyap9aQ1CGNMJLUBpwDHAj0AEskdVZ86F8YEd9J5WcB\nZwIzh/K4ZmY2eLVGH13Xd19Se1rX28CxZwDdEbEqHWMRMBvIk0JEPFkovyU1aiZmZla+mn0Kkr4k\naS2wErhLUq+kk+s89kRgdWG5J62rfIyjJd0NnA78W5U45knqktTV29tIXjIzs0bUmiX1OGA/4M0R\nsX1EbAe8BXibpE8PVwARcU5E7AJ8FvhClTLnRkRHRHS0t7cP10ObmVmFWjWFjwBzI+KevhWpKehw\n4KN1HHsNMLmwPCmtq2YR8P46jmtmZiWplRQ2jYi1lStTv8KmdRx7CTBN0lRJmwFzgM5iAUnTCovv\nA/6EmZk1Ta3RR88NchsAEbFe0nzgarIhqQsjYpmkBUBXRHQC8yUdADwPPEY2RbeZmTVJraTwRklP\n9rNewOb1HDwiFgOLK9adXLh/bD3HMTOzkVFrSGrbSAZiZmbN52kuzMws56RgZmY5JwUzM8s5KZiZ\nWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVnOScHMzHJOCmZmlnNS\nMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxypSYFSTMlrZTULenEfrYfJ2m5pNsl\n/UrSzmXGY2ZmtZWWFCS1AecABwHTgbmSplcUuxXoiIg9gcuA08uKx8zMBlZmTWEG0B0RqyLiOWAR\nMLtYICJ+ExF/SYs3ApNKjMfMzAZQZlKYCKwuLPekddV8HPh5fxskzZPUJamrt7d3GEM0M7Oiluho\nlnQ40AGc0d/2iDg3IjoioqO9vX1kgzMzG0cmlHjsNcDkwvKktG4jkg4ATgLeGRHPlhiPmZkNoMya\nwhJgmqSpkjYD5gCdxQKS9ga+C8yKiIdLjMXMzOpQWlKIiPXAfOBqYAVwSUQsk7RA0qxU7AxgK+BS\nSUsldVY5nJmZjYAym4+IiMXA4op1JxfuH1Dm45uZWWNaoqPZzMxag5OCmZnlnBTMzCznpGBmZjkn\nBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczM\nck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmYmVmu1KQgaaaklZK6JZ3Yz/Z3SLpF\n0npJh5QZi5mZDay0pCCpDTgHOAiYDsyVNL2i2J+BI4ELy4rDzMzqN6HEY88AuiNiFYCkRcBsYHlf\ngYi4N23bUGIcZmZWpzKbjyYCqwvLPWldwyTNk9Qlqau3t3dYgjMzsxcbFR3NEXFuRHREREd7e3uz\nwzEzG7PKTAprgMmF5UlpnZmZtagyk8ISYJqkqZI2A+YAnSU+npmZDVFpSSEi1gPzgauBFcAlEbFM\n0gJJswAkvVlSD/BB4LuSlpUVj5mZDazM0UdExGJgccW6kwv3l5A1K5mZWQsYFR3NZmY2MpwUzMws\n56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeWcFMzMLOekYGZmOScFMzPLOSmY\nmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxypSYFSTMl\nrZTULenEfra/RNLFaftNkqaUGY+ZmdVWWlKQ1AacAxwETAfmSppeUezjwGMRsStwFvBfZcVjZmYD\nK7OmMAPojohVEfEcsAiYXVFmNnBBun8Z8G5JKjEmMzOrQRFRzoGlQ4CZEfGJtPwR4C0RMb9Q5s5U\npict353KrK041jxgXlp8LbBymMLcAVg7YKnW5fiby/E3z2iOHZoT/84R0T5QoQkjEclQRcS5wLnD\nfVxJXRHRMdzHHSmOv7kcf/OM5tihteMvs/loDTC5sDwpreu3jKQJwDbAIyXGZGZmNZSZFJYA0yRN\nlbQZMAforCjTCRyR7h8C/DrKas8yM7MBldZ8FBHrJc0HrgbagIURsUzSAqArIjqB7wM/lNQNPEqW\nOEbSsDdJjTDH31yOv3lGc+zQwvGX1tFsZmajj7/RbGZmOScFMzPLjdukMNAUHK1O0r2S7pC0VFJX\ns+MZiKSFkh5O303pW7e9pGsk/Sn93a6ZMVZTJfZTJK1J53+ppH9oZoy1SJos6TeSlktaJunYtH60\nnP9q8Y+K10DS5pL+KOm2FP+paf3UNL1Pd5ruZ7NmxwrjtE8hTcFxF3Ag0EM2UmpuRCxvamANkHQv\n0FH5Rb9WJekdwDrgBxHx+rTudODRiDgtJebtIuKzzYyzP1ViPwVYFxFfa2Zs9ZC0I7BjRNwiaWvg\nZuD9wJGMjvNfLf5DGQWvQZqlYcuIWCdpU+B3wLHAccAVEbFI0neA2yLi282MFcZvTaGeKThsGEXE\nb8lGmBUVpzm5gOwfveVUiX3UiIgHIuKWdP8pYAUwkdFz/qvFPypEZl1a3DTdAngX2fQ+0ELnf7wm\nhYnA6sJyD6PoTZYE8EtJN6dpQEajV0bEA+n+g8ArmxnMIMyXdHtqXmrJppdKaSbivYGbGIXnvyJ+\nGCWvgaQ2SUuBh4FrgLuBxyNifSrSMp9B4zUpjAX7RcQ+ZLPQHp2aOEat9KXF0dSW+W1gF2Av4AHg\n680NZ2CStgIuBz4VEU8Wt42G899P/KPmNYiIFyJiL7KZHWYAr2tySFWN16RQzxQcLS0i1qS/DwM/\nIXujjTYPpfbivnbjh5scT90i4qH0j74B+B9a/PyntuzLgR9HxBVp9ag5//3FP9peA4CIeBz4DfBW\nYNs0vQ+00GfQeE0K9UzB0bIkbZk63JC0JfAe4M7ae7Wk4jQnRwD/28RYGtL3YZr8Ey18/lNH5/eB\nFRFxZmHTqDj/1eIfLa+BpHZJ26b7W5ANcFlBlhwOScVa5vyPy9FHAGn42v/lb1NwfLnJIdVN0mvI\nageQTVVyYavHL+kiYH+yKYMfAr4E/BS4BNgJuA84NCJarkO3Suz7kzVbBHAv8K+F9vmWImk/4Hrg\nDmBDWv15snb50XD+q8U/l1HwGkjak6wjuY3sQvySiFiQ/o8XAdsDtwKHR8SzzYs0M26TgpmZvdh4\nbT4yM7N+OCmYmVnOScHMzHJOCmZmlnNSMDOznJOCjSmSXijMmrlU0hRJHZK+mbYfKensdP/9kqYP\n4jGOlLQhDTXsW3dnmoJhOJ7DuoFLmZWjtJ/jNGuSZ9J0AkX3Av1NL/5+4Cqg7tlxC99A7QFOAj40\niBhLI2lCYT4ds4a5pmBjnqT9JV1VsW5fYBZwRqpR7JJuv0iTDF4v6XWp7PmSviPpJuD0dIirgD0k\nvbafx1tXuH+IpPMLx/m2pBslrUpxLZS0oq9MYb+z0tz7v5LUntY1Ep/ZoDgp2FizRaHp6CfVCkXE\nDWTTPJwQEXtFxN1kP6Z+TES8CTge+O/CLpOAfSPiuLS8gewD+PMNxrcd2bw3n06PfxawB/AGSX01\nnC2BrojYA7iO7BvUNBif2aC4+cjGmv6ajwaUZuDcF7g0m2oHgJcUilwaES9U7HYhcJKkqQ081JUR\nEZLuAB6KiDvS4y8DpgBLyRLOxan8j4ArBhmfWcOcFMwym5DNb18toTxduSIi1kv6OlD5a2XFuWM2\nr9jWN7fNhsL9vuVq/48xmPjMBsPNRzaePQVsDZDm579H0gchm5lT0hvrOMb5wAFAe2HdQ5J2l7QJ\n2eydjdqEv82eeRjwuyHEZ9YQJwUbzxYBJ0i6VdIuwIeBj0u6DVhGHT/Rmn7O9ZvAKwqrTyTriL6B\n7MdfGvU0MEPSnWQ/2bggrW84PrNGeZZUMzPLuaZgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1Iw\nM7Ock4KZmeX+P4nDxQgNu6AkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7B0fFH7NiOh_",
        "colab_type": "code",
        "outputId": "8dc90bca-49f1-4ec0-da22-08a2d217b156",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "cell_type": "code",
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_2'\n",
        "\n",
        "discores_high_acc_model = createCNNLayerDIScorePlot(loaded_model_high_accuracy, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.6055429558343656\n",
            "STD of DI Score:  0.04535125694283665\n",
            "MAX of DI Score:  0.7097912715870862\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tJcZjZmZ1lJkUFgKTJE2UtBkwA+gqFpC0Y2FyGrC0xHjMzKyO0q4+ioh1kmYD15Bdknpe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\nBrbWjQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "EHVud4ykeRn8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Pick top 10 DI scores"
      ]
    },
    {
      "metadata": {
        "id": "8k5Ob6gWeQ94",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TyI8gOZuftmZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TjMoET8nasck",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### On validation data"
      ]
    },
    {
      "metadata": {
        "id": "cPKQgmyXbACY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "valid_ground_actual = np.argmax(valid_ground, axis=1)\n",
        "# valid_ground_actual.shape\n",
        "# data_to_use = train_three_data , data_label =train_three_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nbg5-HKhaxxj",
        "colab_type": "code",
        "outputId": "0cefd3b7-fd95-40a5-8ca7-3ab339c9a769",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "cell_type": "code",
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_2'\n",
        "discores_low_acc_model_valid = createCNNLayerDIScorePlot(loaded_model_low_accuracy,\n",
        "                                                   layer_name, data_to_use=valid_X, data_label=valid_ground_actual)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  1.0283991648798934\n",
            "STD of DI Score:  0.04766352471097197\n",
            "MAX of DI Score:  1.102371604561001\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zaH4FbSFaxuc",
        "colab_type": "code",
        "outputId": "920ee807-4e04-4697-c9e4-18f57a228a60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "cell_type": "code",
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_2'\n",
        "\n",
        "discores_high_acc_model_valid = createCNNLayerDIScorePlot(loaded_model_high_accuracy,\n",
        "                                                   layer_name, data_to_use=valid_X, data_label=valid_ground_actual)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  1.0489336398135918\n",
            "STD of DI Score:  0.03772804064192905\n",
            "MAX of DI Score:  1.1368837585485827\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vMpuiVdSf60g",
        "colab_type": "code",
        "outputId": "bc786a18-9a97-4dee-b40a-8ab13619d495",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "np.sort(discores_high_acc_model)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.50778735, 0.51148141, 0.5316608 , 0.5503721 , 0.55231392,\n",
              "       0.55683133, 0.5754899 , 0.57891548, 0.58448689, 0.58936178,\n",
              "       0.58980302, 0.59096041, 0.59923165, 0.60396759, 0.6067217 ,\n",
              "       0.60815117, 0.60818232, 0.60875793, 0.61246232, 0.61911506,\n",
              "       0.62163669, 0.62485589, 0.62771089, 0.63252923, 0.63514822,\n",
              "       0.63617041, 0.64203204, 0.65381118, 0.65792124, 0.66861217,\n",
              "       0.68109371, 0.70979014])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "UzN_W73aldFU",
        "colab_type": "code",
        "outputId": "9fd8629c-e026-4579-9740-f2b062ff6c1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# (discores_high_acc_model)\n",
        "K = 10 # how many channels to pick\n",
        "high_acc_di_sorted = np.argsort(discores_high_acc_model)\n",
        "high_acc_di_sorted_list = np.ndarray.tolist(high_acc_di_sorted)\n",
        "top_channels = high_acc_di_sorted_list[-K:]\n",
        "top_channels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 25, 19, 29, 5, 7, 26, 28, 17, 23]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "SD8nswMnxWVC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "total_channels = len(discores_high_acc_model)\n",
        "# total_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MsCtxzRVokuR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "disable_training = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fJn40KQs_BnW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# # returns \n",
        "# def highAccuracyModelTillConv2(input_img): \n",
        "#   conv1_1 = (Conv2D(32, (3,3), padding='same',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), \n",
        "#                     input_shape=x_train.shape[1:], activation='elu', trainable = disable_training,\n",
        "#                     name = 'HighAccuracyConv1'))(input_img)\n",
        "#   conv1_2 = BatchNormalization(name = 'HighAccuracyBN1',\n",
        "#                               trainable = disable_training)(conv1_1)\n",
        "#   conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',trainable = disable_training,\n",
        "#                     name = 'HighAccuracyConv2',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\n",
        "# #   conv2_2 = BatchNormalization(name = 'HighAccuracyBN2',\n",
        "# #                               trainable = disable_training)(conv2_1)  \n",
        "# #   create a mask\n",
        "#   # first swap axis and make channels the 0th dimension\n",
        "# #   swapped_conv2 = keras.backend.permute_dimensions(conv2_1, (3,1,2,0))\n",
        "#   masked = []\n",
        "#   for ch in range(0,total_channels):\n",
        "#     if ch in top_channels:\n",
        "#       m = False\n",
        "#     else:\n",
        "#       m = True \n",
        "#     masked.append(m)\n",
        "#   corrected_conv2 = tf.boolean_mask(conv2_1,mask = masked, axis=3)\n",
        "#   print(corrected_conv2.shape)\n",
        "# #   input_shape = swapped_conv2.get_shape().as_list()\n",
        "# #   print(swapped_conv2.shape)\n",
        "# #   masked_swapped_conv2 = tf.boolean_mask(swapped_conv2,mask = masked)\n",
        "# #   print(masked_swapped_conv2.shape)\n",
        "# #   corrected_conv2 = keras.backend.permute_dimensions(masked_swapped_conv2, (3,1,2,0))\n",
        "# #   print(masked) Masking(mask_value=0., input_shape=(5, 100))\n",
        "# #   swapped_conv2_masked = Lambda(lambda x: keras.backend.boolean_mask(swapped_conv2, mask = 3))(out)\n",
        "# #   masked_swapped_conv2 = keras.backend.manual_variable_initiali\n",
        "#   return corrected_conv2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Sk_0a6JlFux5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# returns \n",
        "def highAccuracyModelTillConv2(input_img): \n",
        "  conv1_1 = (Conv2D(32, (3,3), padding='same',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), \n",
        "                    input_shape=x_train.shape[1:], activation='elu', trainable = disable_training,\n",
        "                    name = 'HighAccuracyConv1'))(input_img)\n",
        "  conv1_2 = BatchNormalization(name = 'HighAccuracyBN1',\n",
        "                              trainable = disable_training)(conv1_1)\n",
        "  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',trainable = disable_training,\n",
        "                    name = 'HighAccuracyConv2',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\n",
        "#   conv2_2 = BatchNormalization(name = 'HighAccuracyBN2',\n",
        "#                               trainable = disable_training)(conv2_1)  \n",
        "  \n",
        "  return conv2_1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OA-0pVGXvMeE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense, Concatenate, Input, Lambda\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "41smOd7GTX5c",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "def cifar10ClassifierTransfer(input_img, conv2_high):\n",
        "  \n",
        "  # Add additional inputs to \n",
        "  \n",
        "  conv1_1 = (Conv2D(32, (3,3), padding='same',name = 'lowAccuracyModelConv1_1',\n",
        "                    trainable = disable_training, kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "  conv1_2 = BatchNormalization(name = 'lowAccuracyModelBN1',\n",
        "                              trainable = disable_training)(conv1_1)\n",
        "  conv2_1 = (Conv2D(32, (3,3), padding='same',name = 'lowAccuracyModelConv2_1',\n",
        "                    trainable = disable_training, activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\n",
        "  channels = top_channels\n",
        "  branch_outputs = []\n",
        "#   orig_channel = conv2_1\n",
        "  # create a mask here\n",
        "  branch_outputs.append(conv2_1)\n",
        "  for i in channels:\n",
        "      # Slicing the ith channel:\n",
        "      out = Lambda(lambda x: x[:,:,:, i])(conv2_high)      \n",
        "      # Setting up your per-channel layers (replace with actual sub-models):\n",
        "      layer_out = Lambda(lambda x: keras.backend.expand_dims(x, axis = 3))(out)\n",
        "      branch_outputs.append(layer_out)\n",
        "  merged = Concatenate()(branch_outputs)\n",
        "\n",
        "\n",
        "  conv2_2 = BatchNormalization()(merged)\n",
        "  pool2_3 = MaxPooling2D(pool_size=(2, 2))(conv2_2) #14 x 14 x 32\n",
        "  drop2_4= Dropout(.2)(pool2_3)\n",
        "\n",
        "  conv3_1 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2_4)\n",
        "  conv3_2 = BatchNormalization()(conv3_1)\n",
        "  conv4_1 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "  conv4_2 = BatchNormalization()(conv4_1)\n",
        "  pool4_3 = MaxPooling2D(pool_size=(2, 2))(conv4_2) #14 x 14 x 32\n",
        "  drop4_4= Dropout(.3)(pool4_3)\n",
        "\n",
        "  conv5_1 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop4_4)\n",
        "  conv5_2 = BatchNormalization()(conv5_1)\n",
        "  conv6_1 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "  conv6_2 = BatchNormalization()(conv6_1)\n",
        "  pool6_3 = MaxPooling2D(pool_size=(2, 2))(conv6_2) #14 x 14 x 32\n",
        "  drop6_4= Dropout(.4)(pool6_3)\n",
        "\n",
        "  flat = Flatten()(drop6_4)\n",
        "  out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sf3PN5YIyba2",
        "colab_type": "code",
        "outputId": "8b62100d-8e6a-4584-85a6-9e0ac6c18781",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "top_channels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 25, 19, 29, 5, 7, 26, 28, 17, 23]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "fzUmKtVGFusU",
        "colab_type": "code",
        "outputId": "20bd8f94-841d-41fb-e48e-440d57b3dd25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1836
        }
      },
      "cell_type": "code",
      "source": [
        "high_acc_output = highAccuracyModelTillConv2(input_img)\n",
        "modelFilt = Model(input_img, \n",
        "              cifar10ClassifierTransfer(input_img,\n",
        "              high_acc_output))\n",
        "#data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "datagen.fit(train_three_data)\n",
        " \n",
        "#training\n",
        "batch_size = 64\n",
        " \n",
        "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "# model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "modelFilt.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "modelFilt.summary()\n",
        " "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "HighAccuracyConv1 (Conv2D)      (None, 32, 32, 32)   896         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "HighAccuracyBN1 (BatchNormaliza (None, 32, 32, 32)   128         HighAccuracyConv1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lowAccuracyModelConv1_1 (Conv2D (None, 32, 32, 32)   896         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "HighAccuracyConv2 (Conv2D)      (None, 32, 32, 32)   9248        HighAccuracyBN1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "lowAccuracyModelBN1 (BatchNorma (None, 32, 32, 32)   128         lowAccuracyModelConv1_1[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_1 (Lambda)               (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_3 (Lambda)               (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_5 (Lambda)               (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_7 (Lambda)               (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_9 (Lambda)               (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_11 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_13 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_15 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_17 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_19 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lowAccuracyModelConv2_1 (Conv2D (None, 32, 32, 32)   9248        lowAccuracyModelBN1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "lambda_2 (Lambda)               (None, 32, 32, 1)    0           lambda_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "lambda_4 (Lambda)               (None, 32, 32, 1)    0           lambda_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "lambda_6 (Lambda)               (None, 32, 32, 1)    0           lambda_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "lambda_8 (Lambda)               (None, 32, 32, 1)    0           lambda_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "lambda_10 (Lambda)              (None, 32, 32, 1)    0           lambda_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "lambda_12 (Lambda)              (None, 32, 32, 1)    0           lambda_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_14 (Lambda)              (None, 32, 32, 1)    0           lambda_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_16 (Lambda)              (None, 32, 32, 1)    0           lambda_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_18 (Lambda)              (None, 32, 32, 1)    0           lambda_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_20 (Lambda)              (None, 32, 32, 1)    0           lambda_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 32, 32, 42)   0           lowAccuracyModelConv2_1[0][0]    \n",
            "                                                                 lambda_2[0][0]                   \n",
            "                                                                 lambda_4[0][0]                   \n",
            "                                                                 lambda_6[0][0]                   \n",
            "                                                                 lambda_8[0][0]                   \n",
            "                                                                 lambda_10[0][0]                  \n",
            "                                                                 lambda_12[0][0]                  \n",
            "                                                                 lambda_14[0][0]                  \n",
            "                                                                 lambda_16[0][0]                  \n",
            "                                                                 lambda_18[0][0]                  \n",
            "                                                                 lambda_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 32, 32, 42)   168         concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 42)   0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 16, 16, 42)   0           max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 16, 16, 64)   24256       dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 16, 16, 64)   256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 16, 16, 64)   36928       batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 16, 16, 64)   256         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 8, 8, 64)     0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 8, 8, 64)     0           max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 8, 8, 128)    73856       dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 8, 8, 128)    512         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 8, 8, 128)    147584      batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 8, 8, 128)    512         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 4, 4, 128)    0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 4, 4, 128)    0           max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 2048)         0           dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 3)            6147        flatten_1[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 311,019\n",
            "Trainable params: 310,039\n",
            "Non-trainable params: 980\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WE7b72OOFups",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "plot_model(modelFilt, to_file=\"FiltModel.png\", show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_lY9fuSspeIA",
        "colab_type": "code",
        "outputId": "4e9be6df-9c65-4b31-ca6a-f1aa297481d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "cell_type": "code",
      "source": [
        "# set weights again\n",
        "loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5-yqdp6_rQkI",
        "colab_type": "code",
        "outputId": "7d39ef38-bd51-4df5-981c-7501dee0d542",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "cell_type": "code",
      "source": [
        "loaded_model_high_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8GkUnIIAqH0k",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## set predetermined weights"
      ]
    },
    {
      "metadata": {
        "id": "nKVw7k8QLwoQ",
        "colab_type": "code",
        "outputId": "bad7ac46-dbff-456f-be3e-0b1caedcadff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "top_channels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 25, 19, 29, 5, 7, 26, 28, 17, 23]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "5hWcRyTNtaXW",
        "colab_type": "code",
        "outputId": "4e8c506d-9171-40bb-d5fe-d74180105d98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "top_channels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 25, 19, 29, 5, 7, 26, 28, 17, 23]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "t0FN66utr5VE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# set weights of high accuracy model for channels not used to 0 [9, 25, 19, 29, 5, 7, 26, 28, 17, 23]\n",
        "high_acc_conv2_weights = loaded_model_high_accuracy.get_layer('conv2d_2').get_weights()\n",
        "temp_weights = np.zeros_like(high_acc_conv2_weights[0])\n",
        "h,w,d,n = high_acc_conv2_weights[0].shape\n",
        "for ch in range(0,n):\n",
        "  if ch in top_channels:\n",
        "    temp_weights[:,:,:,ch] = high_acc_conv2_weights[0][:,:,:,ch]\n",
        "    \n",
        "    \n",
        "  \n",
        "# (high_acc_conv2_weights[0].shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Esv5wQmYFby6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# temp_bias = np.zeros_like(high_acc_conv2_weights[1])\n",
        "temp_bias = high_acc_conv2_weights[1]\n",
        "\n",
        "bias_shape = high_acc_conv2_weights[1].shape\n",
        "for ch in range(0,n):\n",
        "  if ch in top_channels:\n",
        "    temp_bias[ch] = high_acc_conv2_weights[1][ch]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HPV28w1jFulD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# temp_bias\n",
        "weights_to_assign = [temp_weights,temp_bias]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2pYigm1qFJmK",
        "colab_type": "code",
        "outputId": "f9846b86-e179-412c-defc-1817240915f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "temp_weights.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 3, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "metadata": {
        "id": "f1t2Yj3-E01G",
        "colab_type": "code",
        "outputId": "90a7abd4-0582-44a7-fce6-c9222c0fad6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "high_acc_conv2_weights[1].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "Q1qLv08GEv5Q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# temp_weights[:,:,:,11]\n",
        "# modelFilt.get_layer('lowAccuracyModelConv1_1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5AS3DZGUqNw1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "modelFilt.get_layer('HighAccuracyConv1').set_weights(loaded_model_high_accuracy.get_layer('conv2d_1').get_weights())\n",
        "modelFilt.get_layer('HighAccuracyBN1').set_weights(loaded_model_high_accuracy.get_layer('batch_normalization_1').get_weights())\n",
        "# modelFilt.get_layer('HighAccuracyConv2').set_weights(loaded_model_high_accuracy.get_layer('conv2d_2').get_weights())\n",
        "modelFilt.get_layer('HighAccuracyConv2').set_weights(weights_to_assign)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hQKKEkR2F-tx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "modelFilt.get_layer('lowAccuracyModelConv1_1').set_weights(loaded_model_low_accuracy.get_layer('conv2d_1').get_weights())\n",
        "modelFilt.get_layer('lowAccuracyModelBN1').set_weights(loaded_model_low_accuracy.get_layer('batch_normalization_1').get_weights())\n",
        "# modelFilt.get_layer('HighAccuracyConv2').set_weights(loaded_model_high_accuracy.get_layer('conv2d_2').get_weights())\n",
        "modelFilt.get_layer('lowAccuracyModelConv2_1').set_weights(loaded_model_low_accuracy.get_layer('conv2d_2').get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vwuN8V9wJy4_",
        "colab_type": "code",
        "outputId": "a98dc0db-ec02-4d3d-f011-7f0c62830db5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "modelFilt.layers[28]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.normalization.BatchNormalization at 0x7f410a81c400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "24vLwQTJqHgc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# modelFilt.layers[1].set_weights(loaded_model_low_accuracy.layers[1].get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Bn5PhIlAqd8k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# layer_output = model.get_layer('vgg16').get_layer('block3_conv1').output\n",
        "\n",
        "# 28\n",
        "# loaded_model_low_accuracy.layers[1].get_weights()\n",
        "# loaded_model_low_accuracy.layers[0].get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ASwQ6dQskQlv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# layer_dict_model_filt = dict([(layer.name, layer) for layer in modelFilt.layers])\n",
        "# layer_dict_model_filt['lowAccuracyModelConv1_1'].set_weights(loaded_model_low_accuracy['conv2d_1'].get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SPmn-PX7kYYa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# weights_model_filt[0].shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CxwLbMxCjrpy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# modelFilt.get_layer('HighAccuracyConv1').ge"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "59a_w_uXjkOl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# get_cnn1_output_function = K.function([trained_model.layers[0].input],\n",
        "#                                 [trained_model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "# cnn1_output = get_cnn1_output_function([data_to_use])[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M48EuB2YpeEq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# modelFilt.layers[1].set_weights(loaded_model_low_accuracy.layers[1].get_weights())\n",
        "# modelFilt.layers[3].set_weights(loaded_model_low_accuracy.layers[2].get_weights())\n",
        "# modelFilt.layers[5].set_weights(loaded_model_low_accuracy.layers[3].get_weights())\n",
        "\n",
        "\n",
        "# modelFilt.layers[2].set_weights(loaded_model_high_accuracy.layers[1].get_weights())\n",
        "# modelFilt.layers[4].set_weights(loaded_model_high_accuracy.layers[2].get_weights())\n",
        "# modelFilt.layers[6].set_weights(loaded_model_high_accuracy.layers[3].get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-2wj7j0_v8uR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# modelFilt.layers[2].set_weights(loaded_model_low_accuracy.layers[1].get_weights())\n",
        "# modelFilt.layers[4].set_weights(loaded_model_low_accuracy.layers[2].get_weights())\n",
        "# modelFilt.layers[6].set_weights(loaded_model_low_accuracy.layers[3].get_weights())\n",
        "\n",
        "\n",
        "# modelFilt.layers[1].set_weights(loaded_model_low_accuracy.layers[1].get_weights())\n",
        "# modelFilt.layers[3].set_weights(loaded_model_low_accuracy.layers[2].get_weights())\n",
        "# modelFilt.layers[5].set_weights(loaded_model_low_accuracy.layers[3].get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NrTPnsVBtX05",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# layer_dict_modelFilt['conv2d_1'].set_weights(loaded_model_low_accuracy.layers[1].get_weights())\n",
        "# layer_dict_modelFilt['batch_normalization_5'].set_weights(loaded_model_low_accuracy.layers[2].get_weights())\n",
        "# layer_dict_modelFilt['conv2d_2'].set_weights(loaded_model_low_accuracy.layers[3].get_weights())\n",
        "\n",
        "\n",
        "# layer_dict_modelFilt['HighAccuracyConv1'].set_weights(loaded_model_low_accuracy.layers[1].get_weights())\n",
        "# layer_dict_modelFilt['batch_normalization_3'].set_weights(loaded_model_low_accuracy.layers[2].get_weights())\n",
        "# layer_dict_modelFilt['HighAccuracyConv2'].set_weights(loaded_model_low_accuracy.layers[3].get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y3pdihEIpeB2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "a0b4AZgQFum1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# # set weights of some of the layers and make it non trainable!\n",
        "# layer_dict_low = dict([(layer.name, layer) for layer in loaded_model_low_accuracy.layers])\n",
        "# layer_dict_modelFilt = dict([(layer.name, layer) for layer in modelFilt.layers])\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "o-nabstBFukL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# weights_low_conv2d1 = layer_dict_low['conv2d_1'].get_weights()\n",
        "# layer_dict_modelFilt['conv2d_1'].set_weights(weights_low_conv2d1)\n",
        "\n",
        "# weights_low_bn1 = layer_dict_low['batch_normalization_1'].get_weights()\n",
        "# layer_dict_modelFilt['batch_normalization_5'].set_weights(weights_low_bn1)\n",
        "\n",
        "# weights_low_conv2d_2 = layer_dict_low['conv2d_2'].get_weights()\n",
        "# layer_dict_modelFilt['conv2d_2'].set_weights(weights_low_conv2d_2)\n",
        "\n",
        "# modelFilt.layers[1].set_weights(weights_low_conv2d1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7OoELuuikAn6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# modelFilt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wTCdHBwFZ4at",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# set weights of some of the layers and make it non trainable!\n",
        "# layer_dict_high= dict([(layer.name, layer) for layer in loaded_model_high_accuracy.layers])\n",
        "# layer_dict_modelFilt = dict([(layer.name, layer) for layer in modelFilt.layers])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UXkQ68OyZnI1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# weights_highconv2d1 = layer_dict_high['conv2d_1'].get_weights()\n",
        "# layer_dict_modelFilt['HighAccuracyConv1'].set_weights(weights_highconv2d1)\n",
        "\n",
        "# weights_high_bn1 = layer_dict_high['batch_normalization_1'].get_weights()\n",
        "# layer_dict_modelFilt['batch_normalization_3'].set_weights(weights_high_bn1)\n",
        "\n",
        "# weights_high_conv2d_2 = layer_dict_high['conv2d_2'].get_weights()\n",
        "# layer_dict_modelFilt['HighAccuracyConv2'].set_weights(weights_low_conv2d_2)\n",
        "# ### need to modify and zero put non redundant weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y781xnnuorld",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# weights_highconv2d1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "at5JqaEWojaV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# layer_dict_modelFilt['HighAccuracyConv1'].get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wm5P4A9EY0th",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Lm7AFEZibLP9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# modelFilt.layers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jKdEeJCdzLNt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# s = -1\n",
        "# for layer in modelFilt.layers[0:7]:\n",
        "#     s = s+1\n",
        "#     if (s%2) !=0:\n",
        "#       print(s)\n",
        "#       print(layer)\n",
        "#     layer.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Uk2JwzAGbCj0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# s = -1\n",
        "# for layer in modelFilt.layers[0:7]:\n",
        "#     s = s+1\n",
        "#     if (s%2) !=0:\n",
        "#       print(s)\n",
        "#       print(layer)\n",
        "#       layer.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "knA2xBI4jrZF",
        "colab_type": "code",
        "outputId": "2f9a6a66-f5fd-4b93-c6fa-27d843f2fcad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "cell_type": "code",
      "source": [
        "loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ji5jVmbej-LM",
        "colab_type": "code",
        "outputId": "398b4f7e-5e8e-428b-f4a2-a7e523bf15b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "loaded_model_low_accuracy.layers[5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.pooling.MaxPooling2D at 0x7fe00e1dd3c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "metadata": {
        "id": "0w2grlRlKQhJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for l1,l2 in zip(modelFilt.layers[32:],loaded_model_low_accuracy.layers[8:]):\n",
        "#     print (l1)\n",
        "    l1.set_weights(l2.get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7EozUm-dSIx2",
        "colab_type": "code",
        "outputId": "7c57be59-98bd-41eb-a94f-f98ad2a00e0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1836
        }
      },
      "cell_type": "code",
      "source": [
        "modelFilt.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "HighAccuracyConv1 (Conv2D)      (None, 32, 32, 32)   896         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "HighAccuracyBN1 (BatchNormaliza (None, 32, 32, 32)   128         HighAccuracyConv1[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lowAccuracyModelConv1_1 (Conv2D (None, 32, 32, 32)   896         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "HighAccuracyConv2 (Conv2D)      (None, 32, 32, 32)   9248        HighAccuracyBN1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "lowAccuracyModelBN1 (BatchNorma (None, 32, 32, 32)   128         lowAccuracyModelConv1_1[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_41 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_43 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_45 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_47 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_49 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_51 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_53 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_55 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_57 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lambda_59 (Lambda)              (None, 32, 32)       0           HighAccuracyConv2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "lowAccuracyModelConv2_1 (Conv2D (None, 32, 32, 32)   9248        lowAccuracyModelBN1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "lambda_42 (Lambda)              (None, 32, 32, 1)    0           lambda_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_44 (Lambda)              (None, 32, 32, 1)    0           lambda_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_46 (Lambda)              (None, 32, 32, 1)    0           lambda_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_48 (Lambda)              (None, 32, 32, 1)    0           lambda_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_50 (Lambda)              (None, 32, 32, 1)    0           lambda_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_52 (Lambda)              (None, 32, 32, 1)    0           lambda_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_54 (Lambda)              (None, 32, 32, 1)    0           lambda_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_56 (Lambda)              (None, 32, 32, 1)    0           lambda_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_58 (Lambda)              (None, 32, 32, 1)    0           lambda_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "lambda_60 (Lambda)              (None, 32, 32, 1)    0           lambda_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_9 (Concatenate)     (None, 32, 32, 42)   0           lowAccuracyModelConv2_1[0][0]    \n",
            "                                                                 lambda_42[0][0]                  \n",
            "                                                                 lambda_44[0][0]                  \n",
            "                                                                 lambda_46[0][0]                  \n",
            "                                                                 lambda_48[0][0]                  \n",
            "                                                                 lambda_50[0][0]                  \n",
            "                                                                 lambda_52[0][0]                  \n",
            "                                                                 lambda_54[0][0]                  \n",
            "                                                                 lambda_56[0][0]                  \n",
            "                                                                 lambda_58[0][0]                  \n",
            "                                                                 lambda_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 32, 32, 42)   168         concatenate_9[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling2D) (None, 16, 16, 42)   0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_13 (Dropout)            (None, 16, 16, 42)   0           max_pooling2d_13[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 16, 16, 64)   24256       dropout_13[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 16, 16, 64)   256         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 16, 16, 64)   36928       batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 16, 16, 64)   256         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling2D) (None, 8, 8, 64)     0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_14 (Dropout)            (None, 8, 8, 64)     0           max_pooling2d_14[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 8, 8, 128)    73856       dropout_14[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 8, 8, 128)    512         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 8, 8, 128)    147584      batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 8, 8, 128)    512         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling2D) (None, 4, 4, 128)    0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_15 (Dropout)            (None, 4, 4, 128)    0           max_pooling2d_15[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "flatten_5 (Flatten)             (None, 2048)         0           dropout_15[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 3)            6147        flatten_5[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 311,019\n",
            "Trainable params: 289,623\n",
            "Non-trainable params: 21,396\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fev8cENxbr-P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mcp_save = ModelCheckpoint('weights.hdf5', save_best_only=True, monitor='val_loss', mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0BTKJa7Zbwtc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.0001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    if epoch > 100:\n",
        "        lrate = 0.0003\n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sIOMXQsjdSNX",
        "colab_type": "code",
        "outputId": "22db3bbb-64d5-4763-e5be-e59ec3bc6a0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2074
        }
      },
      "cell_type": "code",
      "source": [
        "modelFilt.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=60,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/60\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.9114 - acc: 0.6484 - val_loss: 0.6826 - val_acc: 0.7380\n",
            "Epoch 2/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.6553 - acc: 0.7344 - val_loss: 0.5725 - val_acc: 0.7940\n",
            "Epoch 3/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.5570 - acc: 0.7949 - val_loss: 0.5038 - val_acc: 0.8223\n",
            "Epoch 4/60\n",
            "187/187 [==============================] - 6s 35ms/step - loss: 0.5020 - acc: 0.8178 - val_loss: 0.4261 - val_acc: 0.8480\n",
            "Epoch 5/60\n",
            "187/187 [==============================] - 7s 35ms/step - loss: 0.4710 - acc: 0.8310 - val_loss: 0.4268 - val_acc: 0.8487\n",
            "Epoch 6/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.4435 - acc: 0.8441 - val_loss: 0.4116 - val_acc: 0.8603\n",
            "Epoch 7/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.4352 - acc: 0.8492 - val_loss: 0.3998 - val_acc: 0.8603\n",
            "Epoch 8/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.4187 - acc: 0.8524 - val_loss: 0.3783 - val_acc: 0.8687\n",
            "Epoch 9/60\n",
            "187/187 [==============================] - 7s 36ms/step - loss: 0.4014 - acc: 0.8608 - val_loss: 0.3487 - val_acc: 0.8797\n",
            "Epoch 10/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.3985 - acc: 0.8622 - val_loss: 0.3476 - val_acc: 0.8803\n",
            "Epoch 11/60\n",
            "187/187 [==============================] - 7s 37ms/step - loss: 0.3941 - acc: 0.8641 - val_loss: 0.3598 - val_acc: 0.8743\n",
            "Epoch 12/60\n",
            "187/187 [==============================] - 8s 41ms/step - loss: 0.3851 - acc: 0.8693 - val_loss: 0.3393 - val_acc: 0.8860\n",
            "Epoch 13/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.3772 - acc: 0.8759 - val_loss: 0.3658 - val_acc: 0.8723\n",
            "Epoch 14/60\n",
            "187/187 [==============================] - 7s 37ms/step - loss: 0.3750 - acc: 0.8715 - val_loss: 0.3304 - val_acc: 0.8930\n",
            "Epoch 15/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3667 - acc: 0.8767 - val_loss: 0.3217 - val_acc: 0.8967\n",
            "Epoch 16/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3632 - acc: 0.8798 - val_loss: 0.3111 - val_acc: 0.8973\n",
            "Epoch 17/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3581 - acc: 0.8798 - val_loss: 0.3347 - val_acc: 0.8867\n",
            "Epoch 18/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3491 - acc: 0.8838 - val_loss: 0.3133 - val_acc: 0.8987\n",
            "Epoch 19/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3455 - acc: 0.8824 - val_loss: 0.3133 - val_acc: 0.9000\n",
            "Epoch 20/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3500 - acc: 0.8840 - val_loss: 0.3162 - val_acc: 0.8983\n",
            "Epoch 21/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3397 - acc: 0.8877 - val_loss: 0.3303 - val_acc: 0.8963\n",
            "Epoch 22/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3505 - acc: 0.8843 - val_loss: 0.3249 - val_acc: 0.8960\n",
            "Epoch 23/60\n",
            "187/187 [==============================] - 6s 35ms/step - loss: 0.3395 - acc: 0.8890 - val_loss: 0.3177 - val_acc: 0.8963\n",
            "Epoch 24/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.3348 - acc: 0.8880 - val_loss: 0.3075 - val_acc: 0.9010\n",
            "Epoch 25/60\n",
            "187/187 [==============================] - 7s 35ms/step - loss: 0.3306 - acc: 0.8906 - val_loss: 0.3251 - val_acc: 0.8893\n",
            "Epoch 26/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3299 - acc: 0.8926 - val_loss: 0.3032 - val_acc: 0.8990\n",
            "Epoch 27/60\n",
            "187/187 [==============================] - 7s 37ms/step - loss: 0.3303 - acc: 0.8920 - val_loss: 0.3068 - val_acc: 0.9010\n",
            "Epoch 28/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.3304 - acc: 0.8925 - val_loss: 0.3081 - val_acc: 0.9020\n",
            "Epoch 29/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3288 - acc: 0.8910 - val_loss: 0.3209 - val_acc: 0.8953\n",
            "Epoch 30/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3258 - acc: 0.8919 - val_loss: 0.3074 - val_acc: 0.8990\n",
            "Epoch 31/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3193 - acc: 0.8955 - val_loss: 0.3109 - val_acc: 0.9003\n",
            "Epoch 32/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3179 - acc: 0.8982 - val_loss: 0.3049 - val_acc: 0.9043\n",
            "Epoch 33/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3176 - acc: 0.8961 - val_loss: 0.3099 - val_acc: 0.8997\n",
            "Epoch 34/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3122 - acc: 0.8980 - val_loss: 0.3269 - val_acc: 0.8953\n",
            "Epoch 35/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3069 - acc: 0.9013 - val_loss: 0.3122 - val_acc: 0.8990\n",
            "Epoch 36/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.3078 - acc: 0.8985 - val_loss: 0.3037 - val_acc: 0.8997\n",
            "Epoch 37/60\n",
            "187/187 [==============================] - 7s 37ms/step - loss: 0.3104 - acc: 0.8981 - val_loss: 0.2990 - val_acc: 0.9050\n",
            "Epoch 38/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3097 - acc: 0.8976 - val_loss: 0.3103 - val_acc: 0.9003\n",
            "Epoch 39/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3086 - acc: 0.9023 - val_loss: 0.3159 - val_acc: 0.8960\n",
            "Epoch 40/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3014 - acc: 0.9028 - val_loss: 0.3222 - val_acc: 0.8897\n",
            "Epoch 41/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3055 - acc: 0.9021 - val_loss: 0.3181 - val_acc: 0.9030\n",
            "Epoch 42/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2941 - acc: 0.9057 - val_loss: 0.3311 - val_acc: 0.8933\n",
            "Epoch 43/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3021 - acc: 0.9009 - val_loss: 0.2999 - val_acc: 0.9037\n",
            "Epoch 44/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3101 - acc: 0.8991 - val_loss: 0.3228 - val_acc: 0.8973\n",
            "Epoch 45/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2985 - acc: 0.9046 - val_loss: 0.2989 - val_acc: 0.9083\n",
            "Epoch 46/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2966 - acc: 0.9052 - val_loss: 0.3127 - val_acc: 0.9003\n",
            "Epoch 47/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.3032 - acc: 0.9043 - val_loss: 0.2982 - val_acc: 0.9020\n",
            "Epoch 48/60\n",
            "187/187 [==============================] - 7s 36ms/step - loss: 0.2965 - acc: 0.9061 - val_loss: 0.3066 - val_acc: 0.9040\n",
            "Epoch 49/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.2980 - acc: 0.9041 - val_loss: 0.2994 - val_acc: 0.9010\n",
            "Epoch 50/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2929 - acc: 0.9109 - val_loss: 0.3008 - val_acc: 0.9057\n",
            "Epoch 51/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2924 - acc: 0.9082 - val_loss: 0.2997 - val_acc: 0.9007\n",
            "Epoch 52/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2788 - acc: 0.9134 - val_loss: 0.3065 - val_acc: 0.8977\n",
            "Epoch 53/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2833 - acc: 0.9096 - val_loss: 0.3056 - val_acc: 0.9007\n",
            "Epoch 54/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2919 - acc: 0.9084 - val_loss: 0.3124 - val_acc: 0.8967\n",
            "Epoch 55/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2890 - acc: 0.9091 - val_loss: 0.3033 - val_acc: 0.9027\n",
            "Epoch 56/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2898 - acc: 0.9098 - val_loss: 0.3151 - val_acc: 0.8963\n",
            "Epoch 57/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2863 - acc: 0.9100 - val_loss: 0.2910 - val_acc: 0.9073\n",
            "Epoch 58/60\n",
            "187/187 [==============================] - 6s 34ms/step - loss: 0.2830 - acc: 0.9098 - val_loss: 0.2990 - val_acc: 0.9017\n",
            "Epoch 59/60\n",
            "187/187 [==============================] - 7s 37ms/step - loss: 0.2842 - acc: 0.9087 - val_loss: 0.2832 - val_acc: 0.9113\n",
            "Epoch 60/60\n",
            "187/187 [==============================] - 7s 35ms/step - loss: 0.2791 - acc: 0.9121 - val_loss: 0.2884 - val_acc: 0.9120\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f40dc4299b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "metadata": {
        "id": "MAnK7D_Mm_dz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "modelFilt.load_weights('weights.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-8ZXIw1Um_ao",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "modelFilt.save('ModifiedModelHybrid.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bZsEELS6m_WM",
        "colab_type": "code",
        "outputId": "50708dea-eb17-40f4-c658-4c38417805a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "cell_type": "code",
      "source": [
        "accuracy = modelFilt.history.history['acc']\n",
        "val_accuracy = modelFilt.history.history['val_acc']\n",
        "loss = modelFilt.history.history['loss']\n",
        "val_loss = modelFilt.history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'b', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pmWdg8mT4179cLj8AKSlupMr5812gnz3b3d62XDm45BI3/s1J/FgwxpgMLOgH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\nY0wYsaBvjDFh5P8BBi0srMF0jsQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wf/8La9ZYznfnnDvcicgCVU0trlx83qEaLC3Nksr88stBb919tz3X4/77o1Av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\nwd055+LQ/wMag57YQ8NE9gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5S9iJ-HGnRFW",
        "colab_type": "code",
        "outputId": "68e7b218-665b-4518-c513-48c713ff5f11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "modelFilt.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.2831650772889455, 0.9113333333333333]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "metadata": {
        "id": "gRuSLttWnRCR",
        "colab_type": "code",
        "outputId": "5dd8e920-0264-4b42-b5fe-5c247093e236",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "#testing\n",
        "test_eval = modelFilt.evaluate(test_three_data, test_three_label_one_hot, verbose=0)\n",
        "print('Test loss:', test_eval[0])\n",
        "print('Test accuracy:', test_eval[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.3313914478619893\n",
            "Test accuracy: 0.8949999998410543\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MI4AzYZDnQ_X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GCHKtUi6nQ8I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ie8_KBL8TIl6",
        "colab_type": "code",
        "outputId": "e9dadd09-2000-4f72-d9e7-1974347cf229",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "cell_type": "code",
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'HighAccuracyConv2'\n",
        "discores_low_acc_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.47800322599191625\n",
            "STD of DI Score:  0.07607869969361732\n",
            "MAX of DI Score:  0.6407786286668374\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9I8lFwK3AG6vq2nV7SnNjffsWoyTBcJekHkFzJdFZAFV1fpIdhtl4VZ0BnDFu3tsGbhdw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\nQZLU+f+UYPbi8LQHCQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "r2XDOWtOP2p7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "modelFilt.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CXVt9e15sCf8",
        "colab_type": "code",
        "outputId": "7e737ef7-5020-46e4-e233-d1caa2e064b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_8'\n",
        "discores_low_acc_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5925375772649611\n",
            "STD of DI Score:  0.056699249107349546\n",
            "MAX of DI Score:  0.6699787896299502\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2owmDg4HxVxZ9keYI4aNJdqJpTrpgBs8xI8N+kPiBI2mxmjYUkuwPvB3YGtgtyf2Bo6rq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\nkqSOoSBJ6hgKkqSOoSBJ6vx/AQgiF0LFN4gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dxnMxP6cTGLO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " "
      ]
    },
    {
      "metadata": {
        "id": "_w7HlgF3XA9E",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RX5m4EEvXA5g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "72XqLffkXA20",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EF-E1FlfXAgF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "qyH9eKlYbXNn",
        "colab_type": "code",
        "outputId": "9629cc52-ce19-4bfb-ab0c-5d1c85e43c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2026
        }
      },
      "cell_type": "code",
      "source": [
        "modelFilt.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=60,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])\n",
        "#save to disk\n",
        "# model_json = model.to_json()\n",
        "# with open('modelHighAcc.json', 'w') as json_file:\n",
        "#     json_file.write(model_json)\n",
        "# model.save_weights('modelHighAcc.h5') \n",
        "# model.save('high_acc_complete_modelConv2d_2.h5')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/60\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 1.0914 - acc: 0.6170 - val_loss: 1.0533 - val_acc: 0.6503\n",
            "Epoch 2/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.7965 - acc: 0.6631 - val_loss: 0.6973 - val_acc: 0.7247\n",
            "Epoch 3/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.6907 - acc: 0.7025 - val_loss: 0.6277 - val_acc: 0.7327\n",
            "Epoch 4/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.6372 - acc: 0.7331 - val_loss: 0.5845 - val_acc: 0.7633\n",
            "Epoch 5/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.5893 - acc: 0.7575 - val_loss: 0.5723 - val_acc: 0.7650\n",
            "Epoch 6/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.5638 - acc: 0.7772 - val_loss: 0.5688 - val_acc: 0.7923\n",
            "Epoch 7/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.5404 - acc: 0.7868 - val_loss: 0.5032 - val_acc: 0.8123\n",
            "Epoch 8/60\n",
            "187/187 [==============================] - 7s 37ms/step - loss: 0.5651 - acc: 0.7914 - val_loss: 0.4749 - val_acc: 0.8183\n",
            "Epoch 9/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.5130 - acc: 0.8015 - val_loss: 0.8505 - val_acc: 0.6970\n",
            "Epoch 10/60\n",
            "187/187 [==============================] - 7s 37ms/step - loss: 0.4912 - acc: 0.8123 - val_loss: 0.4684 - val_acc: 0.8337\n",
            "Epoch 11/60\n",
            "187/187 [==============================] - 7s 36ms/step - loss: 0.4880 - acc: 0.8147 - val_loss: 0.4659 - val_acc: 0.8307\n",
            "Epoch 12/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4772 - acc: 0.8246 - val_loss: 0.4656 - val_acc: 0.8333\n",
            "Epoch 13/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4620 - acc: 0.8308 - val_loss: 0.4341 - val_acc: 0.8480\n",
            "Epoch 14/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4480 - acc: 0.8366 - val_loss: 0.5995 - val_acc: 0.8087\n",
            "Epoch 15/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4506 - acc: 0.8375 - val_loss: 0.4449 - val_acc: 0.8377\n",
            "Epoch 16/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4393 - acc: 0.8422 - val_loss: 0.5139 - val_acc: 0.8280\n",
            "Epoch 17/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4462 - acc: 0.8391 - val_loss: 0.4592 - val_acc: 0.8350\n",
            "Epoch 18/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4315 - acc: 0.8464 - val_loss: 0.5755 - val_acc: 0.8043\n",
            "Epoch 19/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4305 - acc: 0.8475 - val_loss: 0.4241 - val_acc: 0.8500\n",
            "Epoch 20/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4261 - acc: 0.8501 - val_loss: 0.4542 - val_acc: 0.8373\n",
            "Epoch 21/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4137 - acc: 0.8554 - val_loss: 0.4838 - val_acc: 0.8317\n",
            "Epoch 22/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.4216 - acc: 0.8521 - val_loss: 0.5241 - val_acc: 0.8177\n",
            "Epoch 23/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.4239 - acc: 0.8544 - val_loss: 0.4090 - val_acc: 0.8617\n",
            "Epoch 24/60\n",
            "187/187 [==============================] - 7s 35ms/step - loss: 0.4111 - acc: 0.8630 - val_loss: 0.4724 - val_acc: 0.8287\n",
            "Epoch 25/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3932 - acc: 0.8648 - val_loss: 0.4638 - val_acc: 0.8450\n",
            "Epoch 26/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3906 - acc: 0.8677 - val_loss: 0.6440 - val_acc: 0.8327\n",
            "Epoch 27/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3948 - acc: 0.8681 - val_loss: 0.4341 - val_acc: 0.8533\n",
            "Epoch 28/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3927 - acc: 0.8655 - val_loss: 0.4833 - val_acc: 0.8410\n",
            "Epoch 29/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3919 - acc: 0.8715 - val_loss: 0.4792 - val_acc: 0.8440\n",
            "Epoch 30/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3878 - acc: 0.8689 - val_loss: 0.4615 - val_acc: 0.8557\n",
            "Epoch 31/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3759 - acc: 0.8753 - val_loss: 0.4198 - val_acc: 0.8637\n",
            "Epoch 32/60\n",
            "187/187 [==============================] - 7s 37ms/step - loss: 0.3850 - acc: 0.8729 - val_loss: 0.5197 - val_acc: 0.8247\n",
            "Epoch 33/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3701 - acc: 0.8810 - val_loss: 0.4236 - val_acc: 0.8650\n",
            "Epoch 34/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3829 - acc: 0.8744 - val_loss: 0.4045 - val_acc: 0.8663\n",
            "Epoch 35/60\n",
            "187/187 [==============================] - 7s 36ms/step - loss: 0.3761 - acc: 0.8809 - val_loss: 0.4072 - val_acc: 0.8637\n",
            "Epoch 36/60\n",
            "187/187 [==============================] - 7s 38ms/step - loss: 0.3773 - acc: 0.8762 - val_loss: 0.3791 - val_acc: 0.8743\n",
            "Epoch 37/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3643 - acc: 0.8834 - val_loss: 0.4467 - val_acc: 0.8547\n",
            "Epoch 38/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3615 - acc: 0.8849 - val_loss: 0.4095 - val_acc: 0.8637\n",
            "Epoch 39/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3662 - acc: 0.8799 - val_loss: 0.4111 - val_acc: 0.8677\n",
            "Epoch 40/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3657 - acc: 0.8847 - val_loss: 0.5137 - val_acc: 0.8497\n",
            "Epoch 41/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3730 - acc: 0.8814 - val_loss: 0.4308 - val_acc: 0.8660\n",
            "Epoch 42/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3685 - acc: 0.8839 - val_loss: 0.3999 - val_acc: 0.8767\n",
            "Epoch 43/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3533 - acc: 0.8926 - val_loss: 0.4592 - val_acc: 0.8573\n",
            "Epoch 44/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3510 - acc: 0.8917 - val_loss: 0.4648 - val_acc: 0.8547\n",
            "Epoch 45/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3586 - acc: 0.8880 - val_loss: 0.4407 - val_acc: 0.8600\n",
            "Epoch 46/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3548 - acc: 0.8914 - val_loss: 0.8102 - val_acc: 0.8277\n",
            "Epoch 47/60\n",
            "187/187 [==============================] - 6s 33ms/step - loss: 0.3541 - acc: 0.8898 - val_loss: 0.5405 - val_acc: 0.8200\n",
            "Epoch 48/60\n",
            " 98/187 [==============>...............] - ETA: 2s - loss: 0.3465 - acc: 0.8892"
          ],
          "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-146-ec12c04c528b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m modelFilt.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=60,                    verbose=1,validation_data=(valid_X,valid_ground),\n\u001b[0;32m----> 2\u001b[0;31m                     shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m#save to disk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# model_json = model.to_json()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# with open('modelHighAcc.json', 'w') as json_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/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mkwargs\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     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\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/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\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/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m                 \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\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    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\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/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    683\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    684\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\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--> 685\u001b[0;31m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\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[0m\u001b[1;32m    686\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\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    687\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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--> 664\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    665\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\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    666\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    659\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    660\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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--> 661\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    662\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\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[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\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[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "h7OTTp-uYZih",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model_low_accuracy.layers[1].get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yDoD-D2pYLEd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# modelFilt.layers[1].get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y-ssOHjPXTzE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# weights_low_conv2d1 = layer_dict_high['conv2d_1'].get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KD0MAAeLFuhX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model_low_accuracy.layers('conv2d_1').get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A0ulN6H5FueV",
        "colab_type": "code",
        "outputId": "a289fa36-bbe6-4c59-e642-1c8d2d4b4a5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "cell_type": "code",
      "source": [
        "# weights_low_bn1 = layer_dict_high['batch_normalization_1'].get_weights()\n",
        "# modelFilt.layers[3].set_weights(weights_low_conv2d1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'batch_normalization_1': <keras.layers.normalization.BatchNormalization at 0x7f1533511438>,\n",
              " 'batch_normalization_2': <keras.layers.normalization.BatchNormalization at 0x7f15335117f0>,\n",
              " 'batch_normalization_3': <keras.layers.normalization.BatchNormalization at 0x7f1533511ba8>,\n",
              " 'batch_normalization_4': <keras.layers.normalization.BatchNormalization at 0x7f1533511e80>,\n",
              " 'batch_normalization_5': <keras.layers.normalization.BatchNormalization at 0x7f153352d278>,\n",
              " 'batch_normalization_6': <keras.layers.normalization.BatchNormalization at 0x7f153352d550>,\n",
              " 'conv2d_1': <keras.layers.convolutional.Conv2D at 0x7f153350c0f0>,\n",
              " 'conv2d_2': <keras.layers.convolutional.Conv2D at 0x7f1533511668>,\n",
              " 'conv2d_3': <keras.layers.convolutional.Conv2D at 0x7f15335119e8>,\n",
              " 'conv2d_4': <keras.layers.convolutional.Conv2D at 0x7f1533511cc0>,\n",
              " 'conv2d_5': <keras.layers.convolutional.Conv2D at 0x7f153352d0b8>,\n",
              " 'conv2d_6': <keras.layers.convolutional.Conv2D at 0x7f153352d390>,\n",
              " 'dense_1': <keras.layers.core.Dense at 0x7f153352d7b8>,\n",
              " 'dropout_1': <keras.layers.core.Dropout at 0x7f15335119b0>,\n",
              " 'dropout_2': <keras.layers.core.Dropout at 0x7f153352d080>,\n",
              " 'dropout_3': <keras.layers.core.Dropout at 0x7f153352d710>,\n",
              " 'flatten_1': <keras.layers.core.Flatten at 0x7f153352d748>,\n",
              " 'input_1': <keras.engine.input_layer.InputLayer at 0x7f153350c438>,\n",
              " 'max_pooling2d_1': <keras.layers.pooling.MaxPooling2D at 0x7f1533511908>,\n",
              " 'max_pooling2d_2': <keras.layers.pooling.MaxPooling2D at 0x7f1533511f98>,\n",
              " 'max_pooling2d_3': <keras.layers.pooling.MaxPooling2D at 0x7f153352d668>}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "metadata": {
        "id": "TEkg52f_HxEg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Ignore below"
      ]
    },
    {
      "metadata": {
        "id": "_iOvwS5SJVT8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def cifar10ClassifierTransfer(input_img, highAccInput):\n",
        "  \n",
        "  # Add additional inputs to \n",
        "  \n",
        "  conv1_1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "  conv1_2 = BatchNormalization()(conv1_1)\n",
        "  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\n",
        "  # now concat secondary input that corresponds to high accuracy n/w to this\n",
        "    \n",
        "  concated_layer = keras.layers.merge.Concatenate()([conv2_1, highAccInput])\n",
        "  conv2_2 = BatchNormalization()(concated_layer)\n",
        "  pool2_3 = MaxPooling2D(pool_size=(2, 2))(conv2_2) #14 x 14 x 32\n",
        "  drop2_4= Dropout(.2)(pool2_3)\n",
        "\n",
        "  conv3_1 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2_4)\n",
        "  conv3_2 = BatchNormalization()(conv3_1)\n",
        "  conv4_1 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "  conv4_2 = BatchNormalization()(conv4_1)\n",
        "  pool4_3 = MaxPooling2D(pool_size=(2, 2))(conv4_2) #14 x 14 x 32\n",
        "  drop4_4= Dropout(.3)(pool4_3)\n",
        "\n",
        "  conv5_1 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop4_4)\n",
        "  conv5_2 = BatchNormalization()(conv5_1)\n",
        "  conv6_1 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "  conv6_2 = BatchNormalization()(conv6_1)\n",
        "  pool6_3 = MaxPooling2D(pool_size=(2, 2))(conv6_2) #14 x 14 x 32\n",
        "  drop6_4= Dropout(.4)(pool6_3)\n",
        "\n",
        "  flat = Flatten()(drop6_4)\n",
        "  out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PyCdAWfOBhSi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def featureExtractor(input_img, layer_name):\n",
        "  get_cnn1_output_function = K.function([trained_model.layers[0].input],\n",
        "                                  [trained_model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "  cnn1_output = get_cnn1_output_function([train_three_data])[0]\n",
        "  return cnn1_output"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lYM0VB8wBhP8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def cifar10ClassifierTransfer(input_img, conv2_outputs):\n",
        "  \n",
        "  # Add additional inputs to \n",
        "  \n",
        "  conv1_1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "  conv1_2 = BatchNormalization()(conv1_1)\n",
        "  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\n",
        "  conv2_2 = BatchNormalization()(conv2_1)\n",
        "  pool2_3 = MaxPooling2D(pool_size=(2, 2))(conv2_2) #14 x 14 x 32\n",
        "  drop2_4= Dropout(.2)(pool2_3)\n",
        "\n",
        "  conv3_1 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2_4)\n",
        "  conv3_2 = BatchNormalization()(conv3_1)\n",
        "  conv4_1 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "  conv4_2 = BatchNormalization()(conv4_1)\n",
        "  pool4_3 = MaxPooling2D(pool_size=(2, 2))(conv4_2) #14 x 14 x 32\n",
        "  drop4_4= Dropout(.3)(pool4_3)\n",
        "\n",
        "  conv5_1 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop4_4)\n",
        "  conv5_2 = BatchNormalization()(conv5_1)\n",
        "  conv6_1 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "  conv6_2 = BatchNormalization()(conv6_1)\n",
        "  pool6_3 = MaxPooling2D(pool_size=(2, 2))(conv6_2) #14 x 14 x 32\n",
        "  drop6_4= Dropout(.4)(pool6_3)\n",
        "\n",
        "  flat = Flatten()(drop6_4)\n",
        "  out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sXXygEe9BhNK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LDTPGc0ABhKO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gsrj25pSoRh_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def highAccuracyModelTillConv2(input_img, channel_indices_first_layer, channel_indices_second_layer): \n",
        "  conv1_1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu', name = 'HighAccuracyConv1'))(input_img)\n",
        "  conv1_2 = BatchNormalization()(conv1_1)\n",
        "  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu', name = 'HighAccuracyConv2',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\n",
        "  conv2_2 = BatchNormalization()(conv2_1)\n",
        "#   pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "#   drop1= Dropout(.2)(pool1)\n",
        "\n",
        "#   conv2 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop1)\n",
        "#   conv2 = BatchNormalization()(conv2)\n",
        "#   conv2 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv2)\n",
        "#   conv2 = BatchNormalization()(conv2)\n",
        "#   pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #14 x 14 x 32\n",
        "#   drop2= Dropout(.3)(pool2)\n",
        "\n",
        "#   conv3 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2)\n",
        "#   conv3 = BatchNormalization()(conv3)\n",
        "#   conv3 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3)\n",
        "#   conv3 = BatchNormalization()(conv3)\n",
        "#   pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) #14 x 14 x 32\n",
        "#   drop3= Dropout(.4)(pool3)\n",
        "\n",
        "#   flat = Flatten()(drop3)\n",
        "#   out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "  return conv1_1[:,:,:,channel_indices_first_layer], conv2_1[:,:,:,channel_indices_second_layer]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RsXlJYX62MB-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}