TariqTransferRandomDIHighestValid.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TariqTransferRandomDIHighestValid.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/KProj/blob/master/TariqTransferRandomDIHighestValid.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": "ecb83d60-4724-4f0b-ad1e-a1be11946c28",
        "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": 334,
      "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": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD8CAYAAAA2Y2wxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "640de145-efb8-445c-8d92-21d2c1a13765",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_three_label"
      ],
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 3, 5, ..., 0, 5, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCQfV4TvZh1r",
        "colab_type": "code",
        "outputId": "66fc1a0c-448b-4bf6-f11e-b7763034147e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_three_label"
      ],
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 5, 0, ..., 5, 0, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gK_tiIWd2Nf",
        "colab_type": "code",
        "outputId": "34d1daa0-5c31-40a7-a0e6-234a32badd4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 340,
      "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": 340
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/gESZomJ0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEYcIMeqd4i1",
        "colab_type": "code",
        "outputId": "c5fde77c-abf5-4c4c-8e0c-50e9a8d9f8cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "plt.hist(test_three_label)"
      ],
      "execution_count": 341,
      "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": 341
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAADfxJREFUeJzt3X+o3fV9x/Hna6b2h92MPy4hS9Jd\noaFDCptycQ5HGWZz/iiNf7Ri2doggfzjNjsHbbp/ZNs/FUZthSGExi0y0Yp1GFppFzSlCDPtjbW2\nmnZenDYJam7rj9aV0mV974/zsbtLExPv995z6v08H3C53+/n+znn+zka87zne885pqqQJPXn1ya9\nAEnSZBgASeqUAZCkThkASeqUAZCkThkASeqUAZCkThkASeqUAZCkTq2a9AJez7nnnlvT09OTXoYk\nvans37//B1U1dbJ5v9IBmJ6eZnZ2dtLLkKQ3lSTPnso8LwFJUqcMgCR1ygBIUqcMgCR1ygBIUqdO\nGoAktyc5kuQ7C8bOTrInyVPt+1ltPEluTTKX5PEkFy64zZY2/6kkW5bn4UiSTtWpPAP4Z+DyY8a2\nAw9W1UbgwbYPcAWwsX1tA26DUTCAm4DfAy4CbnotGpKkyThpAKrqa8CLxwxvBna17V3A1QvG76iR\nR4DVSdYCfwLsqaoXq+olYA+/HBVJ0hgt9ncAa6rqubb9PLCmba8DDi6Yd6iNnWhckjQhg98JXFWV\nZMn+z/JJtjG6fMS73vWuQfc1vf1LS7GkN+yZT101kfOqD5P6cw39/dle6f+sF/sM4IV2aYf2/Ugb\nPwxsWDBvfRs70fgvqaodVTVTVTNTUyf9KAtJ0iItNgC7gddeybMFuH/B+Efbq4EuBl5pl4q+AlyW\n5Kz2y9/L2pgkaUJOegkoyV3AHwLnJjnE6NU8nwLuSbIVeBa4pk1/ALgSmAN+AlwHUFUvJvl74Btt\n3t9V1bG/WJYkjdFJA1BVHz7BoU3HmVvA9Se4n9uB29/Q6iRJy8Z3AktSpwyAJHXKAEhSpwyAJHXK\nAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhS\npwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyA\nJHXKAEhSpwyAJHVqUACS/FWSJ5J8J8ldSd6W5Lwk+5LMJfl8ktPb3Le2/bl2fHopHoAkaXEWHYAk\n64C/BGaq6r3AacC1wM3ALVX1buAlYGu7yVbgpTZ+S5snSZqQoZeAVgFvT7IKeAfwHHApcG87vgu4\num1vbvu045uSZOD5JUmLtOgAVNVh4B+A7zP6i/8VYD/wclUdbdMOAeva9jrgYLvt0Tb/nMWeX5I0\nzJBLQGcx+qn+POA3gTOAy4cuKMm2JLNJZufn54fenSTpBIZcAvoj4D+rar6q/hu4D7gEWN0uCQGs\nBw637cPABoB2/Ezgh8feaVXtqKqZqpqZmpoasDxJ0usZEoDvAxcneUe7lr8JeBLYC3ywzdkC3N+2\nd7d92vGHqqoGnF+SNMCQ3wHsY/TL3EeBb7f72gF8ArgxyRyja/w72012Aue08RuB7QPWLUkaaNXJ\np5xYVd0E3HTM8NPARceZ+1PgQ0POJ0laOr4TWJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6\nZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAk\nqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMGQJI6ZQAkqVMG\nQJI6NSgASVYnuTfJd5McSPL7Sc5OsifJU+37WW1uktyaZC7J40kuXJqHIElajKHPAD4LfLmqfhv4\nHeAAsB14sKo2Ag+2fYArgI3taxtw28BzS5IGWHQAkpwJvA/YCVBVP6uql4HNwK42bRdwddveDNxR\nI48Aq5OsXfTKJUmDDHkGcB4wD/xTkm8m+VySM4A1VfVcm/M8sKZtrwMOLrj9oTYmSZqAIQFYBVwI\n3FZVFwD/xf9d7gGgqgqoN3KnSbYlmU0yOz8/P2B5kqTXMyQAh4BDVbWv7d/LKAgvvHZpp30/0o4f\nBjYsuP36Nvb/VNWOqpqpqpmpqakBy5MkvZ5FB6CqngcOJnlPG9oEPAnsBra0sS3A/W17N/DR9mqg\ni4FXFlwqkiSN2aqBt/8L4M4kpwNPA9cxiso9SbYCzwLXtLkPAFcCc8BP2lxJ0oQMCkBVPQbMHOfQ\npuPMLeD6IeeTJC0d3wksSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLU\nKQMgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUKQMg\nSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUKQMgSZ0yAJLUqcEBSHJakm8m+WLbPy/J\nviRzST6f5PQ2/ta2P9eOTw89tyRp8ZbiGcANwIEF+zcDt1TVu4GXgK1tfCvwUhu/pc2TJE3IoAAk\nWQ9cBXyu7Qe4FLi3TdkFXN22N7d92vFNbb4kaQKGPgP4DPBx4Odt/xzg5ao62vYPAeva9jrgIEA7\n/kqbL0magEUHIMn7gSNVtX8J10OSbUlmk8zOz88v5V1LkhYY8gzgEuADSZ4B7mZ06eezwOokq9qc\n9cDhtn0Y2ADQjp8J/PDYO62qHVU1U1UzU1NTA5YnSXo9iw5AVX2yqtZX1TRwLfBQVf0psBf4YJu2\nBbi/be9u+7TjD1VVLfb8kqRhluN9AJ8Abkwyx+ga/842vhM4p43fCGxfhnNLkk7RqpNPObmq+irw\n1bb9NHDRceb8FPjQUpxPkjSc7wSWpE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQ\npE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4Z\nAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4ZAEnqlAGQpE4tOgBJ\nNiTZm+TJJE8kuaGNn51kT5Kn2vez2niS3JpkLsnjSS5cqgchSXrjhjwDOAr8dVWdD1wMXJ/kfGA7\n8GBVbQQebPsAVwAb29c24LYB55YkDbToAFTVc1X1aNv+MXAAWAdsBna1abuAq9v2ZuCOGnkEWJ1k\n7aJXLkkaZEl+B5BkGrgA2Aesqarn2qHngTVtex1wcMHNDrWxY+9rW5LZJLPz8/NLsTxJ0nEMDkCS\ndwJfAD5WVT9aeKyqCqg3cn9VtaOqZqpqZmpqaujyJEknMCgASd7C6C//O6vqvjb8wmuXdtr3I238\nMLBhwc3XtzFJ0gQMeRVQgJ3Agar69IJDu4EtbXsLcP+C8Y+2VwNdDLyy4FKRJGnMVg247SXAR4Bv\nJ3msjf0N8CngniRbgWeBa9qxB4ArgTngJ8B1A84tSRpo0QGoqoeBnODwpuPML+D6xZ5PkrS0fCew\nJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXK\nAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhS\npwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSp8YegCSXJ/lekrkk28d9fknSyFgDkOQ04B+BK4Dz\ngQ8nOX+ca5AkjYz7GcBFwFxVPV1VPwPuBjaPeQ2SJMYfgHXAwQX7h9qYJGnMVk16AcdKsg3Y1nZf\nTfK9AXd3LvCD4at6Y3LzuM/4CxN5vBPmYx4j/2yPT24e9Jh/61QmjTsAh4ENC/bXt7FfqKodwI6l\nOFmS2aqaWYr7ejPo7fGCj7kXPublMe5LQN8ANiY5L8npwLXA7jGvQZLEmJ8BVNXRJH8OfAU4Dbi9\nqp4Y5xokSSNj/x1AVT0APDCm0y3JpaQ3kd4eL/iYe+FjXgapquU+hyTpV5AfBSFJnVqRAejt4yaS\n3J7kSJLvTHot45JkQ5K9SZ5M8kSSGya9puWW5G1Jvp7kW+0x/+2k1zQOSU5L8s0kX5z0WsYlyTNJ\nvp3ksSSzy3aelXYJqH3cxH8Af8zojWbfAD5cVU9OdGHLKMn7gFeBO6rqvZNezzgkWQusrapHk/w6\nsB+4eoX/ew5wRlW9muQtwMPADVX1yISXtqyS3AjMAL9RVe+f9HrGIckzwExVLet7H1biM4DuPm6i\nqr4GvDjpdYxTVT1XVY+27R8DB1jh7yqvkVfb7lva18r6Ce4YSdYDVwGfm/RaVqKVGAA/bqIzSaaB\nC4B9k13J8muXQx4DjgB7qmqlP+bPAB8Hfj7phYxZAf+WZH/7dIRlsRIDoI4keSfwBeBjVfWjSa9n\nuVXV/1TV7zJ6F/1FSVbsJb8k7weOVNX+Sa9lAv6gqi5k9MnJ17fLvEtuJQbgpB83oZWhXQf/AnBn\nVd036fWMU1W9DOwFLp/0WpbRJcAH2vXwu4FLk/zLZJc0HlV1uH0/Avwro0vbS24lBsCPm+hA+4Xo\nTuBAVX160usZhyRTSVa37bczeqHDdye7quVTVZ+sqvVVNc3ov+OHqurPJrysZZfkjPbCBpKcAVwG\nLMsr/FZcAKrqKPDax00cAO5Z6R83keQu4N+B9yQ5lGTrpNc0BpcAH2H0U+Fj7evKSS9qma0F9iZ5\nnNEPOnuqqpuXRnZkDfBwkm8BXwe+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",
        "outputId": "3daeaa2e-4df2-4921-da84-6f7607429aca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "train_ground"
      ],
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 1.],\n",
              "       [0., 0., 1.],\n",
              "       [1., 0., 0.],\n",
              "       ...,\n",
              "       [0., 1., 0.],\n",
              "       [1., 0., 0.],\n",
              "       [0., 1., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 346
        }
      ]
    },
    {
      "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": "MMuuJ3IcDxxN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "enable_training = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ich0RbYnebwW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cifar10ClassifierPruned(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',trainable = enable_training))(input_img)\n",
        "  conv1_2 = BatchNormalization(name = 'conv1_2',trainable = enable_training)(conv1_1)\n",
        "  pool1_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool1_3',trainable = enable_training)(conv1_2) #14 x 14 x 32\n",
        "  conv2_1 = (Conv2D(128, (3,3),name = 'conv2_1' ,trainable = enable_training,padding='same',\n",
        "                    activation= 'relu',kernel_regularizer=regularizers.l2(weight_decay)))(pool1_3)\n",
        "  conv2_2 = BatchNormalization(name = 'conv2_2',trainable = enable_training)(conv2_1)\n",
        "  pool2_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool2_3',trainable = enable_training)(conv2_2) #14 x 14 x 32\n",
        "  \n",
        "  conv3_1 = (Conv2D(256, (3,3),trainable = enable_training, padding='same',name = 'conv3_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool2_3)\n",
        "  conv3_2 = BatchNormalization(name = 'conv3_2',trainable = enable_training)(conv3_1)\n",
        "  conv4_1 = (Conv2D(256, (3,3), trainable = enable_training,padding='same',name = 'conv4_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "  conv4_2 = BatchNormalization(name = 'conv4_2',trainable = enable_training)(conv4_1)\n",
        "  pool4_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool4_2',trainable = enable_training)(conv4_2) #14 x 14 x 32\n",
        "  \n",
        "  conv5_1 = (Conv2D(512, (3,3), padding='same',name = 'conv5_1',trainable = enable_training,\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool4_2)\n",
        "  conv5_2 = BatchNormalization(name = 'conv5_2',trainable = enable_training)(conv5_1)\n",
        "  conv6_1 = (Conv2D(512, (3,3), padding='same',trainable = enable_training,name = 'conv6_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "  conv6_2 = BatchNormalization(name = 'conv6_2',trainable = enable_training)(conv6_1)\n",
        "  pool6_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool6_2',trainable = enable_training)(conv6_2) #14 x 14 x 32\n",
        "  \n",
        "  conv7_1 = (Conv2D(512, (3,3), padding='same',name = 'conv7_1',trainable = enable_training,\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool6_2)\n",
        "  conv7_2 = BatchNormalization(name = 'conv7_2',trainable = enable_training)(conv7_1)\n",
        "  conv8_1 = (Conv2D(256, (3,3), padding='same',name = 'conv8_1' ,trainable = enable_training,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": "4KLI4xHpMlS9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compute_di_score(features,Y):\n",
        "    '''\n",
        "    Computes DI Score of a Neural Network Output\n",
        "    For convolution layer, the DI score of each channel is computed\n",
        "    For fully connected layer, the DI score of each node is computed\n",
        "    \n",
        "    Input:\n",
        "    features: shape: convolution: Ne,H,W,Nf\n",
        "                     fully connected: Ne,Nf\n",
        "                     Ne = number of example\n",
        "                     Nf = number of channels/filters\n",
        "    Y: shape: Ne,n_class\n",
        "                One hot encoded array. Each row contains one example.\n",
        "    \n",
        "    Output:\n",
        "    dis: shape: (Nf,), di sore of each of the filters.\n",
        "    '''\n",
        "    fshape = len(features.shape)\n",
        "    \n",
        "    #Taking the features to Appropriate Shape\n",
        "    if fshape==4:\n",
        "        Ne,H,W,Nf = features.shape\n",
        "        features = features.transpose(3,0,1,2) #Nf, Ne, H,W\n",
        "        features = features.reshape(Nf,Ne,-1) #Nf, Ne, HxW\n",
        "        features = features.transpose(0,2,1) # Nf, HxW, Ne\n",
        "        d = H*W\n",
        "    elif fshape==2:\n",
        "        Ne,Nf = features.shape\n",
        "        features = features.transpose(1,0) #Nf, Ne\n",
        "        features = np.expand_dims(features,axis=1)\n",
        "        d = 1\n",
        "    \n",
        "    #Normalizing the Y\n",
        "    Y = Y.T\n",
        "    N_number = np.sqrt(np.array([np.sum(Y,axis=1)]).T)\n",
        "    Y = Y/N_number\n",
        "    \n",
        "    dis = np.zeros(Nf)\n",
        "\n",
        "    rho = 10**-8\n",
        "    for i in range(Nf):\n",
        "        X = features[i,:,:]\n",
        "        \n",
        "        dis[i] = np.trace(np.linalg.inv(X.dot(X.T)+rho*np.eye(d)).dot(X).dot(Y.T).dot(Y).dot(X.T))\n",
        "        \n",
        "        \n",
        "    return dis"
      ],
      "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 = compute_di_score(outputs_filter_reshaped, true_labels)\n",
        "    cnn_di_scores.append(this_filter_score)\n",
        "  return cnn_di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2w2HyV5MWn1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "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_X , data_label =train_ground ):\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 = compute_di_score(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": "3cd24d7a-37ff-4353-ccee-361fea63cf01",
        "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": 355,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.45540413411458336, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 355
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rddbxdrtXcR7",
        "colab_type": "code",
        "outputId": "fb7e426e-f929-429e-ba28-e19b20182e21",
        "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": 356,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4554041388829549, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 356
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsXuB6NbXmGB",
        "colab_type": "code",
        "outputId": "51951d3e-6cf2-4358-a437-44fdf3dd49bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "valid_ground"
      ],
      "execution_count": 357,
      "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": 357
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVSvyoZ8Uw_y",
        "colab_type": "code",
        "outputId": "d9d91ea8-afae-4427-cf84-97edc42d533f",
        "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": 358,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.45106768385569257, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 358
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAY0LGCNSMBc",
        "colab_type": "code",
        "outputId": "1770143e-f1b2-4afd-9f19-3862a708ec7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# loaded_model_low_accuracy.load_weights('weights.hdf5')\n",
        "# loaded_model_low_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n",
        "loaded_model_high_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n"
      ],
      "execution_count": 359,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.8258795102437335, 0.3953333332538605]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 359
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jsm88PV3TlmL",
        "colab_type": "code",
        "outputId": "f6adb962-0260-455a-f7f9-5410ff36c681",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_high_accuracy.evaluate(test_three_data, test_three_label_one_hot, verbose=0)"
      ],
      "execution_count": 360,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.126735850016276, 0.3673333334128062]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 360
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-dQDj8vPnXC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7c-A-a4SS6S",
        "colab_type": "code",
        "outputId": "b45a8a2c-447a-4689-9f12-acf91ca80bc7",
        "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": 361,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.45106768385569257, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 361
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzbIXvcEoNMj",
        "colab_type": "code",
        "outputId": "3bed470c-edd4-4745-9227-67ba4a8dcd56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        }
      },
      "source": [
        "loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 362,
      "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": "1e058ded-f418-40f1-908f-cd4349f442b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "# layer_names = ['conv2_1']\n",
        "layer_names = ['conv8_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": 364,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  1.331331297427941\n",
            "STD of DI Score:  0.9772967565250594\n",
            "MAX of DI Score:  2.616307017978204\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\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": "f954d8b9-8655-4dd3-a75d-3192b3b26cc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "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": 367,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.4508742286708036\n",
            "STD of DI Score:  0.40304218211343\n",
            "MAX of DI Score:  1.19291369866481\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\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",
        "outputId": "007a9c8a-e30d-4723-cafd-8ee8f06c7c1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "for i,d in enumerate(all_di_acores_high_accuracy):\n",
        "  print(len(d),i)"
      ],
      "execution_count": 373,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "512 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1cAvZpx9G8_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Ks = [128*2]"
      ],
      "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": "mCeCjHCzbJj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# low_accuracy_model_weight.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Va1DmrzzY1Z_",
        "colab_type": "code",
        "outputId": "866fb736-97fd-4766-f247-211e665b8914",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        }
      },
      "source": [
        "prunedModel = Model(input_img, (cifar10ClassifierPruned(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",
        "# opt_rms = keras.optimizers.Adam(lr=0.001,decay=1e-6,beta_1=0.5)\n",
        "# model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "prunedModel.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "prunedModel.summary()"
      ],
      "execution_count": 378,
      "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, 256)         1179904   \n",
            "_________________________________________________________________\n",
            "conv8_2 (BatchNormalization) (None, 2, 2, 256)         1024      \n",
            "_________________________________________________________________\n",
            "pool8_2 (MaxPooling2D)       (None, 1, 1, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              1052672   \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: 29,984,547\n",
            "Trainable params: 29,979,555\n",
            "Non-trainable params: 4,992\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "koxUAwE_a0RE",
        "colab_type": "code",
        "outputId": "ac86c9b5-7efb-4421-feb1-c9bf05fad036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "yy = prunedModel.get_layer('conv8_1').get_weights()\n",
        "len(yy)\n",
        "print(yy[0].shape)\n",
        "print(yy[1].shape)\n",
        "# print(yy[2].shape)\n",
        "# print(yy[3].shape)"
      ],
      "execution_count": 379,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3, 3, 512, 256)\n",
            "(256,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPPUBw49dpTH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# this_layer_name\n",
        "layer_names_to_transfer = [] \n",
        "for layer in prunedModel.layers:\n",
        "    layer_names_to_transfer.append(layer.name)\n",
        "    if (layer.name == 'conv7_2'):\n",
        "      break\n",
        "#     print(layer.name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDP4eyRPgxLH",
        "colab_type": "code",
        "outputId": "cff275ef-8e18-475b-bc09-aeac83d7f5ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "source": [
        "layer_names_to_transfer"
      ],
      "execution_count": 381,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_1',\n",
              " 'conv1_1',\n",
              " 'conv1_2',\n",
              " 'pool1_3',\n",
              " 'conv2_1',\n",
              " 'conv2_2',\n",
              " 'pool2_3',\n",
              " 'conv3_1',\n",
              " 'conv3_2',\n",
              " 'conv4_1',\n",
              " 'conv4_2',\n",
              " 'pool4_2',\n",
              " 'conv5_1',\n",
              " 'conv5_2',\n",
              " 'conv6_1',\n",
              " 'conv6_2',\n",
              " 'pool6_2',\n",
              " 'conv7_1',\n",
              " 'conv7_2']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 381
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qMvf_sig69z",
        "colab_type": "code",
        "outputId": "00e2ed4f-9eaa-4944-8f3c-6b73dda8c2c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# temp_wt.shape\n",
        "Ks"
      ],
      "execution_count": 382,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[256]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 382
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFmu48r41ch0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "223a68cd-6c6c-4077-e71c-dfae405d3e97"
      },
      "source": [
        "len(bottom_channels)"
      ],
      "execution_count": 387,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 387
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91d4crRj1BfO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e6f39606-e543-463e-dab8-c690ce281b29"
      },
      "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.flip(np.argsort(this_high_accuracy_di_scores))\n",
        "  this_high_accuracy_di_scores_sorted_values = np.flip(np.sort(this_high_accuracy_di_scores))\n",
        "\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",
        "  this_low_accuracy_di_scores_sorted_list_values = np.sort(this_low_accuracy_di_scores)\n",
        "  \n",
        "  N_j = len(this_high_accuracy_di_scores_sorted_values)\n",
        "  \n",
        "  for idx in range(N_j):\n",
        "    if this_high_accuracy_di_scores_sorted_values[idx] < this_low_accuracy_di_scores_sorted_list_values[idx]:\n",
        "      no_id = idx\n",
        "      break\n",
        "  top_channels = this_high_accuracy_di_scores_sorted_list[:no_id]\n",
        "  bottom_channels = this_low_accuracy_di_scores_sorted_list[-(256-no_id):]  \n",
        "  print(len(bottom_channels),len(top_channels))\n",
        "\n",
        "  top_channels = np.random.choice(this_high_accuracy_di_scores_sorted_list,1)\n",
        "  bottom_channels = np.random.choice(this_low_accuracy_di_scores_sorted_list,255)\n",
        "#   print(len(bottom_channels),len(top_channels))\n",
        "#   bottom_channels = this_low_accuracy_di_scores_sorted_list[-Ks[l]:] \n",
        "  \n",
        "  \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",
        "  \n",
        "#   temp_wt =high_accuracy_model_weight[:,:,:,top_channels]# np.concatenate((low_accuracy_model_weight[:,:,:,bottom_channels],high_accuracy_model_weight[:,:,:,top_channels] ), axis = 3)\n",
        "#   temp_bias = high_accuracy_model_bias[top_channels] #np.concatenate((low_accuracy_model_bias[bottom_channels],high_accuracy_model_bias[top_channels] ))\n",
        "  \n",
        "#   temp_wt = np.concatenate((low_accuracy_model_weight[:,:,:,bottom_channels],high_accuracy_model_weight[:,:,:,top_channels] ), axis = 3)\n",
        "  temp_wt = np.concatenate((high_accuracy_model_weight[:,:,:,top_channels],low_accuracy_model_weight[:,:,:,bottom_channels] ), axis = 3)\n",
        "\n",
        "#   temp_bias =np.concatenate((low_accuracy_model_bias[bottom_channels],high_accuracy_model_bias[top_channels] ))\n",
        "  \n",
        "  temp_bias =np.concatenate((high_accuracy_model_bias[top_channels],low_accuracy_model_bias[bottom_channels] ))\n",
        "\n",
        "  pmod = prunedModel.get_layer(this_layer_name).get_weights()\n",
        "  \n",
        "  prunedModel.get_layer(this_layer_name).set_weights([temp_wt, temp_bias])"
      ],
      "execution_count": 388,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "89 167\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtQ_qDHE16LM",
        "colab_type": "code",
        "outputId": "2ec9e5e0-5e84-4a88-892e-31bfe4b06e31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "yy = np.where(np.array(this_high_accuracy_di_scores)<1e-3)\n",
        "yy[0].shape"
      ],
      "execution_count": 389,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(184,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 389
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1q2Z39311u8C",
        "colab_type": "code",
        "outputId": "751eb536-b3dc-4c77-920a-3d2658b82542",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8892
        }
      },
      "source": [
        "this_high_accuracy_di_scores_sorted_list"
      ],
      "execution_count": 390,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[418,\n",
              " 19,\n",
              " 303,\n",
              " 251,\n",
              " 18,\n",
              " 454,\n",
              " 223,\n",
              " 384,\n",
              " 37,\n",
              " 354,\n",
              " 228,\n",
              " 505,\n",
              " 440,\n",
              " 162,\n",
              " 93,\n",
              " 268,\n",
              " 216,\n",
              " 381,\n",
              " 477,\n",
              " 186,\n",
              " 422,\n",
              " 79,\n",
              " 235,\n",
              " 482,\n",
              " 274,\n",
              " 349,\n",
              " 405,\n",
              " 89,\n",
              " 351,\n",
              " 167,\n",
              " 168,\n",
              " 63,\n",
              " 65,\n",
              " 94,\n",
              " 195,\n",
              " 193,\n",
              " 45,\n",
              " 453,\n",
              " 141,\n",
              " 289,\n",
              " 77,\n",
              " 441,\n",
              " 17,\n",
              " 131,\n",
              " 24,\n",
              " 134,\n",
              " 350,\n",
              " 367,\n",
              " 413,\n",
              " 202,\n",
              " 194,\n",
              " 212,\n",
              " 324,\n",
              " 12,\n",
              " 487,\n",
              " 430,\n",
              " 151,\n",
              " 99,\n",
              " 173,\n",
              " 469,\n",
              " 427,\n",
              " 177,\n",
              " 322,\n",
              " 96,\n",
              " 511,\n",
              " 183,\n",
              " 47,\n",
              " 182,\n",
              " 298,\n",
              " 444,\n",
              " 343,\n",
              " 230,\n",
              " 164,\n",
              " 420,\n",
              " 392,\n",
              " 285,\n",
              " 20,\n",
              " 483,\n",
              " 107,\n",
              " 338,\n",
              " 451,\n",
              " 339,\n",
              " 465,\n",
              " 375,\n",
              " 300,\n",
              " 60,\n",
              " 417,\n",
              " 64,\n",
              " 491,\n",
              " 2,\n",
              " 473,\n",
              " 0,\n",
              " 369,\n",
              " 59,\n",
              " 150,\n",
              " 290,\n",
              " 271,\n",
              " 330,\n",
              " 265,\n",
              " 478,\n",
              " 226,\n",
              " 180,\n",
              " 509,\n",
              " 118,\n",
              " 214,\n",
              " 414,\n",
              " 493,\n",
              " 293,\n",
              " 205,\n",
              " 115,\n",
              " 102,\n",
              " 143,\n",
              " 321,\n",
              " 101,\n",
              " 459,\n",
              " 66,\n",
              " 244,\n",
              " 231,\n",
              " 314,\n",
              " 210,\n",
              " 370,\n",
              " 241,\n",
              " 179,\n",
              " 90,\n",
              " 207,\n",
              " 139,\n",
              " 229,\n",
              " 4,\n",
              " 128,\n",
              " 401,\n",
              " 446,\n",
              " 125,\n",
              " 316,\n",
              " 140,\n",
              " 30,\n",
              " 219,\n",
              " 153,\n",
              " 245,\n",
              " 325,\n",
              " 348,\n",
              " 301,\n",
              " 1,\n",
              " 347,\n",
              " 188,\n",
              " 260,\n",
              " 49,\n",
              " 362,\n",
              " 377,\n",
              " 138,\n",
              " 191,\n",
              " 122,\n",
              " 7,\n",
              " 120,\n",
              " 335,\n",
              " 383,\n",
              " 334,\n",
              " 294,\n",
              " 470,\n",
              " 9,\n",
              " 159,\n",
              " 395,\n",
              " 78,\n",
              " 36,\n",
              " 87,\n",
              " 363,\n",
              " 372,\n",
              " 272,\n",
              " 280,\n",
              " 25,\n",
              " 23,\n",
              " 82,\n",
              " 248,\n",
              " 296,\n",
              " 156,\n",
              " 227,\n",
              " 39,\n",
              " 232,\n",
              " 62,\n",
              " 201,\n",
              " 501,\n",
              " 261,\n",
              " 488,\n",
              " 104,\n",
              " 495,\n",
              " 246,\n",
              " 190,\n",
              " 11,\n",
              " 426,\n",
              " 497,\n",
              " 222,\n",
              " 328,\n",
              " 374,\n",
              " 218,\n",
              " 500,\n",
              " 106,\n",
              " 415,\n",
              " 423,\n",
              " 221,\n",
              " 463,\n",
              " 472,\n",
              " 73,\n",
              " 462,\n",
              " 358,\n",
              " 331,\n",
              " 492,\n",
              " 434,\n",
              " 5,\n",
              " 10,\n",
              " 206,\n",
              " 88,\n",
              " 15,\n",
              " 14,\n",
              " 85,\n",
              " 72,\n",
              " 44,\n",
              " 424,\n",
              " 275,\n",
              " 510,\n",
              " 185,\n",
              " 163,\n",
              " 165,\n",
              " 197,\n",
              " 105,\n",
              " 146,\n",
              " 448,\n",
              " 71,\n",
              " 302,\n",
              " 458,\n",
              " 61,\n",
              " 447,\n",
              " 307,\n",
              " 135,\n",
              " 403,\n",
              " 273,\n",
              " 56,\n",
              " 176,\n",
              " 46,\n",
              " 504,\n",
              " 204,\n",
              " 326,\n",
              " 411,\n",
              " 416,\n",
              " 297,\n",
              " 480,\n",
              " 211,\n",
              " 494,\n",
              " 355,\n",
              " 103,\n",
              " 476,\n",
              " 496,\n",
              " 336,\n",
              " 175,\n",
              " 503,\n",
              " 452,\n",
              " 318,\n",
              " 254,\n",
              " 86,\n",
              " 345,\n",
              " 409,\n",
              " 279,\n",
              " 21,\n",
              " 356,\n",
              " 337,\n",
              " 342,\n",
              " 281,\n",
              " 486,\n",
              " 474,\n",
              " 396,\n",
              " 323,\n",
              " 333,\n",
              " 55,\n",
              " 33,\n",
              " 129,\n",
              " 507,\n",
              " 53,\n",
              " 421,\n",
              " 132,\n",
              " 400,\n",
              " 234,\n",
              " 361,\n",
              " 149,\n",
              " 52,\n",
              " 92,\n",
              " 391,\n",
              " 366,\n",
              " 471,\n",
              " 386,\n",
              " 329,\n",
              " 379,\n",
              " 340,\n",
              " 158,\n",
              " 412,\n",
              " 145,\n",
              " 276,\n",
              " 225,\n",
              " 315,\n",
              " 169,\n",
              " 155,\n",
              " 376,\n",
              " 50,\n",
              " 437,\n",
              " 288,\n",
              " 284,\n",
              " 264,\n",
              " 398,\n",
              " 461,\n",
              " 466,\n",
              " 114,\n",
              " 236,\n",
              " 51,\n",
              " 38,\n",
              " 449,\n",
              " 29,\n",
              " 425,\n",
              " 199,\n",
              " 97,\n",
              " 54,\n",
              " 306,\n",
              " 450,\n",
              " 443,\n",
              " 170,\n",
              " 365,\n",
              " 127,\n",
              " 313,\n",
              " 136,\n",
              " 217,\n",
              " 287,\n",
              " 382,\n",
              " 98,\n",
              " 117,\n",
              " 28,\n",
              " 31,\n",
              " 32,\n",
              " 34,\n",
              " 27,\n",
              " 35,\n",
              " 116,\n",
              " 40,\n",
              " 41,\n",
              " 42,\n",
              " 119,\n",
              " 502,\n",
              " 26,\n",
              " 22,\n",
              " 506,\n",
              " 121,\n",
              " 16,\n",
              " 13,\n",
              " 123,\n",
              " 508,\n",
              " 124,\n",
              " 8,\n",
              " 6,\n",
              " 3,\n",
              " 43,\n",
              " 113,\n",
              " 100,\n",
              " 69,\n",
              " 95,\n",
              " 489,\n",
              " 490,\n",
              " 91,\n",
              " 84,\n",
              " 83,\n",
              " 81,\n",
              " 80,\n",
              " 76,\n",
              " 75,\n",
              " 74,\n",
              " 70,\n",
              " 485,\n",
              " 499,\n",
              " 108,\n",
              " 68,\n",
              " 67,\n",
              " 126,\n",
              " 109,\n",
              " 110,\n",
              " 498,\n",
              " 58,\n",
              " 57,\n",
              " 111,\n",
              " 112,\n",
              " 48,\n",
              " 419,\n",
              " 209,\n",
              " 130,\n",
              " 311,\n",
              " 436,\n",
              " 346,\n",
              " 344,\n",
              " 341,\n",
              " 438,\n",
              " 439,\n",
              " 332,\n",
              " 327,\n",
              " 442,\n",
              " 320,\n",
              " 319,\n",
              " 317,\n",
              " 312,\n",
              " 310,\n",
              " 352,\n",
              " 309,\n",
              " 308,\n",
              " 305,\n",
              " 304,\n",
              " 445,\n",
              " 299,\n",
              " 295,\n",
              " 292,\n",
              " 291,\n",
              " 286,\n",
              " 283,\n",
              " 282,\n",
              " 278,\n",
              " 435,\n",
              " 353,\n",
              " 484,\n",
              " 387,\n",
              " 410,\n",
              " 408,\n",
              " 407,\n",
              " 406,\n",
              " 404,\n",
              " 402,\n",
              " 399,\n",
              " 397,\n",
              " 394,\n",
              " 393,\n",
              " 390,\n",
              " 389,\n",
              " 388,\n",
              " 385,\n",
              " 433,\n",
              " 428,\n",
              " 429,\n",
              " 380,\n",
              " 378,\n",
              " 431,\n",
              " 373,\n",
              " 371,\n",
              " 368,\n",
              " 432,\n",
              " 364,\n",
              " 360,\n",
              " 359,\n",
              " 357,\n",
              " 277,\n",
              " 270,\n",
              " 269,\n",
              " 475,\n",
              " 196,\n",
              " 464,\n",
              " 192,\n",
              " 189,\n",
              " 187,\n",
              " 467,\n",
              " 184,\n",
              " 468,\n",
              " 181,\n",
              " 178,\n",
              " 174,\n",
              " 172,\n",
              " 171,\n",
              " 166,\n",
              " 267,\n",
              " 161,\n",
              " 160,\n",
              " 157,\n",
              " 154,\n",
              " 152,\n",
              " 479,\n",
              " 148,\n",
              " 147,\n",
              " 144,\n",
              " 142,\n",
              " 137,\n",
              " 481,\n",
              " 133,\n",
              " 198,\n",
              " 200,\n",
              " 203,\n",
              " 208,\n",
              " 266,\n",
              " 263,\n",
              " 262,\n",
              " 259,\n",
              " 258,\n",
              " 257,\n",
              " 256,\n",
              " 253,\n",
              " 252,\n",
              " 455,\n",
              " 250,\n",
              " 249,\n",
              " 247,\n",
              " 243,\n",
              " 242,\n",
              " 240,\n",
              " 239,\n",
              " 238,\n",
              " 237,\n",
              " 456,\n",
              " 233,\n",
              " 457,\n",
              " 224,\n",
              " 220,\n",
              " 460,\n",
              " 215,\n",
              " 213,\n",
              " 255]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 390
        }
      ]
    },
    {
      "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",
        "  \n",
        "#   temp_wt =high_accuracy_model_weight[:,:,:,top_channels]# np.concatenate((low_accuracy_model_weight[:,:,:,bottom_channels],high_accuracy_model_weight[:,:,:,top_channels] ), axis = 3)\n",
        "#   temp_bias = high_accuracy_model_bias[top_channels] #np.concatenate((low_accuracy_model_bias[bottom_channels],high_accuracy_model_bias[top_channels] ))\n",
        "  \n",
        "# #   temp_wt = np.concatenate((low_accuracy_model_weight[:,:,:,bottom_channels],high_accuracy_model_weight[:,:,:,top_channels] ), axis = 3)\n",
        "# #   temp_bias = np.concatenate((low_accuracy_model_bias[bottom_channels],high_accuracy_model_bias[top_channels] ))\n",
        "# #   print(temp_wt.shape,temp_bias.shape )\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",
        "# #   print(low_accuracy_model_weight.shape)\n",
        "#   pmod = prunedModel.get_layer(this_layer_name).get_weights()\n",
        "# #   print(pmod[0].shape)\n",
        "  \n",
        "#   prunedModel.get_layer(this_layer_name).set_weights([temp_wt, temp_bias])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPHqyAJyhVxN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for transfer_layer in layer_names_to_transfer:\n",
        "  prunedModel.get_layer(transfer_layer).set_weights(copied_low_accuracy_model.get_layer(transfer_layer).get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uiHfuCCAh4FZ",
        "colab_type": "code",
        "outputId": "25f4c7ed-300e-4b76-f151-a4a72a25a354",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "source": [
        "layer_names_to_transfer"
      ],
      "execution_count": 393,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_1',\n",
              " 'conv1_1',\n",
              " 'conv1_2',\n",
              " 'pool1_3',\n",
              " 'conv2_1',\n",
              " 'conv2_2',\n",
              " 'pool2_3',\n",
              " 'conv3_1',\n",
              " 'conv3_2',\n",
              " 'conv4_1',\n",
              " 'conv4_2',\n",
              " 'pool4_2',\n",
              " 'conv5_1',\n",
              " 'conv5_2',\n",
              " 'conv6_1',\n",
              " 'conv6_2',\n",
              " 'pool6_2',\n",
              " 'conv7_1',\n",
              " 'conv7_2']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 393
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFMhtMXgh-Qf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# copied_low_accuracy_model.get_layer('conv1_1').get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGd9idFth1NQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# prunedModel.get_layer('conv1_1').get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OodsSlYDCesc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_accuracy_model_weights_bias.shape\n",
        "# Ks"
      ],
      "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": "5ee67c66-7f35-4a7e-bfcd-724dbb946c79",
        "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": 403,
      "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": 403
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZchwmWh7la9",
        "colab_type": "code",
        "outputId": "b2b72bf9-265a-4f40-a478-28ccd78754ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        }
      },
      "source": [
        "copied_low_accuracy_model.summary()"
      ],
      "execution_count": 404,
      "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_model2Prune.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 = 0.0001\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": "0ce8582b-271d-4412-badc-bbd5021cbf62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 436,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.45540413411458336, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 436
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLlzjVbCiM6N",
        "colab_type": "code",
        "outputId": "d3e5e007-1376-45a5-fbfd-bbb5a9177b68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "prunedModel.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 437,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.2396901168823242, 0.12733333334326744]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 437
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIOMXQsjdSNX",
        "colab_type": "code",
        "outputId": "715c3bb0-8aff-4936-e108-bd65c1779fe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1421
        }
      },
      "source": [
        "prunedModel.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=40,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])"
      ],
      "execution_count": 438,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "187/187 [==============================] - 12s 67ms/step - loss: 0.3390 - acc: 0.9197 - val_loss: 0.4360 - val_acc: 0.8760\n",
            "Epoch 2/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2882 - acc: 0.9408 - val_loss: 0.4410 - val_acc: 0.8827\n",
            "Epoch 3/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2765 - acc: 0.9429 - val_loss: 0.4846 - val_acc: 0.8790\n",
            "Epoch 4/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2677 - acc: 0.9472 - val_loss: 0.4812 - val_acc: 0.8823\n",
            "Epoch 5/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2542 - acc: 0.9513 - val_loss: 0.4851 - val_acc: 0.8907\n",
            "Epoch 6/40\n",
            "187/187 [==============================] - 9s 47ms/step - loss: 0.2572 - acc: 0.9493 - val_loss: 0.4768 - val_acc: 0.8800\n",
            "Epoch 7/40\n",
            "187/187 [==============================] - 9s 47ms/step - loss: 0.2507 - acc: 0.9493 - val_loss: 0.4569 - val_acc: 0.8897\n",
            "Epoch 8/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2453 - acc: 0.9547 - val_loss: 0.5255 - val_acc: 0.8840\n",
            "Epoch 9/40\n",
            "187/187 [==============================] - 9s 47ms/step - loss: 0.2361 - acc: 0.9567 - val_loss: 0.4871 - val_acc: 0.8837\n",
            "Epoch 10/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2379 - acc: 0.9554 - val_loss: 0.4770 - val_acc: 0.8917\n",
            "Epoch 11/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2361 - acc: 0.9559 - val_loss: 0.5152 - val_acc: 0.8867\n",
            "Epoch 12/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2270 - acc: 0.9593 - val_loss: 0.4894 - val_acc: 0.8910\n",
            "Epoch 13/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2199 - acc: 0.9614 - val_loss: 0.5402 - val_acc: 0.8933\n",
            "Epoch 14/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2252 - acc: 0.9606 - val_loss: 0.6727 - val_acc: 0.8827\n",
            "Epoch 15/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2207 - acc: 0.9606 - val_loss: 0.5475 - val_acc: 0.8913\n",
            "Epoch 16/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2160 - acc: 0.9617 - val_loss: 0.5953 - val_acc: 0.8923\n",
            "Epoch 17/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2129 - acc: 0.9638 - val_loss: 0.5619 - val_acc: 0.8860\n",
            "Epoch 18/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2160 - acc: 0.9630 - val_loss: 0.5170 - val_acc: 0.8857\n",
            "Epoch 19/40\n",
            "187/187 [==============================] - 9s 47ms/step - loss: 0.2066 - acc: 0.9648 - val_loss: 0.5854 - val_acc: 0.8837\n",
            "Epoch 20/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.2045 - acc: 0.9662 - val_loss: 0.7007 - val_acc: 0.8853\n",
            "Epoch 21/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1970 - acc: 0.9674 - val_loss: 0.5958 - val_acc: 0.8927\n",
            "Epoch 22/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1987 - acc: 0.9678 - val_loss: 0.6094 - val_acc: 0.8893\n",
            "Epoch 23/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1973 - acc: 0.9682 - val_loss: 0.6378 - val_acc: 0.8913\n",
            "Epoch 24/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1964 - acc: 0.9688 - val_loss: 0.5793 - val_acc: 0.8863\n",
            "Epoch 25/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1990 - acc: 0.9667 - val_loss: 0.7557 - val_acc: 0.8860\n",
            "Epoch 26/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1992 - acc: 0.9693 - val_loss: 0.6466 - val_acc: 0.8800\n",
            "Epoch 27/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1967 - acc: 0.9690 - val_loss: 0.5991 - val_acc: 0.8800\n",
            "Epoch 28/40\n",
            "187/187 [==============================] - 9s 47ms/step - loss: 0.1903 - acc: 0.9713 - val_loss: 0.6527 - val_acc: 0.8853\n",
            "Epoch 29/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1976 - acc: 0.9713 - val_loss: 0.6004 - val_acc: 0.8850\n",
            "Epoch 30/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1865 - acc: 0.9719 - val_loss: 0.6706 - val_acc: 0.8900\n",
            "Epoch 31/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1959 - acc: 0.9715 - val_loss: 0.5584 - val_acc: 0.8860\n",
            "Epoch 32/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1798 - acc: 0.9753 - val_loss: 0.6048 - val_acc: 0.8893\n",
            "Epoch 33/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1889 - acc: 0.9702 - val_loss: 0.5451 - val_acc: 0.8863\n",
            "Epoch 34/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1790 - acc: 0.9755 - val_loss: 0.5992 - val_acc: 0.8890\n",
            "Epoch 35/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1789 - acc: 0.9745 - val_loss: 0.6713 - val_acc: 0.8847\n",
            "Epoch 36/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1784 - acc: 0.9745 - val_loss: 0.6218 - val_acc: 0.8867\n",
            "Epoch 37/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1761 - acc: 0.9754 - val_loss: 0.6073 - val_acc: 0.8870\n",
            "Epoch 38/40\n",
            "187/187 [==============================] - 9s 47ms/step - loss: 0.1769 - acc: 0.9750 - val_loss: 0.6511 - val_acc: 0.8893\n",
            "Epoch 39/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1729 - acc: 0.9767 - val_loss: 0.6737 - val_acc: 0.8797\n",
            "Epoch 40/40\n",
            "187/187 [==============================] - 9s 46ms/step - loss: 0.1751 - acc: 0.9769 - val_loss: 0.6388 - val_acc: 0.8870\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f17caa58080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 438
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAnK7D_Mm_dz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prunedModel.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",
        "prunedModel.save('pruned_modelTariq.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZsEELS6m_WM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "21cd8b78-16f6-4f3f-94a4-cd1efd049f38"
      },
      "source": [
        "accuracy = prunedModel.history.history['acc']\n",
        "val_accuracy = prunedModel.history.history['val_acc']\n",
        "loss = prunedModel.history.history['loss']\n",
        "val_loss = prunedModel.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": 441,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xd4VGX2wPHvgQRBerMASlFUajAE\n0AWkiIoFEBQF0RVcZVfFuhZcXevaGxbWjooNWRU7YgEXXXSlo3SU7I/eQhUCgZzfH+dOGELKJJlk\nksz5PM88mZnbztzMnPve933ve0VVcc45Fx8qxDoA55xzJceTvnPOxRFP+s45F0c86TvnXBzxpO+c\nc3HEk75zzsURT/pxSEQqisgOETk6mvPGkogcKyJR738sIr1EJDXs9WIR6RrJvIXY1ssi8rfCLu9c\nJBJiHYDLn4jsCHt5KLAb2Be8/rOqvlWQ9anqPqBatOeNB6p6fDTWIyKXAxeravewdV8ejXU7lxdP\n+mWAqmYl3aAkebmqfp3b/CKSoKp7SyI25/Lj38fSxat3ygER+YeIvCsi74jIduBiETlZRH4UkS0i\nskZEnhaRxGD+BBFREWkSvH4zmD5RRLaLyA8i0rSg8wbTzxSRJSKyVUSeEZH/iMjQXOKOJMY/i8gy\nEdksIk+HLVtRRJ4UkU0i8hvQO4/9c7uIjMv23mgReSJ4frmILAw+z69BKTy3da0Uke7B80NF5I0g\ntvlA+2zz3iEivwXrnS8ifYP32wDPAl2DqrONYfv27rDl/xJ89k0i8qGIHBnJvinIfg7FIyJfi0ia\niKwVkVvCtvP3YJ9sE5EZItIgp6o0Efk+9H8O9ufUYDtpwB0i0lxEpgTb2Bjst5phyzcOPuOGYPpT\nIlI5iLlF2HxHishOEamb2+d1+VBVf5ShB5AK9Mr23j+APUAf7EBeBegAdMLO5poBS4ARwfwJgAJN\ngtdvAhuBFCAReBd4sxDzHgZsB/oF024EMoChuXyWSGL8CKgJNAHSQp8dGAHMBxoBdYGp9nXOcTvN\ngB1A1bB1rwdSgtd9gnkE6AnsAtoG03oBqWHrWgl0D54/BnwL1AYaAwuyzXsBcGTwP7koiOHwYNrl\nwLfZ4nwTuDt4fnoQYzugMvBPYHIk+6aA+7kmsA64DjgEqAF0DKbdBswFmgefoR1QBzg2+74Gvg/9\nn4PPthe4EqiIfR+PA04FKgXfk/8Aj4V9nl+C/Vk1mL9zMO1F4P6w7fwVmBDr32FZfsQ8AH8U8B+W\ne9KfnM9yNwH/Cp7nlMifD5u3L/BLIea9DPgubJoAa8gl6UcY40lh0z8AbgqeT8WquULTzsqeiLKt\n+0fgouD5mcDiPOb9FLg6eJ5X0v+/8P8FcFX4vDms9xfg7OB5fkn/deCBsGk1sHacRvntmwLu50uA\n6bnM92so3mzvR5L0f8snhvND2wW6AmuBijnM1xlYDkjweg4wINq/q3h6ePVO+bEi/IWInCAinwWn\n69uAe4F6eSy/Nuz5TvJuvM1t3gbhcaj9SlfmtpIIY4xoW8D/8ogX4G1gcPD8ouB1KI5zROS/QdXD\nFqyUnde+CjkyrxhEZKiIzA2qKLYAJ0S4XrDPl7U+Vd0GbAYahs0T0f8sn/18FJbcc5LXtPxk/z4e\nISLjRWRVEMNr2WJIVes0cABV/Q921tBFRFoDRwOfFTImh9fplyfZuyu+gJUsj1XVGsCdWMm7OK3B\nSqIAiIhwYJLKrigxrsGSRUh+XUrHA71EpCFW/fR2EGMV4D3gQazqpRbwZYRxrM0tBhFpBjyHVXHU\nDda7KGy9+XUvXY1VGYXWVx2rRloVQVzZ5bWfVwDH5LJcbtN+D2I6NOy9I7LNk/3zPYz1OmsTxDA0\nWwyNRaRiLnGMBS7GzkrGq+ruXOZzEfCkX35VB7YCvwcNYX8ugW1+CiSLSB8RScDqiesXU4zjgetF\npGHQqHdrXjOr6lqsCuI1rGpnaTDpEKyeeQOwT0TOweqeI43hbyJSS+w6hhFh06phiW8Ddvy7Aivp\nh6wDGoU3qGbzDvAnEWkrIodgB6XvVDXXM6c85LWfPwaOFpERInKIiNQQkY7BtJeBf4jIMWLaiUgd\n7GC3FuswUFFEhhN2gMojht+BrSJyFFbFFPIDsAl4QKxxvIqIdA6b/gZWHXQRdgBwReBJv/z6K3Ap\n1rD6AtbgWqxUdR1wIfAE9iM+BpiNlfCiHeNzwDfAz8B0rLSen7exOvqsqh1V3QLcAEzAGkPPxw5e\nkbgLO+NIBSYSlpBUdR7wDPBTMM/xwH/Dlv0KWAqsE5HwaprQ8l9g1TATguWPBoZEGFd2ue5nVd0K\nnAachx2IlgDdgsmPAh9i+3kb1qhaOai2uwL4G9aof2y2z5aTu4CO2MHnY+D9sBj2AucALbBS//9h\n/4fQ9FTs/7xbVacV8LO7bEKNI85FXXC6vho4X1W/i3U8ruwSkbFY4/DdsY6lrPOLs1xUiUhvrKfM\nLqzLXwZW2nWuUIL2kX5Am1jHUh549Y6Lti7Ab1hd9hlAf294c4UlIg9i1wo8oKr/F+t4ygOv3nHO\nuTjiJX3nnIsjpa5Ov169etqkSZNYh+Gcc2XKzJkzN6pqXl2kgQiTftA49xQ2jsbLqvpQtumNgTFY\nn+w0bMjYlcG0R4CzsbOKr4DrNI86pSZNmjBjxoxIwnLOORcQkfyuSgciqN4Jut2NxsYraQkMFpGW\n2WZ7DBirqm2xvsUPBsv+ARs7oy3QGhv4qRvOOediIpI6/Y7AMlX9TVX3AOOw7lPhWgKTg+dTwqYr\nNkJgJezKx0TsAhDnnHMxEEnSb8iBgyet5ODxVOYCA4Ln/YHqIlJXVX/ADgJrgsckVV2YfQMiMjwY\nq3vGhg0bCvoZnHPORShavXduArqJyGys+mYVNo7Jsdil1Y2wA0VPyeH+oqr6oqqmqGpK/fr5tkM4\n55wrpEgacldx4EiCjcg20p+qriYo6YtINeA8Vd0SDDL1o6ruCKZNBE4G/JJ855yLgUhK+tOB5iLS\nVEQqAYOwAZOyiEg9EQmt6zasJw/YwEndgtuuJWJnAQdV7zjnnCsZ+Sb9YAS8EcAkLGGPV9X5InKv\nBPf8BLoDi0VkCXA4cH/w/nvYTRh+xur956rqJ9H9CM455yJV6oZhSElJUe+n75wrj3btgi1bDn5s\n3mx/69WD4cMLt24RmamqKfnNV+quyHXOufJk3z748EN48kn4z3/ynvekkwqf9CPlSd8554rBtm3w\nyivw9NOQmgpNm8Kdd8KRR0KtWvaoXXv/85o1oXLl4o/Lk75zrtTIzISvvoJu3QqXAOfNg48+go4d\nrdRcs2Z04lq3zkrps2ZZom7SZP+jTh2QsDsqL19uif6VV2D7dujSBR5/HPr1g4q53QW4BHnSd86V\nCnv3wmWXwRtvwJAh9lciuT19YNMmOOccWBFcSioCbdpA58726NIFjj46/3VmZsKiRZbkQ49ly/av\nM3szaPXq+w8AmZkwcSJUqAAXXAA33AAp+daylyxP+s65mNu9GwYPhgkToGtXeOstK+1fcUVky6vC\nsGGwdi18+y1kZOxP2G+8Ac89Z/M1bAjHHJN74t+3DxYsgLQ0e12vnh0w/vxn+5ucbI2xqalWok9N\nPfD5tm1wyy0wYoRtqzTypO+ci6mdO6F/f/jyS3jqKUuYZ54J11wDHTpAu3b5r2PUKPjkE1u+WzCk\nY69e9nfvXvj55/0HgTVrcl9PxYoWS+jsoHnzgw8QhxxiMUUSV2nkXTadczGzdatVyUybBi+/bKV1\ngA0b4MQToUoVmDkTatTIfR0//WRVN2efDR98ULAqofIk0i6bfucs51xMbNwIPXvCjz/CuHH7Ez5A\n/fr23vLlcPnlB9ejh2zZAhdeCA0awJgx8ZvwC8KTvnOuxK1ebdUwCxZYb5uBAw+ep0sXuP9++Ne/\n4J//PHi6qh0QVq60A0Tt2sUfd3ngdfrOuSLZvRt++w2WLLHH0qWwZ4+V1g87zP6GP09Pt6qY9evh\niy/218Hn5Oab4bvv4MYboVOnA3vCPPccvP8+PPqodc90kfE6fefcARYuhHfftQbQ3Gzdasl9yRL4\n3/+sq2JI/frWx37DBkvwOald2xJ+x475x7Npk9XvJyRYP/latWDOHDsI9OplDbgVvM4i4jp9T/rO\nOcB6tdx9tzWoZmbmfSFR1arWs+W44/b/DT2vVcvmUYXff7cS/YYN+/+mpUHfvjZ/pH780bpy9ukD\nr71mJf6dOy3516tXlE9dfvjYO865iGzfDo89Zo89e+Dqq+Hvf7cSe1GIQLVq9mjWrGjrOukkePhh\n+Otf4Zdf4NdfrT++J/yC85Mi5+JURgY8/7yVzu+917pOLlxoQwiUxhvY3XCDDWWwdKnF2/Wge/C5\nSHhJ37lyRhU+/dR6yCQmQqVKB/5NTLRqln/8AxYvhlNOsR40nTrFOvK8icCbb9rYPP36xTqassuT\nvnOlREaGlWLnz7dHlSpw1VU2tkukVq60boyTJuU/b4sW8PHHVsIvK/3bq1WzK2Zd4XnSd64EqFrD\nY/iNM9autX7qoSS/eLElftg/sNeoUdZX/dJL825YVYWxY+G662wdTz8N551nzzMyrK4+/K+IDXGQ\n4Bkg7vi/3LkoCO/CGHr8+qv1VAkl+dy6QDZtCq1aWd/1Vq3sccIJ1mB5/fXwpz/BM8/YTTi6dz94\n+TVr7MYbn35qFzS9+ioce2yxflxXhnnSd66AduyAr7+2KpRffrEEv379/uki0LixjebYrNmBN8oI\nv3lG3bpw/PHW/TEnHTvaAGHjx9vIjT16WNXGo4/aulXh7bdtYLJdu+ygcO213mfd5c376TsXgd9+\ng88+s9L0t99aNUn16jbSYqiPeqif+jHHRP8OSKGk/uCDdgXstddaTBMmwMknW+n++OOju01XtvjF\nWc4VQWYm/PCD3dv000/tphpgifXss+3RpYv1iClJa9bAHXdYkq9UyXrg3HBD6bgjk4stT/rOFcKC\nBdYt8O23bXiBxESrRw8l+tJSV75ggY3rfswxsY7ElRZ+Ra5zEVq1ykZpfPNNu6y/QgU4/XQrRffr\nV7AukyWlZctYR+DKKk/6Li7t3g3vvWfVJJMnW6Nohw5256ULL4TDD491hM4VD0/6Lq6sXAkvvAAv\nvmg9bpo1gzvvhIsuKtgAYM6VVZ70XZm0b5+V1N96Cxo1sqF327WD1q3tStZwqvDvf8Ozz1rDbGam\nXYU6YoQNzetdHF088aTvypSMDEv0Dz5o/eMbNbKE/txzNr1iRbuwqV07OxAkJlqpfv58qFPHRmn8\ny1/sgijn4pEnfVcmpKfbOOoPPWS9apKS7KKlAQOspL58uTXCzp5tf7/91g4OAO3bW939hRcefBbg\nXLzxpO9Ktd9/h5desqtQV6+2kSCffda6T4YPEtasmT0GDNj/XuiGHccdV3YGFHOuuHnSd6XO77/D\nl1/a1aaffGLj1nTvbgOK9ewZeQIP3ZvVObdfRElfRHoDTwEVgZdV9aFs0xsDY4D6QBpwsaquDKYd\nDbwMHAUocJaqpkbrA7jY2bYNHn/chieoXx8aNjz40aCB3d0ov8bSTZvsytcJEyzh79pl49P07QtX\nXGFXvzrnii7fpC8iFYHRwGnASmC6iHysqgvCZnsMGKuqr4tIT+BB4JJg2ljgflX9SkSqAWG3UHZl\n0a5dMHq0NaampVlC3rDB6tLXrbPeMuEqVDh4wLHQo2ZNW27qVOuR06iRjSrZv7/dGSkxMTaf0bny\nKpKSfkdgmar+BiAi44B+QHjSbwncGDyfAnwYzNsSSFDVrwBUdUeU4nYxkJEBY8bYrepWr4YzzrCx\n3tu3P3CedevsKtfQY906G3p4yxbYvNn+Ll68/3XjxnDrrZbo27f3+nfnilMkSb8hsCLs9Uog+43V\n5gIDsCqg/kB1EakLHAdsEZEPgKbA18BIVd0XvrCIDAeGAxx99NGF+BiuOGVm2jAFd95pY8T/4Q82\nNk23bgfPm5hopfVGjUo+Tudc/qJ1WcpNQDcRmQ10A1YB+7CDStdgegegGTA0+8Kq+qKqpqhqSn1v\neStV5s+H5GQYMsTGff/0U/j++5wTvnOu9IukpL8Ka4QNaRS8l0VVV2MlfYJ6+/NUdYuIrATmhFUN\nfQicBLwShdhdMZs40fq2V61qJfsLL/SrV50r6yL5CU8HmotIUxGpBAwCPg6fQUTqiUhoXbdhPXlC\ny9YSkVDxvScHtgW4UkjV7rF6zjk2dO/06TB4sCd858qDfH/GqroXGAFMAhYC41V1vojcKyJ9g9m6\nA4tFZAlwOHB/sOw+rGrnGxH5GRDgpah/Chc1GRlw1VV2g+2+feG777x+3rnyxG+i4rJs3gwDB8I3\n31hvmgce8NK9c2WF30QlTuzaBS+/bEMGn302dO5cuFvnLV0KffrYfVdffRWGDo16qM65UsCTfhmV\nnm5j0jz4oN03NSEBHnnErn7t29f6vPfqlf8NutPTbXCyIUOsf/w339hFUc658smTfhmze7eV7B98\n0C586tYN3nnHulV+8YUNY/Dee3YRVdWqcOaZdgCoXx9SU+2xfPn+52vW2HpbtLBxbvyeq86Vb16n\nX0bs3m2J/IEHrCqna1e45x7o0ePgeffsgSlT7ADw0Uewdu3+aRUrwtFH23jyTZrYo1kzq9qpUaOk\nPo1zLtoirdP3pF8GTJsGgwbBihV2New998Cpp0Y2XEFmpnW5TE+3RN+ggVUFOefKF2/ILSfWrYPz\nzoNDD4VJk+C00wo2Nk2FCjYGvXPOgSf9Um3fPrth99atNtxwmzaxjsg5V9Z50i/F7rsPJk+2unxP\n+M65aPBLb0qpr76yIYwvvRSGDYt1NM658sKTfim0erX1m2/Rwm5W4pxz0eLVO6XM3r02uNnvv8O/\n/2197Z1zLlq8pB8Fa9dCSop1p5wwwbpJFtadd9qtA194wUr6zjkXTZ70i2jlSrsqdtEiS/4DBliy\nfvllu6CqICZOtCttL78cLr64eOJ1zsU3T/pFkJoKp5xiQxlMmgRLlthtBatWhSuusKtdH37Yulzm\nZ8UKuOQSaNvWxrJ3zrni4FfkFtLSpXZV7I4dlvA7dNg/TdUGLnvkEeuFU706DB8OrVvnvr7nn7db\nE86cCccdV/zxO+fKF78itxgtWGAjWGZk2Bg3SUkHThex6b16wezZ8Oij8OSTedf1V6wIb73lCd85\nV7y8pF9Ac+faUAgVK1ppvmXLyJbbtAm2b899erVqNiyyc84Vhpf0i8GMGXD66VZnP3kyNG8e+bJ1\n69rDOediyRtyI/TDD1aHX7OmdaksSMJ3zrnSwpN+BObNs5uRHHaYJfymTWMdkXPOFY4n/XwsXw69\ne1ud+9dfw1FHxToi55wrPK/Tz8P69VaHn54O330HjRvHOiLnnCsaT/q52LbNqnRWr7YSfqtWsY7I\nOeeKzpN+DtLT4dxzrS7/44/h5JNjHZFzzkWHJ/1s9u2zYY2nTIE337TSvnPOlRfekBtGFa66Cj74\nAEaNsuTvnHPliSf9MHfeCS++CLffDtddF+tonHMu+jzpAxs3wpVXwj/+YaNj3ndfrCNyzrniEddJ\nf88eePxxOPZYeOkluP56eO45GzDNOefKo7hM+qrw4Yc2WNpNN9kdr+bNs5EwK1aMdXTOOVd8Ikr6\nItJbRBaLyDIRGZnD9MYi8o2IzBORb0WkUbbpNURkpYg8G63AC2vOHOjZE/r3h0MOsbtVff555KNl\nOudcWZZv0heRisBo4EygJTBYRLKnyMeAsaraFrgXeDDb9PuAqUUPt/D27LHbECYnw88/w+jRNkxy\n796xjMo550pWJCX9jsAyVf1NVfcA44B+2eZpCUwOnk8Jny4i7YHDgS+LHm7hffghvPIKXH01LFtm\nXTMT/CoF51yciSTpNwRWhL1eGbwXbi4wIHjeH6guInVFpALwOHBTUQMtqtmzITHRGm5r1Yp1NM45\nFxvRasi9CegmIrOBbsAqYB9wFfC5qq7Ma2ERGS4iM0RkxoYNG6IU0oFmz7Z6+0qVimX1zjlXJkRS\nwbEKCB9QuFHwXhZVXU1Q0heRasB5qrpFRE4GuorIVUA1oJKI7FDVkdmWfxF4Eex2iYX9MLlRtaR/\n1lnRXrNzzpUtkST96UBzEWmKJftBwEXhM4hIPSBNVTOB24AxAKo6JGyeoUBK9oRfEtautWGSTzyx\npLfsnHOlS77VO6q6FxgBTAIWAuNVdb6I3CsifYPZugOLRWQJ1mh7fzHFWyizZ9vfdu1iG4dzzsVa\nRP1XVPVz4PNs790Z9vw94L181vEa8FqBI4yCUNJPSorF1p1zrvSIiyty58yBZs3spubOORfP4iLp\nz57t9fnOOQdxkPS3bYNff/Wk75xzEAdJf+5c++uNuM45FwdJP9SI6yV955yLg3vkzpkDhx0GRx4Z\n60icK7iMjAxWrlxJenp6rENxpUTlypVp1KgRiYmJhVq+3Cf92bOtasdvjOLKopUrV1K9enWaNGmC\n+Jc47qkqmzZtYuXKlTRt2rRQ6yjX1Tt79sD8+V6148qu9PR06tat6wnfASAi1K1bt0hnfuU66S9Y\nABkZ3ojryjZP+C5cUb8P5TrpeyOuc0WzadMm2rVrR7t27TjiiCNo2LBh1us9e/ZEtI5hw4axePHi\nPOcZPXo0b731VjRCdvko13X6c+ZA1ap243PnXMHVrVuXOXPmAHD33XdTrVo1brrpwNtjqCqqSoUK\nOZchX3311Xy3c/XVVxc92BK2d+9eEsrgnZjKfUm/bVu/2blz0bZs2TJatmzJkCFDaNWqFWvWrGH4\n8OGkpKTQqlUr7r333qx5u3Tpwpw5c9i7dy+1atVi5MiRJCUlcfLJJ7N+/XoA7rjjDkaNGpU1/8iR\nI+nYsSPHH38806ZNA+D333/nvPPOo2XLlpx//vmkpKRkHZDC3XXXXXTo0IHWrVvzl7/8BVUbrX3J\nkiX07NmTpKQkkpOTSU1NBeCBBx6gTZs2JCUlcfvttx8QM8DatWs5Nig5vvzyy5x77rn06NGDM844\ng23bttGzZ0+Sk5Np27Ytn376aVYcr776Km3btiUpKYlhw4axdetWmjVrxt69ewHYvHnzAa9LStk7\nTEUoM9NK+pdcEutInIuO66+373Q0tWsHQa4tsEWLFjF27FhSUlIAeOihh6hTpw579+6lR48enH/+\n+bRseeDttLdu3Uq3bt146KGHuPHGGxkzZgwjRx482rqq8tNPP/Hxxx9z77338sUXX/DMM89wxBFH\n8P777zN37lySk5NzjOu6667jnnvuQVW56KKL+OKLLzjzzDMZPHgwd999N3369CE9PZ3MzEw++eQT\nJk6cyE8//USVKlVIS0vL93PPnj2bOXPmULt2bTIyMvjwww+pUaMG69evp3PnzpxzzjnMnTuXhx9+\nmGnTplGnTh3S0tKoWbMmnTt35osvvuCcc87hnXfeYeDAgSV+tlBuS/rLl8P27V6f71xxOeaYY7IS\nPsA777xDcnIyycnJLFy4kAULFhy0TJUqVTjzzDMBaN++fVZpO7sBAwYcNM/333/PoEGDAEhKSqJV\nq1Y5LvvNN9/QsWNHkpKS+Pe//838+fPZvHkzGzdupE+fPoD1dT/00EP5+uuvueyyy6hSpQoAderU\nyfdzn3766dSuXRuwg9PIkSNp27Ytp59+OitWrGDjxo1MnjyZCy+8MGt9ob+XX355VnXXq6++yrBh\nw/LdXrSV25K+j6HvypvClsiLS9WqVbOeL126lKeeeoqffvqJWrVqcfHFF+fYrbBS2P1KK1asmGvV\nxiGHHJLvPDnZuXMnI0aMYNasWTRs2JA77rijUN0bExISyMzMBDho+fDPPXbsWLZu3cqsWbNISEig\nUaNGeW6vW7dujBgxgilTppCYmMgJJ5xQ4NiKqtyW9OfMsbr81q1jHYlz5d+2bduoXr06NWrUYM2a\nNUyaNCnq2+jcuTPjx48H4Oeff87xTGLXrl1UqFCBevXqsX37dt5//30AateuTf369fnkk08AS+Q7\nd+7ktNNOY8yYMezatQsgq3qnSZMmzJw5E4D33sv9ViFbt27lsMMOIyEhga+++opVq+xOsj179uTd\nd9/NWl94tdHFF1/MkCFDYlLKh3Kc9EM3Qq9cOdaROFf+JScn07JlS0444QT++Mc/0rlz56hv45pr\nrmHVqlW0bNmSe+65h5YtW1Iz200y6taty6WXXkrLli0588wz6dSpU9a0t956i8cff5y2bdvSpUsX\nNmzYwDnnnEPv3r1JSUmhXbt2PPnkkwDcfPPNPPXUUyQnJ7N58+ZcY7rkkkuYNm0abdq0Ydy4cTRv\n3hyw6qdbbrmFU045hXbt2nHzzTdnLTNkyBC2bt3KhRdeGM3dEzEJtWyXFikpKTpjxowir6dBA+jV\nC8aOjUJQzsXIwoULadGiRazDKBX27t3L3r17qVy5MkuXLuX0009n6dKlZa7b5Lhx45g0aVJEXVlz\nk9P3QkRmqmpKLotkKVt7K0Lr1sGaNd6I61x5smPHDk499VT27t2LqvLCCy+UuYR/5ZVX8vXXX/PF\nF1/ELIaytcciFOrW5o24zpUftWrVyqpnL6uee+65WIdQPuv0veeOc87lrFwm/TlzoEkTCLrSOuec\nC5TLpB8aQ98559yByl3S37EDli71RlznnMtJuUv68+aBqid956KhR48eB11oNWrUKK688so8l6tW\nrRoAq1ev5vzzz89xnu7du5Nf9+xRo0axc+fOrNdnnXUWW7ZsiSR0l4tyl/S9Ede56Bk8eDDjxo07\n4L1x48YxePDgiJZv0KBBnle05id70v/888+pVatWoddX0lQ1aziH0qLcJf05c6BuXWjUKNaROFf2\nnX/++Xz22WdZN0xJTU1l9erVdO3aNavffHJyMm3atOGjjz46aPnU1FRaB2Oh7Nq1i0GDBtGiRQv6\n9++fNfQBWP/10LDMd911FwBPP/00q1evpkePHvTo0QOw4RE2btwIwBNPPEHr1q1p3bp11rDMqamp\ntGjRgiuuuIJWrVpx+umnH7CdkE8++YROnTpx4okn0qtXL9atWwfYtQDDhg2jTZs2tG3bNmsYhy++\n+ILk5GSSkpI49dRTAbu/wGPvtz/BAAAeeElEQVSPPZa1ztatW5OamkpqairHH388f/zjH2ndujUr\nVqzI8fMBTJ8+nT/84Q8kJSXRsWNHtm/fzimnnHLAkNFdunRh7ty5Bfq/5aXc9dP3G6G7cisGYyvX\nqVOHjh07MnHiRPr168e4ceO44IILEBEqV67MhAkTqFGjBhs3buSkk06ib9++ud7O77nnnuPQQw9l\n4cKFzJs374Chke+//37q1KnDvn37OPXUU5k3bx7XXnstTzzxBFOmTKFevXoHrGvmzJm8+uqr/Pe/\n/0VV6dSpE926daN27dosXbqUd955h5deeokLLriA999/n4svvviA5bt06cKPP/6IiPDyyy/zyCOP\n8Pjjj3PfffdRs2ZNfv75Z8DGvN+wYQNXXHEFU6dOpWnTphENv7x06VJef/11TjrppFw/3wknnMCF\nF17Iu+++S4cOHdi2bRtVqlThT3/6E6+99hqjRo1iyZIlpKenk5SUlO82I1WuSvoZGfDLL16f71w0\nhVfxhFftqCp/+9vfaNu2Lb169WLVqlVZJeacTJ06NSv5tm3blrZt22ZNGz9+PMnJyZx44onMnz8/\nx8HUwn3//ff079+fqlWrUq1aNQYMGMB3330HQNOmTWkX1O/mNnzzypUrOeOMM2jTpg2PPvoo8+fP\nB+Drr78+4C5etWvX5scff+SUU06hadOmQGTDLzdu3Dgr4ef2+RYvXsyRRx5Jhw4dAKhRowYJCQkM\nHDiQTz/9lIyMDMaMGcPQoUPz3V5BlKuS/qJFsHu3J31XTsVobOV+/fpxww03MGvWLHbu3En79u0B\nG8Bsw4YNzJw5k8TERJo0aVKoYYyXL1/OY489xvTp06lduzZDhw4t1HpCQsMygw3NnFP1zjXXXMON\nN95I3759+fbbb7n77rsLvJ3w4ZfhwCGYw4dfLujnO/TQQznttNP46KOPGD9+fNSvQi5XJX1vxHUu\n+qpVq0aPHj247LLLDmjADQ0rnJiYyJQpU/jf//6X53pOOeUU3n77bQB++eUX5s2bB9iwzFWrVqVm\nzZqsW7eOiRMnZi1TvXp1tm/fftC6unbtyocffsjOnTv5/fffmTBhAl27do34M23dupWGDRsC8Prr\nr2e9f9pppzF69Ois15s3b+akk05i6tSpLF++HDhw+OVZs2YBMGvWrKzp2eX2+Y4//njWrFnD9OnT\nAdi+fXvWvQMuv/xyrr32Wjp06JB1w5ZoiSjpi0hvEVksIstE5KB7m4lIYxH5RkTmici3ItIoeL+d\niPwgIvODacU6luicOVClChx/fHFuxbn4M3jwYObOnXtA0h8yZAgzZsygTZs2jB07Nt8bglx55ZXs\n2LGDFi1acOedd2adMSQlJXHiiSdywgkncNFFFx0wLPPw4cPp3bt3VkNuSHJyMkOHDqVjx4506tSJ\nyy+/nBMLcIp/9913M3DgQNq3b39Ae8Edd9zB5s2bad26NUlJSUyZMoX69evz4osvMmDAAJKSkrKG\nRD7vvPNIS0ujVatWPPvssxx33HE5biu3z1epUiXeffddrrnmGpKSkjjttNOyzgDat29PjRo1imXM\n/XyHVhaRisAS4DRgJTAdGKyqC8Lm+Rfwqaq+LiI9gWGqeomIHAeoqi4VkQbATKCFquba0bYoQyv3\n6AG7dsGPPxZqcedKHR9aOT6tXr2a7t27s2jRIipUOLhsXpShlSMp6XcElqnqb6q6BxgH9Ms2T0tg\ncvB8Smi6qi5R1aXB89XAeqB+BNssMFUr6XvVjnOuLBs7diydOnXi/vvvzzHhF1Uka2wIrAh7vTJ4\nL9xcYEDwvD9QXUTqhs8gIh2BSsCv2TcgIsNFZIaIzNiwYUOksR9gxQrYssUbcZ1zZdsf//hHVqxY\nwcCBA4tl/dE6jNwEdBOR2UA3YBWwLzRRRI4E3sCqfQ66PE1VX1TVFFVNqV+/cCcCRx8N69fDoEGF\nWtw55+JCJF02VwFHhb1uFLyXJai6GQAgItWA80L19iJSA/gMuF1Vi7W2vZDHC+dKNVXN9YInF3+K\neovbSEr604HmItJURCoBg4CPw2cQkXoiElrXbcCY4P1KwARgrKoWfgAO5+JU5cqV2bRpU5F/6K58\nUFU2bdpE5cqVC72OfEv6qrpXREYAk4CKwBhVnS8i9wIzVPVjoDvwoIgoMBUIXdJ2AXAKUFdEhgbv\nDVXVKF9L7lz51KhRI1auXElh27pc+VO5cmUaFWFwsXy7bJa0onTZdM65eBXNLpvOOefKCU/6zjkX\nRzzpO+dcHPGk75xzccSTvnPOxRFP+s45F0c86TvnXBzxpO+cc3HEk75zzsURT/rOORdHPOk751wc\n8aTvnHNxxJO+c87FEU/6zjkXRzzpO+dcHPGk75xzccSTvnPOxRFP+s45F0c86TvnXBzxpO+cc3HE\nk75zzsURT/rOORdHPOk751wc8aTvnHNxxJO+c87FEU/6zjkXRzzpO+dcHPGk75xzccSTvnPOxRFP\n+s45F0ciSvoi0ltEFovIMhEZmcP0xiLyjYjME5FvRaRR2LRLRWRp8Lg0msE755wrmHyTvohUBEYD\nZwItgcEi0jLbbI8BY1W1LXAv8GCwbB3gLqAT0BG4S0RqRy9855xzBRFJSb8jsExVf1PVPcA4oF+2\neVoCk4PnU8KmnwF8pappqroZ+AroXfSwnXPOFUYkSb8hsCLs9crgvXBzgQHB8/5AdRGpG+GyiMhw\nEZkhIjM2bNgQaezOOecKKFoNuTcB3URkNtANWAXsi3RhVX1RVVNUNaV+/fpRCsk551x2CRHMswo4\nKux1o+C9LKq6mqCkLyLVgPNUdYuIrAK6Z1v22yLE65xzrggiKelPB5qLSFMRqQQMAj4On0FE6olI\naF23AWOC55OA00WkdtCAe3rwnnPOuRjIN+mr6l5gBJasFwLjVXW+iNwrIn2D2boDi0VkCXA4cH+w\nbBpwH3bgmA7cG7znnHMuBkRVYx3DAVJSUnTGjBmxDsM558oUEZmpqin5zedX5DrnXBzxpO+cc3HE\nk75zzsURT/rOORdHPOk751wc8aTvnHNxxJO+K7y9e+Gf/4ShQ2HPnlhH45yLgCd9Vzj//je0bw9X\nXw2vvw6ffx7riErep59Camqso3CuQDzpu4JZsQIGDYLu3WHLFhg3Do44whJ/tOzYAZMmwW23QY8e\n8Nln0Vt3tEybBn36QHIyTJkS62ici1gkA665su6bb6BePUhKKvw6du2Cxx6DBx8EVbj7brj5Zjj0\nUJgxA0aNgg0boDCjpO7YYUl0yhT49luYPh327YOEBKheHS67DBYtgtql5P47qnDLLXD44VCnDpx+\nOoweDcOHxzoy5/KnqqXq0b59e3VRtHq1auXKqrVqqS5dWrh1fPCBapMmqqA6cKBqauqB03/+2aaN\nGlXwdY8YoZqQYMsnJKj+4Q+qf/ub6pdfqu7YoTprlmrFiqpXXFG42IvDhx9avM8/r7pli+oZZ9jr\n665TzcjIf/lNm1Rvv121WzfVlSuLPVwXH4AZGkGOjXmSz/4os0n/vfdUp0+PdRQHu+46S5q1a6u2\nbq26fXvBln/oIfuatG6tOnly7vOlpKi2a1ewdc+Zs/9AMmmSJfmc/PWvNt/UqQVbf3HIyFA94QTV\n44/fn+AzMmw/g+qZZ9qBICehZF+9us1bqZLqSSeppqeXXPyu3PKkX5L+9z8rpaakxDqSA4VK+cOG\nWcm5QgVLsJmZkS3/0kv2FRk8OP8S7DPP2Lxz5kQe38UXq1arppqWlvd8O3aoNm6s2qJF7BPkCy/Y\n55ww4eBpzz9v34OWLVV//XX/+2lpqnfcsT/ZDxyoOm+e6r/+Za+HDy+5+EvCpk35/09d1HnSL0kj\nRtiuBKvqKKjt21Ufeyz3EmJhXX+9lfKXLbPXDz9sMT78cP7Lvv++HSR691bdvTv/+TduVE1MVL3h\nhshiS0212G68MbL5P//cYr/33sjmLw47dqgecYRVQeV24PzmGzurqltX9ZNPVP/+d9UaNSz288+3\nZB9u5Eib9sILxR9/SVi3TvWoo1QbNFBdvDjW0cQVT/olZe1aK0337WulvJtuKvg6Qsl48ODoxRUq\n5Q8duv+9zEzVCy6wZD5pUu7LfvONVT2cfHLuVS45Oe881fr1VffsyX/e66+3/fV//xf5+i+80OJa\ntCjyZaLpvvvs//Sf/+Q935Ilqscdt78gcN55qnPn5jzv3r3WJpCYqDptWvRjLkl79lg7ReXKqvXq\nWeJfsqRg6/jyS9t3jz4a+RlpuC1bYvP92L7d2qJat1ZdsaLkt6+e9EvOrbeqilip5txzrSQYSWNe\nyL59qk2bqlatav+Ot96KTlw33GAl6eyNtzt2qLZpY6XR8CqIkOnTrcqlVSs7TS+Ijz+2z/Dxx3nP\nl5Zmn/eSSwq2/jVrrEG6e/fCJYSiWL/eqmfOPTey+dPSVP/xj8iquzZtUm3WTPXII+1gXVaF2jXe\neMPOeOvXL1jif/ZZ+87WqmXr6dOnYN/Bjz6y319CgurEiYX7DAWVmWm/2QYN9h/k//a3ktl2Np70\nS0JamiWCCy6w1xMm2C797LPI1xGqtnjrLas2qFnz4N4xBbVmjZW2Lr005+nLltkPq23bA0vyixZZ\nCa1x48L1KtmzR/Www1QHDMh7vvvvt8+cvaojEqE69TFjCr5sUVxzjSWkhQuLZ/1z56oeeqhq586R\nVaeVNmPH2v/l+uv3v/fzz/Z9atgw755jGRmqV11ly59zjurWrdYTLDFR9eijVX/4Ie9tp6VZAQLs\nO52UZPuyuM+cZs2y/xeotm9vZ4B9+thvIAb/Q0/6JSF0uh8qze3ebV/ygQMjX8c556gefrgt++uv\ndhDp1s1O+wvrxhtzLuWHmzjRzlAGD7bSyooV9gOrX7/gp+TZt52YaHX8Odm1y34UZ55ZuPXv22c/\ntDp1rPRdEpYutdLjn/9cvNsZN86+T1ddVbzbibaZM62Q0b37wVV78+blnfjT0lR79bLPffPNB37v\n//tf6yqckKD6xBM5n9198omdIVWsaO0nu3dbleuxx9rZ7C+/RPezqtr3bvhw+/3Uq2cdHkJxT5xo\nn+Wdd6K/3Xx40i9u27dbY93ZZx/4/nXXWb1zJL0Xli+3L84dd+x/77XXNOLG1pysXatapYrqH/+Y\n/7wPPGDb+vvfrWdM9epWeimKuXNtnc88k/P0UEk9r+6f+Zk/3w4sF19c+HUUxAUXWMmxJKpebr7Z\n9s8rrxT/tqJhwwYrLBx1lDXi5mTuXPutNGp0YOJfssS6viYm5v5509JU+/WzfdKv3/7f1ebNdiYb\n6k48Y8aBy/32mx0MGjSw31lh7dxpvfNmzLCz8ocftrPkihXtt75584Hz79tnVXWnnFLwbX31Vf7t\nRXnwpF/cnnhCc2zUmzXL3v/nP/Nfx223WaNqeGNmZqb18khMtBJUQf31r7bOSErrmZnWyAiqhxyi\nOmVKwbeXkxNPtNPd7PbuVW3e3Lq2FrVO/o47LO4vvyzaevLz00/7D4wlISPDSr6VKuVfrRFrGRmq\nPXvadye/a1TCE/+yZXbQD/Vy+ve/8142M9N+bwkJVvIfPdqSecWKdt1Dbt14f/7ZEvSxx+Z+QAq3\nYIHqZZepdupkibtaNc2qpw9/nHpq3mcQjzyiBe7Jl55ubXtJSYX+bXjSL07p6fal69794GmZmVav\n2LFj/uuoX99KL9lt3GjrP+EE1d9/jzyugpTyQ7ZtUx0ypGDtEPl56qmcv/QffGDvjx9f9G3s2mUH\nkGbNrFpk8mTb3rp1RasaC5eZaf/j+vVtP5WUjRv3XwF96KHWxpKSonrWWVa6vekmSyyx6sUUErpo\n7tVXI5t/zhxL8ocdlvP1DPn54Qc7qwDraBDJxZD/+Y/9Jk480doKcrJwoVVzilgHg9NOs9/E9dfb\n2fBLL9lV2NOm2RlEfkl5wwY7EF59deSfbdQo+1xffBH5Mtl40i9OoSqK3EqZobOABQtyX8fbb9s8\nuXWd/PJLmz5iRORx3XRT5KX84rR+/cHdVzMz95egopWUp0yxH1f2kliorrVFC6t++/LLwpWePvtM\n86yqKk7Ll9vV0DfeaI2UvXurJidbNUroMx9+eO5tJ0W1eXPejZGh729Bvp+qlvjr17cDWGGuS9m0\nybZdkIv0Pv/cvo/dullhIWThQtWLLtqf7EeOtIQdDZdcYtWlkRQWtmyxg+GppxbpDNiTfnHJyLDE\n1aFD7v+gtWvt1PPWW3NfT5cuqsccY3WAubn+evsXff55/nGtW2clmoJ2gywu555rSSnUfXXqVPss\no0dHdztpaVbCnzzZSvzPPKN6552qf/mLVV01bGjb7dzZ6kwj+VEtXGi9dapXt/9RaetNk5lpdcwJ\nCVYijbannrJECNabrHlz61l27rk2BtKtt9p3rWvXyK7JyG7PnpLvcvvWW/Z5zj3XqmaGDLECUtWq\n9nmi3Snghx9se889l/+8t91m82ZvlyggT/rF5c03NdfL8MP16WNVNDmVaufNs3U89lje69i1y05j\njzgi/y/lzTfbl7i0XAUZGpTs00/tdZ8+VvouSHVVNKSnW/tKo0YWT5cuql9/fXDSyciw6qdTT7X5\nEhOtFFhcXTSj4a67LNYPP4zeOn/4wQ4mvXrZ1c8jRthFcT17WoPp4YdbgaZ5c+saXJY8/fT+s8FD\nD1W95Zbi6wGWmWljUbVtm/cBbuVKO4BedFGRN+lJvzjs22dJuFWrvEvoqjaMQW51dFdeaafokZya\nz5ljjXo9etjYLh98oPrdd5bc09LsC7VunX2JS6o3SyT27LHT+IEDrbcNqN5zT+ziSU+3s4xQyb9r\nV7vyeN06u27gqKPs/UaN7KKqtWtjF2ukdu+2hr8jjij4hXQ52bTJ6sybNDm4V0q4ffvy//6XVs88\nYxdPlUR33xdftO/U99/nPs+f/mS/799+K/LmPOkXh9DFV2++mf+86enWl3zQoAPf37bNegXkduFU\nTkaPttJVTj0JEhJsbJcKFWLfsJddqPvquedaaSZa9aVFkZ5uV36Gkn+FCprVI+ODDwp2NXVpMHu2\nfQeKWq2XmWlDiSQmWo8lV3Q7dthvM7cquPnz7fsXfkFbEXjSj7bMTKvHb9Ys8sQwYoSV6MNLTf/8\np+32H38s2PbT0+1UcPZsa/x9803VJ5+0+sDLL49NY2N+Zs/ef3AqaINfcdu1yw6mI0eW7iqcSNx5\np0Y0/EVennzS1vHkk9GLy1nbUKVKOZ9Z9OljB4UoFYY86Wc3Z44NIPbtt4VbPtSbpiCjIc6YceAy\nmZlWL5qcXPINWbGSlGSlmSicvrpc7N5t4ykdeWThhjT+73+thN+vX/x8L0vKggWWAx566MD3Qx0b\nHnggapvypB9u3z7rLhi6COmjjwq2/OzZVtfbsGHBuoqFkvzJJ9vr776zGF56qWDbL8umTi35cXLi\n0cyZVgVYkGpDVTsLbdLErgXwMfCLR/futo9DnTpC3ZcbNoxqxwZP+uFeecU+6hNP2M6uWDHyC0om\nTLBG0kaNLPkX1KOP2rYXLbILQGrWLNhwxc5FKnSVcqjHVH4yM1X797c2gYJWN7rIjR+vBwzEGLp5\nTpSH2ohq0gd6A4uBZcDIHKYfDUwBZgPzgLOC9xOB14GfgYXAbfltK+pJPy3Nugp27mxf8u3b7Yo7\nsIScm8xM1QcftPk6dSp897TVq+0gc/nldgp97bWFW49z+UlPtzPLBg3y7n0TErpy+vHHiz+2eLZn\nj/WwOvtse968ufUAjNZFioGoJX2gIvAr0AyoBMwFWmab50XgyuB5SyA1eH4RMC54fiiQCjTJa3tR\nT/pXX211yuHjmqen2yBaYBdmZK/HTE/fP1Tr4ME26FJRnH22ZjVolvVGQ1e6TZ9uhYxhw/KfLzHR\nGhO9Hr/4/f3vdsFbaEC9Tz6J+iYiTfoJ5K8jsExVfwMQkXFAP2BB2DwK1Aie1wRWh71fVUQSgCrA\nHmBbBNuMjjlz4Lnn4KqrIClp//uHHAJvvw116sDDD8PGjfD885CQAOvXQ//+MG0a3Hcf3H47iBQt\njksvhc8+gx494IQTirYu5/KSkgK33goPPABpaVCpUs7zTZsGRxwBr71W9O+3y98VV8D998Ojj8Ip\np8DZZ8cslEiSfkNgRdjrlUCnbPPcDXwpItcAVYFewfvvYQeINVhJ/wZVTcu+AREZDgwHOProowsQ\nfh4yM+Hqq6FuXUve2VWsCP/8Jxx2GNx7r/1AbrsNBg60xP+vf8H550cnlr59oU8f+Otfo7M+5/Jy\n552wZAnMn5/7PEceCaNHW8HHFb+jjrI88OGH8MgjMT3QRpL0IzEYeE1VHxeRk4E3RKQ1dpawD2gA\n1Aa+E5GvQ2cNIar6IlZFREpKikYlojfesNLMmDFQq1bO84jAPffYgeG662DCBGjQAL77Dtq3j0oY\ngJ1ZfPxx9NbnXF4OOcQKLa50GTUKLrkEOmUvM5esSJL+KuCosNeNgvfC/Qlr7EVVfxCRykA9rE7/\nC1XNANaLyH+AFOA3itOWLXDLLXDSSVa1kp9rr7US//vvw1NPWeJ3zrloatzYHjFWIYJ5pgPNRaSp\niFQCBgHZi63/B5wKICItgMrAhuD9nsH7VYGTgEXRCT0Pd90FGzbY6WuFSD4iMGiQlY484TvnyrF8\nM6Kq7gVGAJOwbpfjVXW+iNwrIn2D2f4KXCEic4F3gKFBa/JooJqIzMcOHq+q6rzi+CBZ5s2DZ5+F\nv/wFkpOLdVPOOVfWiOXm0iMlJUVnzJhRuIVVrWV80SJYvNgbqZxzcUNEZqpqSn7zRasht3R48034\n/nt4+WVP+M45l4MIK7zLgK1b4eabrWV82LBYR+Occ6VS+Un6u3ZZb51nn4288dY55+JM+aneOeII\nu/DBOedcrrxI7JxzccSTvnPOxRFP+s45F0c86TvnXBzxpO+cc3HEk75zzsURT/rOORdHPOk751wc\nKXUDronIBuB/RVhFPWBjlMKJNo+tcDy2wvHYCqesxtZYVevnt4JSl/SLSkRmRDLSXCx4bIXjsRWO\nx1Y45T02r95xzrk44knfOefiSHlM+i/GOoA8eGyF47EVjsdWOOU6tnJXp++ccy535bGk75xzLhee\n9J1zLo6Um6QvIr1FZLGILBORkbGOJ5yIpIrIzyIyR0QKedf3qMYzRkTWi8gvYe/VEZGvRGRp8Ld2\nKYnrbhFZFey7OSJyVknHFcRxlIhMEZEFIjJfRK4L3i8N+y232GK+70Sksoj8JCJzg9juCd5vKiL/\nDX6v74pIpVIU22sisjxsv7Ur6djCYqwoIrNF5NPgddH3m6qW+QdQEfgVaAZUAuYCLWMdV1h8qUC9\nWMcRFs8pQDLwS9h7jwAjg+cjgYdLSVx3AzeVgn12JJAcPK8OLAFalpL9lltsMd93gADVgueJwH+B\nk4DxwKDg/eeBK0tRbK8B58f6OxfEdSPwNvBp8LrI+628lPQ7AstU9TdV3QOMA/rFOKZSS1WnAmnZ\n3u4HvB48fx04t0SDIte4SgVVXaOqs4Ln24GFQENKx37LLbaYU7MjeJkYPBToCbwXvB+r/ZZbbKWC\niDQCzgZeDl4LUdhv5SXpNwRWhL1eSSn50gcU+FJEZorI8FgHk4vDVXVN8HwtcHgsg8lmhIjMC6p/\nSrz6JDsRaQKciJUMS9V+yxYblIJ9F1RRzAHWA19hZ+VbVHVvMEvMfq/ZY1PV0H67P9hvT4rIIbGI\nDRgF3AJkBq/rEoX9Vl6SfmnXRVWTgTOBq0XklFgHlBe1c8fSUuJ5DjgGaAesAR6PZTAiUg14H7he\nVbeFT4v1fsshtlKx71R1n6q2AxphZ+UnxCKOnGSPTURaA7dhMXYA6gC3lnRcInIOsF5VZ0Z73eUl\n6a8Cjgp73Sh4r1RQ1VXB3/XABOyLX9qsE5EjAYK/62McDwCqui74YWYCLxHDfSciiVhSfUtVPwje\nLhX7LafYStO+C+LZAkwBTgZqiUhCMCnmv9ew2HoH1WWqqruBV4nNfusM9BWRVKy6uifwFFHYb+Ul\n6U8Hmgct25WAQcDHMY4JABGpKiLVQ8+B04Ff8l4qJj4GLg2eXwp8FMNYsoQSaqA/Mdp3QX3qK8BC\nVX0ibFLM91tusZWGfSci9UWkVvC8CnAa1uYwBTg/mC1W+y2n2BaFHcQFqzMv8f2mqrepaiNVbYLl\ns8mqOoRo7LdYt05HsZX7LKzXwq/A7bGOJyyuZlhvornA/NIQG/AOdrqfgdUL/gmrL/wGWAp8DdQp\nJXG9AfwMzMMS7JEx2mddsKqbecCc4HFWKdlvucUW830HtAVmBzH8AtwZvN8M+AlYBvwLOKQUxTY5\n2G+/AG8S9PCJ1QPozv7eO0Xebz4Mg3POxZHyUr3jnHMuAp70nXMujnjSd865OOJJ3znn4ognfeec\niyOe9J1zLo540nfOuTjy/7nIFs2H3YY2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO47jJCEu7o7jOEnI/wNQM8ifQ3cuwQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S9iJ-HGnRFW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97a1619b-7b4a-40c6-8d73-314328b1f7ef"
      },
      "source": [
        "prunedModel.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 442,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5402393188476563, 0.8933333333333333]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 442
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8o16S6NPmfn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "b660bfab-17d5-4fe4-cd75-b975cc3a2048"
      },
      "source": [
        "#testing\n",
        "test_eval = prunedModel.evaluate(test_three_data, test_three_label_one_hot, verbose=0)\n",
        "print('Test loss:', test_eval[0])\n",
        "print('Test accuracy:', test_eval[1])"
      ],
      "execution_count": 443,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.4805153809388479\n",
            "Test accuracy: 0.8973333334922791\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z06QupNsYeLZ",
        "colab_type": "code",
        "outputId": "295dc294-1f16-458c-db8b-9aac29ccee97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "layer_names"
      ],
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['conv8_1']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 330
        }
      ]
    },
    {
      "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": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "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": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "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": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "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": [
        " "
      ]
    }
  ]
}