MultiLayerTransferV2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MultiLayerTransferV1.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/KProj/blob/master/MultiLayerTransferV2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdTAcjYec3s7",
        "colab_type": "text"
      },
      "source": [
        "## Load standard libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCo6LRTTcxmC",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sg_ZApCOPN9u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.utils.vis_utils import plot_model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oG3txkjndHWQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras.backend.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rDrYIgZc20A",
        "colab_type": "text"
      },
      "source": [
        "## Load image data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85Dlxs7adKVS",
        "colab_type": "code",
        "outputId": "27176dd5-e657-4594-8f18-ad9d1a013ba1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "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": 84,
      "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": {
            "image/png": 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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": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dQQPlF6RdS2m",
        "colab_type": "text"
      },
      "source": [
        "## Create Function to get data corresponding to three labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFyk3xzNdYNW",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ex0j41PLde2x",
        "colab_type": "text"
      },
      "source": [
        "## Get data for three classes using getThreeClassesTrainTest function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpqbOTfBdhHk",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddFucSl_YW5B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = (np.load('train_three_data_saved.npy'),\n",
        "                                                                      np.load('train_three_label_saved.npy'),\n",
        "                                                                      np.load('test_three_data_saved.npy'),\n",
        "                                                                      np.load('test_three_label_saved.npy'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDQiwalsYIGo",
        "colab_type": "code",
        "outputId": "0be17560-9dc7-4a8a-94fb-331fed538b5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_three_label"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 3, 5, ..., 0, 5, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCQfV4TvZh1r",
        "colab_type": "code",
        "outputId": "9a119dd8-30b4-45ad-c805-70bbb68ce520",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_three_label"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 5, 0, ..., 5, 0, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gK_tiIWd2Nf",
        "colab_type": "code",
        "outputId": "82f789f7-aa62-4013-de04-8a9140042116",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 90,
      "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": 90
        },
        {
          "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": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEYcIMeqd4i1",
        "colab_type": "code",
        "outputId": "04134d0c-ff4b-4953-aaf9-6dabfa88b288",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "plt.hist(test_three_label)"
      ],
      "execution_count": 91,
      "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": 91
        },
        {
          "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": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4n_WiMNd_Hf",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VT_AKqQmeBk_",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWEjK_exeEaJ",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOFKlZWKeH25",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lr931MtCYGoN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ssi32yj-d1iX",
        "colab_type": "text"
      },
      "source": [
        "## Define the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUISHV2weN0t",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ich0RbYnebwW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cifar10Classifier(input_img):\n",
        "  \n",
        "  conv1_1 = (Conv2D(64, (3,3),name = 'conv1_1' ,padding='same', \n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:],\n",
        "                    activation='relu'))(input_img)\n",
        "  conv1_2 = BatchNormalization(name = 'conv1_2')(conv1_1)\n",
        "  pool1_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool1_3')(conv1_2) #14 x 14 x 32\n",
        "  conv2_1 = (Conv2D(128, (3,3),name = 'conv2_1' ,padding='same',\n",
        "                    activation= 'relu',kernel_regularizer=regularizers.l2(weight_decay)))(pool1_3)\n",
        "  conv2_2 = BatchNormalization(name = 'conv2_2')(conv2_1)\n",
        "  pool2_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool2_3')(conv2_2) #14 x 14 x 32\n",
        "  \n",
        "  conv3_1 = (Conv2D(256, (3,3), padding='same',name = 'conv3_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool2_3)\n",
        "  conv3_2 = BatchNormalization(name = 'conv3_2')(conv3_1)\n",
        "  conv4_1 = (Conv2D(256, (3,3), padding='same',name = 'conv4_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "  conv4_2 = BatchNormalization(name = 'conv4_2')(conv4_1)\n",
        "  pool4_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool4_2')(conv4_2) #14 x 14 x 32\n",
        "  \n",
        "  conv5_1 = (Conv2D(512, (3,3), padding='same',name = 'conv5_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool4_2)\n",
        "  conv5_2 = BatchNormalization(name = 'conv5_2')(conv5_1)\n",
        "  conv6_1 = (Conv2D(512, (3,3), padding='same',name = 'conv6_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "  conv6_2 = BatchNormalization(name = 'conv6_2')(conv6_1)\n",
        "  pool6_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool6_2')(conv6_2) #14 x 14 x 32\n",
        "  \n",
        "  conv7_1 = (Conv2D(512, (3,3), padding='same',name = 'conv7_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool6_2)\n",
        "  conv7_2 = BatchNormalization(name = 'conv7_2')(conv7_1)\n",
        "  conv8_1 = (Conv2D(512, (3,3), padding='same',name = 'conv8_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv7_2)\n",
        "  conv8_2 = BatchNormalization(name = 'conv8_2')(conv8_1)\n",
        "  pool8_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool8_2')(conv8_2) #14 x 14 x 32\n",
        "\n",
        "\n",
        "  flat = Flatten()(pool8_2)\n",
        "  fc1 = Dense(4096, activation='relu', name = 'fc1')(flat)\n",
        "  fc2 = Dense(4096, activation='relu', name = 'fc2')(fc1)\n",
        "  fc3 = Dense(1000, activation='relu', name = 'fc3')(fc2)\n",
        "\n",
        "\n",
        "  \n",
        "  out = Dense(num_classes, activation='softmax', name = 'out')(fc3)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_0LHS-Xf7aN",
        "colab_type": "text"
      },
      "source": [
        "## Define functions for calculation of DI score\n",
        "getDIScoreOfLayer(layer_output, true_labels), \n",
        "getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsLk-f7sf6zd",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubrpaIpFgBzn",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQ5H0p_KjVCw",
        "colab_type": "text"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9MHI1LFjUZs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def createCNNLayerDIScorePlot(trained_model, layer_name, data_to_use = train_three_data , data_label =train_three_label ):\n",
        "#   print(trained_model.layers)\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",
        "  \n",
        "  model = trained_model\n",
        "  intermediate_layer_model = Model(inputs=model.input,\n",
        "                                 outputs=model.get_layer(layer_name).output)\n",
        "  cnn1_output = intermediate_layer_model.predict(data_to_use)\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 ' + layer_name)\n",
        "  plt.title('DI score ' + layer_name)\n",
        "\n",
        "#   plt.ylim([0,1])\n",
        "  return di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCCl7-vOfSZ9",
        "colab_type": "text"
      },
      "source": [
        "## load pretrained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqIkmDGkfRtD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loaded_model_low_accuracy = keras.models.load_model('vgg16lowAccuracyModelDataV3.h5')\n",
        "loaded_model_high_accuracy = keras.models.load_model('vgg16HighAccuracyModelData.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7J98O5qfi8Z",
        "colab_type": "code",
        "outputId": "7b75ca51-f7ed-4c59-dc4c-6b0ad1aaee17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_low_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n",
        "\n",
        "#[0.3284807046254476, 0.923]"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4554041368166606, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rddbxdrtXcR7",
        "colab_type": "code",
        "outputId": "72cf6efd-e6d1-4a53-8d6a-6ccdf160f0af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "temp_model = keras.models.load_model('vgg16lowAccuracyModelDataV3.h5')\n",
        "temp_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4554041436513265, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsXuB6NbXmGB",
        "colab_type": "code",
        "outputId": "5d0f8689-d9d1-4675-e15a-7aa8ec45a49f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "valid_ground"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 1.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 1., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [1., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVSvyoZ8Uw_y",
        "colab_type": "code",
        "outputId": "74ab56bd-2b60-4555-f94c-de669105e2a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_low_accuracy.evaluate(test_three_data, test_three_label_one_hot, verbose=0)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4510676853656769, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAY0LGCNSMBc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loaded_model_low_accuracy.load_weights('weights.hdf5')\n",
        "# loaded_model_low_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jsm88PV3TlmL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7c-A-a4SS6S",
        "colab_type": "code",
        "outputId": "ab36752f-4d43-4da4-c4f2-6dff73e08361",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_low_accuracy.evaluate(test_three_data, test_three_label_one_hot, verbose=0)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4510676853656769, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzbIXvcEoNMj",
        "colab_type": "code",
        "outputId": "0c67539c-17c2-4575-cf04-79b590efeca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        }
      },
      "source": [
        "loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv1_1 (Conv2D)             (None, 32, 32, 64)        1792      \n",
            "_________________________________________________________________\n",
            "conv1_2 (BatchNormalization) (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "pool1_3 (MaxPooling2D)       (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2_1 (Conv2D)             (None, 16, 16, 128)       73856     \n",
            "_________________________________________________________________\n",
            "conv2_2 (BatchNormalization) (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "pool2_3 (MaxPooling2D)       (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv3_1 (Conv2D)             (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "conv3_2 (BatchNormalization) (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv4_1 (Conv2D)             (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "conv4_2 (BatchNormalization) (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "pool4_2 (MaxPooling2D)       (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv5_1 (Conv2D)             (None, 4, 4, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "conv5_2 (BatchNormalization) (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv6_1 (Conv2D)             (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv6_2 (BatchNormalization) (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "pool6_2 (MaxPooling2D)       (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv7_1 (Conv2D)             (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv7_2 (BatchNormalization) (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv8_1 (Conv2D)             (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv8_2 (BatchNormalization) (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "pool8_2 (MaxPooling2D)       (None, 1, 1, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              2101248   \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "fc3 (Dense)                  (None, 1000)              4097000   \n",
            "_________________________________________________________________\n",
            "out (Dense)                  (None, 3)                 3003      \n",
            "=================================================================\n",
            "Total params: 32,214,051\n",
            "Trainable params: 32,208,547\n",
            "Non-trainable params: 5,504\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UijDrRL2khSY",
        "colab_type": "text"
      },
      "source": [
        "## Create a plot for DI score using function createCNNLayerDIScorePlot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lyk2-GAGaPnV",
        "colab_type": "text"
      },
      "source": [
        "#### On train data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EN1yVJGzUzXX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# layer_name = 'conv2d_2'\n",
        "# model = loaded_model_low_accuracy\n",
        "# intermediate_layer_model = Model(i4nputs=model.input,\n",
        "#                                  outputs=model.get_layer(layer_name).output)\n",
        "# intermediate_output = intermediate_layer_model.predict(train_three_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_GyLE1oSbGn",
        "colab_type": "code",
        "outputId": "9c790559-3928-48fc-de86-a017e5861676",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_names = ['conv3_1','conv4_1','conv5_1','conv6_1']\n",
        "all_di_acores_low_accuracy = []\n",
        "for lay in layer_names:\n",
        "  this_di_score_low_accuracy_model = createCNNLayerDIScorePlot(loaded_model_low_accuracy, lay)\n",
        "  all_di_acores_low_accuracy.append(this_di_score_low_accuracy_model)\n",
        "# discores_low_acc_model = createCNNLayerDIScorePlot(loaded_model_low_accuracy, layer_name)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.19749844508595787\n",
            "STD of DI Score:  0.12781701219563232\n",
            "MAX of DI Score:  0.5098698647993428\n",
            "Mean of DI SCore:  0.34570139195850247\n",
            "STD of DI Score:  0.24701570789438163\n",
            "MAX of DI Score:  0.7313626336477702\n",
            "Mean of DI SCore:  0.19495639609450685\n",
            "STD of DI Score:  0.3254786915540021\n",
            "MAX of DI Score:  0.9865197330387301\n",
            "Mean of DI SCore:  0.37382222574710966\n",
            "STD of DI Score:  0.48835417624165806\n",
            "MAX of DI Score:  1.3794686910017402\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2yu73u3Ae56g5HApmZtY2Zp+CpJ/x/MtSnyciFoz1mpkVY+X9Kzm06EJYpXUb7dRXGJXE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\nmZm1ORTMzKzt/wMxOf1NCw/yNAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBUWcfwaYwsx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# discores_low_acc_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siJntOdpYoNZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.sort(discores_low_acc_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQC6i-0MVmWk",
        "colab_type": "code",
        "outputId": "5cee4e6b-8bcd-4077-949b-e8e1d5181bef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        }
      },
      "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)\n",
        "all_di_acores_high_accuracy = []\n",
        "for lay in layer_names:\n",
        "  this_di_score_high_accuracy_model = createCNNLayerDIScorePlot(loaded_model_high_accuracy, lay)\n",
        "  all_di_acores_high_accuracy.append(this_di_score_high_accuracy_model)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.2189881730597697\n",
            "STD of DI Score:  0.11230534560189395\n",
            "MAX of DI Score:  0.5067606097713149\n",
            "Mean of DI SCore:  0.21035941816979606\n",
            "STD of DI Score:  0.129535571359662\n",
            "MAX of DI Score:  0.5465293511639104\n",
            "Mean of DI SCore:  0.07020846489940907\n",
            "STD of DI Score:  0.08509172793570702\n",
            "MAX of DI Score:  0.286623851657295\n",
            "Mean of DI SCore:  0.050368643443725505\n",
            "STD of DI Score:  0.08475685808544185\n",
            "MAX of DI Score:  0.3209857270386972\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/cMUM6jPriUPBmmwl8CklD4zfB/gAcLykiREsl023goh4HPgy8KKW2ctJOqKv\nJBnFtle/BJZIuonk+QKnpfN7rs+sVx4l1czMMj5TMDOzjEPBzMwyDgUzM8s4FMzMLONQMDOzjEPB\nzMwyDgUzM8v8N2G8uksEUEvpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGjrlhMbVmUv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHVud4ykeRn8",
        "colab_type": "text"
      },
      "source": [
        "## Pick top 10 DI scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8k5Ob6gWeQ94",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyI8gOZuftmZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjMoET8nasck",
        "colab_type": "text"
      },
      "source": [
        "#### On validation data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPKQgmyXbACY",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbg5-HKhaxxj",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zaH4FbSFaxuc",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMpuiVdSf60g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.sort(discores_high_acc_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZueWb5hY7zZJ",
        "colab_type": "text"
      },
      "source": [
        "#### Pick top channels from the layers selected"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9HwamF674hP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "topChoose = True\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmggM8xc8c8G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "9fb50ef5-a663-4181-a862-9ad6e8896763"
      },
      "source": [
        "for i,d in enumerate(all_di_acores_high_accuracy):\n",
        "  print(len(d),i)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "256 0\n",
            "256 1\n",
            "512 2\n",
            "512 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1cAvZpx9G8_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Ks = [128,128,256,256]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3zlCJ3R9jjk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_low_accuracy_model = loaded_model_low_accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZY28-KyEAhdg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_high_accuracy_model = loaded_model_high_accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgoNV1oG8BS4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for l,DIs in enumerate(all_di_acores_high_accuracy):\n",
        "  this_layer_name = layer_names[l]\n",
        "  this_high_accuracy_di_scores = all_di_acores_high_accuracy[l]\n",
        "  this_high_accuracy_di_scores_sorted = np.argsort(this_high_accuracy_di_scores)\n",
        "  this_high_accuracy_di_scores_sorted_list = np.ndarray.tolist(this_high_accuracy_di_scores_sorted)\n",
        "  top_channels = this_high_accuracy_di_scores_sorted_list[-Ks[l]:]\n",
        "  \n",
        "  # do same thing for lowest DIs\n",
        "  this_low_accuracy_di_scores = all_di_acores_low_accuracy[l]\n",
        "  this_low_accuracy_di_scores_sorted = np.argsort(this_low_accuracy_di_scores)\n",
        "  this_low_accuracy_di_scores_sorted_list = np.ndarray.tolist(this_low_accuracy_di_scores_sorted)\n",
        "  bottom_channels = this_low_accuracy_di_scores_sorted_list[-Ks[l]:]\n",
        "  \n",
        "  # now replace the weights\n",
        "  low_accuracy_model_weights_bias = copied_low_accuracy_model.get_layer(this_layer_name).get_weights()\n",
        "  high_accuracy_model_weights_bias = copied_high_accuracy_model.get_layer(this_layer_name).get_weights()\n",
        "\n",
        "  low_accuracy_model_weight,low_accuracy_model_bias = low_accuracy_model_weights_bias[0],low_accuracy_model_weights_bias[1]\n",
        "  high_accuracy_model_weight,high_accuracy_model_bias = high_accuracy_model_weights_bias[0],high_accuracy_model_weights_bias[1]\n",
        "#   print(low_accuracy_model_weight.shape,low_accuracy_model_bias.shape)\n",
        "  low_accuracy_model_weight[:,:,:,bottom_channels] = high_accuracy_model_weight[:,:,:,top_channels]\n",
        "#   print(bottom_channels)\n",
        "  high_accuracy_model_bias[bottom_channels] = high_accuracy_model_bias[top_channels]\n",
        "  \n",
        "  copied_low_accuracy_model.get_layer(this_layer_name).set_weights([low_accuracy_model_weight, high_accuracy_model_bias])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OodsSlYDCesc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_accuracy_model_weights_bias.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AtLgL9ICGJE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_accuracy_model_bias"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzN_W73aldFU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# # (discores_high_acc_model)\n",
        "# K= 50 # 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:]#np.random.choice(32,K)#\n",
        "# print('Top Channels: ',top_channels)\n",
        "\n",
        "\n",
        "# low_acc_di_sorted = np.argsort(discores_low_acc_model)\n",
        "# low_acc_di_sorted_list = np.ndarray.tolist(low_acc_di_sorted)\n",
        "# low_channels = low_acc_di_sorted_list[0:K]#np.random.choice(32,K)\n",
        "# print('Low Channels: ', low_channels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_BDN-T-BaGz",
        "colab_type": "text"
      },
      "source": [
        "#####  get low accuracy DI channels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5DgBrzXCGB8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# discores_low_acc_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5B34pIwCbXN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.plot(discores_low_acc_model,'-*')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRrtIpLVBdK4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SD8nswMnxWVC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# total_channels = len(discores_high_acc_model)\n",
        "# total_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsCtxzRVokuR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "enable_training = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sk_0a6JlFux5",
        "colab_type": "code",
        "outputId": "ac92c842-dc88-454e-d961-d62306d8a4ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# returns \n",
        "'''\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 = enable_training,\n",
        "                    name = 'HighAccuracyConv1'))(input_img)\n",
        "  conv1_2 = BatchNormalization(name = 'HighAccuracyBN1',\n",
        "                              trainable = enable_training)(conv1_1)\n",
        "  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',trainable = enable_training,\n",
        "                    name = 'HighAccuracyConv2',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\n",
        "#   conv2_2 = BatchNormalization(name = 'HighAccuracyBN2',\n",
        "#                               trainable = enable_training)(conv2_1)  \n",
        "  \n",
        "  return conv2_1\n",
        "'''"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\ndef 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 = enable_training,\\n                    name = 'HighAccuracyConv1'))(input_img)\\n  conv1_2 = BatchNormalization(name = 'HighAccuracyBN1',\\n                              trainable = enable_training)(conv1_1)\\n  conv2_1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',trainable = enable_training,\\n                    name = 'HighAccuracyConv2',kernel_regularizer=regularizers.l2(weight_decay)))(conv1_2)\\n#   conv2_2 = BatchNormalization(name = 'HighAccuracyBN2',\\n#                               trainable = enable_training)(conv2_1)  \\n  \\n  return conv2_1\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZchwmWh7la9",
        "colab_type": "code",
        "outputId": "f125c42a-6abb-4ea3-e781-d79fa05d00af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        }
      },
      "source": [
        "copied_low_accuracy_model.summary()"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv1_1 (Conv2D)             (None, 32, 32, 64)        1792      \n",
            "_________________________________________________________________\n",
            "conv1_2 (BatchNormalization) (None, 32, 32, 64)        256       \n",
            "_________________________________________________________________\n",
            "pool1_3 (MaxPooling2D)       (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2_1 (Conv2D)             (None, 16, 16, 128)       73856     \n",
            "_________________________________________________________________\n",
            "conv2_2 (BatchNormalization) (None, 16, 16, 128)       512       \n",
            "_________________________________________________________________\n",
            "pool2_3 (MaxPooling2D)       (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv3_1 (Conv2D)             (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "conv3_2 (BatchNormalization) (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv4_1 (Conv2D)             (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "conv4_2 (BatchNormalization) (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "pool4_2 (MaxPooling2D)       (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv5_1 (Conv2D)             (None, 4, 4, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "conv5_2 (BatchNormalization) (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv6_1 (Conv2D)             (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv6_2 (BatchNormalization) (None, 4, 4, 512)         2048      \n",
            "_________________________________________________________________\n",
            "pool6_2 (MaxPooling2D)       (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv7_1 (Conv2D)             (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv7_2 (BatchNormalization) (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv8_1 (Conv2D)             (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv8_2 (BatchNormalization) (None, 2, 2, 512)         2048      \n",
            "_________________________________________________________________\n",
            "pool8_2 (MaxPooling2D)       (None, 1, 1, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              2101248   \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "fc3 (Dense)                  (None, 1000)              4097000   \n",
            "_________________________________________________________________\n",
            "out (Dense)                  (None, 3)                 3003      \n",
            "=================================================================\n",
            "Total params: 32,214,051\n",
            "Trainable params: 32,208,547\n",
            "Non-trainable params: 5,504\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OA-0pVGXvMeE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Concatenate, Input, Lambda\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41smOd7GTX5c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# def cifar10ClassifierTransfer(input_img):\n",
        "  \n",
        "#   conv1_1 = (Conv2D(64, (3,3),name = 'conv1_1' ,padding='same', \n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:],\n",
        "#                     activation='relu'))(input_img)\n",
        "#   conv1_2 = BatchNormalization(name = 'conv1_2')(conv1_1)\n",
        "#   pool1_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool1_3')(conv1_2) #14 x 14 x 32\n",
        "#   conv2_1 = (Conv2D(128, (3,3),name = 'conv2_1' ,padding='same',\n",
        "#                     activation= 'relu',kernel_regularizer=regularizers.l2(weight_decay)))(pool1_3)\n",
        "#   conv2_2 = BatchNormalization(name = 'conv2_2')(conv2_1)\n",
        "#   pool2_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool2_3')(conv2_2) #14 x 14 x 32\n",
        "  \n",
        "#   conv3_1 = (Conv2D(256, (3,3), padding='same',name = 'conv3_1',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool2_3)\n",
        "#   conv3_2 = BatchNormalization(name = 'conv3_2')(conv3_1)\n",
        "#   conv4_1 = (Conv2D(256, (3,3), padding='same',name = 'conv4_1' ,activation= 'relu',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "#   conv4_2 = BatchNormalization(name = 'conv4_2')(conv4_1)\n",
        "#   pool4_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool4_2')(conv4_2) #14 x 14 x 32\n",
        "  \n",
        "#   conv5_1 = (Conv2D(512, (3,3), padding='same',name = 'conv5_1',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool4_2)\n",
        "#   conv5_2 = BatchNormalization(name = 'conv5_2')(conv5_1)\n",
        "#   conv6_1 = (Conv2D(512, (3,3), padding='same',name = 'conv6_1' ,activation= 'relu',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "#   conv6_2 = BatchNormalization(name = 'conv6_2')(conv6_1)\n",
        "#   pool6_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool6_2')(conv6_2) #14 x 14 x 32\n",
        "  \n",
        "#   conv7_1 = (Conv2D(512, (3,3), padding='same',name = 'conv7_1',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool6_2)\n",
        "#   conv7_2 = BatchNormalization(name = 'conv7_2')(conv7_1)\n",
        "#   conv8_1 = (Conv2D(512, (3,3), padding='same',name = 'conv8_1' ,activation= 'relu',\n",
        "#                     kernel_regularizer=regularizers.l2(weight_decay)))(conv7_2)\n",
        "#   conv8_2 = BatchNormalization(name = 'conv8_2')(conv8_1)\n",
        "#   pool8_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool8_2')(conv8_2) #14 x 14 x 32\n",
        "\n",
        "\n",
        "#   flat = Flatten()(pool8_2)\n",
        "#   fc1 = Dense(4096, activation='relu', name = 'fc1')(flat)\n",
        "#   fc2 = Dense(4096, activation='relu', name = 'fc2')(fc1)\n",
        "#   fc3 = Dense(1000, activation='relu', name = 'fc3')(fc2)\n",
        "\n",
        "\n",
        "  \n",
        "#   out = Dense(num_classes, activation='softmax', name = 'out')(fc3)\n",
        "  \n",
        "#   return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sf3PN5YIyba2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# top_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzUmKtVGFusU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #high_acc_output = highAccuracyModelTillConv2(input_img)\n",
        "# modelFilt = Model(input_img, \n",
        "#               cifar10ClassifierTransfer(input_img))\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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cty76ahJDRhj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#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",
        "# #training\n",
        "batch_size = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WE7b72OOFups",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_model(copied_low_accuracy_model, to_file=\"FiltModel.png\", show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-yqdp6_rQkI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loaded_model_high_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GkUnIIAqH0k",
        "colab_type": "text"
      },
      "source": [
        "## set predetermined weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKVw7k8QLwoQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# top_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hWcRyTNtaXW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# top_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhmWPU7RA9N3",
        "colab_type": "text"
      },
      "source": [
        "#### # get low accuracy DI channel - stored in low_channels\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "s"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mL5R24pGA9KP",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0JBUK8KA26B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# loaded_model_high_accuracy.get_layer(layer_name).get_weights()[0].shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmS9J6gmDFIL",
        "colab_type": "text"
      },
      "source": [
        "## Get filter weights for top K10 DI scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0FN66utr5VE",
        "colab_type": "code",
        "colab": {}
      },
      "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(layer_name).get_weights()\n",
        "# # temp_weights = np.zeros_like(high_acc_conv2_weights[0].shape[0])\n",
        "# temp_weights_top_10_DI = np.zeros(shape = (high_acc_conv2_weights[0].shape[0],high_acc_conv2_weights[0].shape[1],high_acc_conv2_weights[0].shape[2],K))\n",
        "# h,w,d,n = high_acc_conv2_weights[0].shape\n",
        "# j = 0\n",
        "# for ch in range(0,n):\n",
        "#   if ch in top_channels:\n",
        "#     temp_weights_top_10_DI[:,:,:,j] = high_acc_conv2_weights[0][:,:,:,ch]\n",
        "#     j = j+1\n",
        "    \n",
        "  \n",
        "# # (high_acc_conv2_weights[0].shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bF9DJY3BD6Wf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_weights_top_10_DI.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lR-sOsQD6UV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# top_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Esv5wQmYFby6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_bias_top_10_DI = np.zeros(shape = (K,))\n",
        "# # temp_bias = high_acc_conv2_weights[1]\n",
        "\n",
        "# # bias_shape = high_acc_conv2_weights[1].shape\n",
        "# j = 0\n",
        "# for ch in range(0,high_acc_conv2_weights[1].shape[0]):\n",
        "# #   print (ch)\n",
        "#   if ch in top_channels:\n",
        "#     temp_bias_top_10_DI[j] = high_acc_conv2_weights[1][ch]\n",
        "#     j = j+1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPV28w1jFulD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_bias\n",
        "# weights_to_assign_fromHighAcc = [temp_weights_top_10_DI,temp_bias_top_10_DI]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pYigm1qFJmK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_weights_top_10_DI.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1t2Yj3-E01G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_acc_conv2_weights[1].shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQKKEkR2F-tx",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knA2xBI4jrZF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZ9nBYl-h9SD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # preset the weights\n",
        "# for l1,l2 in zip(modelFilt.layers,loaded_model_low_accuracy.layers):\n",
        "#     l1.set_weights(l2.get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGIUVWh3G4x4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# layer_to_replace = layer_name\n",
        "# low_acc_conv2d_weights_bias = modelFilt.get_layer(layer_to_replace).get_weights()\n",
        "# low_acc_conv2d_weights = low_acc_conv2d_weights_bias[0]\n",
        "# low_acc_conv2d_wbias= low_acc_conv2d_weights_bias[1]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVFju9iFKJID",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_bias_top_10_DI.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlkRaotsHceY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # now change weights for layers corresponding to lowest DI scores\n",
        "# orig_weights = np.copy(low_acc_conv2d_weights)\n",
        "# j = 0\n",
        "# for ch in range(0,orig_weights.shape[3]):\n",
        "#   if ch in low_channels:\n",
        "# #     print(ch)\n",
        "#     orig_weights[:,:,:,ch] = temp_weights_top_10_DI[:,:,:,j]\n",
        "#     j = j+1\n",
        "# # orig_weights.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ye2_DU60K0nL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# orig_bias = np.copy(low_acc_conv2d_wbias)\n",
        "# j = 0\n",
        "# for ch in range(0,orig_bias.shape[0]):\n",
        "#   if ch in low_channels:\n",
        "# #     print(ch)\n",
        "#     orig_bias[ch] = temp_bias_top_10_DI[j]\n",
        "#     j = j+1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDX113kegLSi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RERDrTNALkUA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# new_wt_bias_conv2d1 = [orig_weights,orig_bias]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMc3GS52iEv0",
        "colab_type": "text"
      },
      "source": [
        "#### Overwrite weights fow low DI with high DI"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTUKj97VLfIu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# modelFilt.get_layer(layer_to_replace).set_weights(new_wt_bias_conv2d1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ji5jVmbej-LM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# loaded_model_low_accuracy.layers[5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0w2grlRlKQhJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EozUm-dSIx2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# modelFilt.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRWAVstvGhht",
        "colab_type": "text"
      },
      "source": [
        "#### # now replace weights in conv2d layer for filters correspondng to lowest 10 DI scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcnUQrPtGay_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fev8cENxbr-P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "file_save_name = 'weights_top_k_model2.hdf5'\n",
        "mcp_save = ModelCheckpoint(file_save_name, save_best_only=True, monitor='val_acc', mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BTKJa7Zbwtc",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdiZwWSth97S",
        "colab_type": "code",
        "outputId": "e64b8895-6669-4cb8-d56b-e7878598ea02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.4017736523946127, 0.34199999992052715]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIOMXQsjdSNX",
        "colab_type": "code",
        "outputId": "551cc49e-db8f-4db6-97bd-d9e38be49d64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        }
      },
      "source": [
        "copied_low_accuracy_model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=30,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "187/187 [==============================] - 10s 52ms/step - loss: 0.5754 - acc: 0.8084 - val_loss: 0.6034 - val_acc: 0.8277\n",
            "Epoch 2/30\n",
            "187/187 [==============================] - 10s 52ms/step - loss: 0.4054 - acc: 0.8798 - val_loss: 0.5144 - val_acc: 0.8480\n",
            "Epoch 3/30\n",
            "187/187 [==============================] - 9s 51ms/step - loss: 0.3664 - acc: 0.8935 - val_loss: 0.5044 - val_acc: 0.8483\n",
            "Epoch 4/30\n",
            "187/187 [==============================] - 9s 51ms/step - loss: 0.3477 - acc: 0.8981 - val_loss: 0.4567 - val_acc: 0.8630\n",
            "Epoch 5/30\n",
            "187/187 [==============================] - 9s 51ms/step - loss: 0.3303 - acc: 0.9062 - val_loss: 0.4619 - val_acc: 0.8607\n",
            "Epoch 6/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.3164 - acc: 0.9108 - val_loss: 0.5150 - val_acc: 0.8473\n",
            "Epoch 7/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.3032 - acc: 0.9208 - val_loss: 0.4785 - val_acc: 0.8670\n",
            "Epoch 8/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2945 - acc: 0.9245 - val_loss: 0.4868 - val_acc: 0.8667\n",
            "Epoch 9/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2855 - acc: 0.9245 - val_loss: 0.4673 - val_acc: 0.8633\n",
            "Epoch 10/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2756 - acc: 0.9251 - val_loss: 0.5534 - val_acc: 0.8603\n",
            "Epoch 11/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2702 - acc: 0.9326 - val_loss: 0.5140 - val_acc: 0.8670\n",
            "Epoch 12/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2585 - acc: 0.9356 - val_loss: 0.4975 - val_acc: 0.8600\n",
            "Epoch 13/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2587 - acc: 0.9341 - val_loss: 0.4973 - val_acc: 0.8720\n",
            "Epoch 14/30\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.2578 - acc: 0.9388 - val_loss: 0.4614 - val_acc: 0.8740\n",
            "Epoch 15/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2526 - acc: 0.9363 - val_loss: 0.4564 - val_acc: 0.8770\n",
            "Epoch 16/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2449 - acc: 0.9413 - val_loss: 0.4571 - val_acc: 0.8810\n",
            "Epoch 17/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2391 - acc: 0.9429 - val_loss: 0.4505 - val_acc: 0.8823\n",
            "Epoch 18/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2445 - acc: 0.9424 - val_loss: 0.4612 - val_acc: 0.8780\n",
            "Epoch 19/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2316 - acc: 0.9465 - val_loss: 0.5101 - val_acc: 0.8623\n",
            "Epoch 20/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2263 - acc: 0.9489 - val_loss: 0.5172 - val_acc: 0.8743\n",
            "Epoch 21/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2262 - acc: 0.9506 - val_loss: 0.5091 - val_acc: 0.8777\n",
            "Epoch 22/30\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.2198 - acc: 0.9525 - val_loss: 0.4981 - val_acc: 0.8763\n",
            "Epoch 23/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2114 - acc: 0.9526 - val_loss: 0.4711 - val_acc: 0.8793\n",
            "Epoch 24/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2115 - acc: 0.9556 - val_loss: 0.5847 - val_acc: 0.8753\n",
            "Epoch 25/30\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2097 - acc: 0.9550 - val_loss: 0.5195 - val_acc: 0.8780\n",
            "Epoch 26/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2032 - acc: 0.9576 - val_loss: 0.5438 - val_acc: 0.8787\n",
            "Epoch 27/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2100 - acc: 0.9552 - val_loss: 0.4989 - val_acc: 0.8800\n",
            "Epoch 28/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2011 - acc: 0.9592 - val_loss: 0.5358 - val_acc: 0.8807\n",
            "Epoch 29/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1964 - acc: 0.9596 - val_loss: 0.5171 - val_acc: 0.8720\n",
            "Epoch 30/30\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1952 - acc: 0.9616 - val_loss: 0.5461 - val_acc: 0.8733\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f8380eb1160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAnK7D_Mm_dz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_low_accuracy_model.load_weights(file_save_name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8ZXIw1Um_ao",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# copied_low_accuracy_model.save('final1_'+str(K)+'_'+file_save_name)\n",
        "# copied_low_accuracy_model.save('final1_Transfer1')\n",
        "copied_low_accuracy_model.save('final1_Random1')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZsEELS6m_WM",
        "colab_type": "code",
        "outputId": "6f4afefc-895b-4e37-849b-6846dab1f127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "accuracy = copied_low_accuracy_model.history.history['acc']\n",
        "val_accuracy = copied_low_accuracy_model.history.history['val_acc']\n",
        "loss = copied_low_accuracy_model.history.history['loss']\n",
        "val_loss = copied_low_accuracy_model.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": 164,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bM2+eDeY9fz48+aT1qsmQkGBt9sOH+0VW5/LiQd8dVH/+aXeyjhoF//d/1qOm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\nXGsLnTAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MrvJf/4pgowMHms2ooH/C9HQL61rQa/7wQ90/SalLF4saGM5g5u7dB0If/PWv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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S9iJ-HGnRFW",
        "colab_type": "code",
        "outputId": "9e3e1d88-3a54-483b-ed74-ac4b23fabc91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.45054046758015953, 0.8823333333333333]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8o16S6NPmfn",
        "colab_type": "code",
        "outputId": "7914c19e-f71b-426e-bf34-bbd1e8aaa711",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#testing\n",
        "test_eval = copied_low_accuracy_model.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])\n",
        "\n"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.46544078572591147\n",
            "Test accuracy: 0.8886666668256124\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRuSLttWnRCR",
        "colab_type": "code",
        "outputId": "3f087696-283d-49af-96c5-9416e5a7726d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = layer_to_replace\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_to_replace)\n",
        "\n",
        "plt.ylim([0,1.0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5702386197235612\n",
            "STD of DI Score:  0.059415098660331\n",
            "MAX of DI Score:  0.6984114818796965\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iufJCEXEWcBbAhAkTor+L7DR9ef52O5wL3un/ZNZe/H5qrMpQWASMKY2PztPKFgK3RsTr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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI4AzYZDnQ_X",
        "colab_type": "code",
        "outputId": "bf40213b-79ab-434c-9780-1b9e161b182b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = 'lowAccuracyModelConv2_1'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_to_replace)\n",
        "\n",
        "plt.ylim([0,1.0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5441455605104493\n",
            "STD of DI Score:  0.07125697376575997\n",
            "MAX of DI Score:  0.6857804932152856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZCguAMaXx0bmtbD5wa0S8BTwq6SFSSNxeYV1mLeXgsHbW1DEFSStJ+tASLvt2YJyksZKG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\nUDAzs4JDwczMCg4FMzMr/C+wNKsqDlWHzAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9AxL2k2cPvs",
        "colab_type": "code",
        "outputId": "05b2cbc6-ec55-4ffc-f3b7-47a0d819c614",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_1'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5324752079826454\n",
            "STD of DI Score:  0.08518245611608953\n",
            "MAX of DI Score:  0.6952950607026254\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmomNWqXclu8",
        "colab_type": "code",
        "outputId": "3a234d4d-d4e1-4831-c7a8-59acc185d0d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_2'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5472756918767483\n",
            "STD of DI Score:  0.07193005751902407\n",
            "MAX of DI Score:  0.7008227385646286\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1iC11tO-c1ME",
        "colab_type": "code",
        "outputId": "d5e2ca0b-926e-4343-8a92-b002380a5ba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_3'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.4283726311323016\n",
            "STD of DI Score:  0.11606436878312644\n",
            "MAX of DI Score:  0.7211856134234298\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PYfByEs+iaT6ivbjdXX0WpfExzl/exTBpm/7S/pprS+HzSd5Kczbyw4GPAgyeyCZJS4kr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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3SsO1CXc09Q",
        "colab_type": "code",
        "outputId": "80ab8bbd-d50a-4858-f8aa-e0e29cf392d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_4'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.4676357866426307\n",
            "STD of DI Score:  0.129639936407154\n",
            "MAX of DI Score:  0.9082884744169848\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Unii3onHcpx6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2XDOWtOP2p7",
        "colab_type": "code",
        "outputId": "5ba31b93-776d-4b0a-87ca-a773a09b26d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "modelFilt.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",
            "lowAccuracyModelConv1_1 (Con (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "lowAccuracyModelBN1 (BatchNo (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "lowAccuracyModelConv2_1 (Con (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (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_1 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (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_3 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (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"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxnMxP6cTGLO",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    }
  ]
}