Random_ScheduleTransferConv8_1_K_256.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Random ScheduleTransferConv8_1_K_256.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/KProj/blob/master/Random_ScheduleTransferConv8_1_K_256.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",
        "outputId": "a9c510fb-642f-4853-952b-44dbd4b3d9bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "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": "89eced24-c507-4695-d363-e7cac1e3c538",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "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": 4,
      "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": "46wpZt2uHu1Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_three_data,train_three_label,test_three_data,test_three_label = (np.load('train_three_data_LoAcc.npy'),\n",
        "#                                                                       np.load('train_three_label_LoAcc.npy'),\n",
        "#                                                                       np.load('test_three_data_LoAcc.npy'),\n",
        "#                                                                       np.load('test_three_label_LoAcc.npy'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AaX5l7XI9P4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDQiwalsYIGo",
        "colab_type": "code",
        "outputId": "b0596bf3-0f7b-49f7-f356-de2dc2e0facf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_three_label"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 3, 5, ..., 0, 5, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCQfV4TvZh1r",
        "colab_type": "code",
        "outputId": "722c4d68-5f95-4c22-8874-817505c01153",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_three_label"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 5, 0, ..., 5, 0, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gK_tiIWd2Nf",
        "colab_type": "code",
        "outputId": "76fb422b-88fa-476b-c333-9755a04a10c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 11,
      "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": 11
        },
        {
          "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": "036be266-2905-4dc2-fbf2-f5438a38e8d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "plt.hist(test_three_label)"
      ],
      "execution_count": 12,
      "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": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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": "SO2qr4-1KuwC",
        "colab_type": "code",
        "outputId": "78a95a57-0fb1-4cda-810f-d4488aeb6d5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "train_ground"
      ],
      "execution_count": 16,
      "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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOFKlZWKeH25",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lr931MtCYGoN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ssi32yj-d1iX",
        "colab_type": "text"
      },
      "source": [
        "## Define the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUISHV2weN0t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x, y = 32, 32\n",
        "inChannel = 3\n",
        "input_img = Input(shape = (x, y, inChannel))\n",
        "ep = 10 \n",
        "weight_decay = 1e-4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ich0RbYnebwW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cifar10Classifier(input_img):\n",
        "  \n",
        "  conv1_1 = (Conv2D(64, (3,3),name = 'conv1_1' ,padding='same', \n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:],\n",
        "                    activation='relu'))(input_img)\n",
        "  conv1_2 = BatchNormalization(name = 'conv1_2')(conv1_1)\n",
        "  pool1_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool1_3')(conv1_2) #14 x 14 x 32\n",
        "  conv2_1 = (Conv2D(128, (3,3),name = 'conv2_1' ,padding='same',\n",
        "                    activation= 'relu',kernel_regularizer=regularizers.l2(weight_decay)))(pool1_3)\n",
        "  conv2_2 = BatchNormalization(name = 'conv2_2')(conv2_1)\n",
        "  pool2_3 = MaxPooling2D(pool_size=(2, 2), name = 'pool2_3')(conv2_2) #14 x 14 x 32\n",
        "  \n",
        "  conv3_1 = (Conv2D(256, (3,3), padding='same',name = 'conv3_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool2_3)\n",
        "  conv3_2 = BatchNormalization(name = 'conv3_2')(conv3_1)\n",
        "  conv4_1 = (Conv2D(256, (3,3), padding='same',name = 'conv4_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv3_2)\n",
        "  conv4_2 = BatchNormalization(name = 'conv4_2')(conv4_1)\n",
        "  pool4_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool4_2')(conv4_2) #14 x 14 x 32\n",
        "  \n",
        "  conv5_1 = (Conv2D(512, (3,3), padding='same',name = 'conv5_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool4_2)\n",
        "  conv5_2 = BatchNormalization(name = 'conv5_2')(conv5_1)\n",
        "  conv6_1 = (Conv2D(512, (3,3), padding='same',name = 'conv6_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv5_2)\n",
        "  conv6_2 = BatchNormalization(name = 'conv6_2')(conv6_1)\n",
        "  pool6_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool6_2')(conv6_2) #14 x 14 x 32\n",
        "  \n",
        "  conv7_1 = (Conv2D(512, (3,3), padding='same',name = 'conv7_1',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay), activation='relu'))(pool6_2)\n",
        "  conv7_2 = BatchNormalization(name = 'conv7_2')(conv7_1)\n",
        "  conv8_1 = (Conv2D(512, (3,3), padding='same',name = 'conv8_1' ,activation= 'relu',\n",
        "                    kernel_regularizer=regularizers.l2(weight_decay)))(conv7_2)\n",
        "  conv8_2 = BatchNormalization(name = 'conv8_2')(conv8_1)\n",
        "  pool8_2 = MaxPooling2D(pool_size=(2, 2), name = 'pool8_2')(conv8_2) #14 x 14 x 32\n",
        "\n",
        "\n",
        "  flat = Flatten()(pool8_2)\n",
        "  fc1 = Dense(4096, activation='relu', name = 'fc1')(flat)\n",
        "  fc2 = Dense(4096, activation='relu', name = 'fc2')(fc1)\n",
        "  fc3 = Dense(1000, activation='relu', name = 'fc3')(fc2)\n",
        "\n",
        "\n",
        "  \n",
        "  out = Dense(num_classes, activation='softmax', name = 'out')(fc3)\n",
        "  \n",
        "  return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_0LHS-Xf7aN",
        "colab_type": "text"
      },
      "source": [
        "## Define functions for calculation of DI score\n",
        "getDIScoreOfLayer(layer_output, true_labels), \n",
        "getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsLk-f7sf6zd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDIScoreOfLayer(layer_output, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  rho = 1e-6\n",
        "  mu = layer_output.mean(axis=0)\n",
        "#   print(mu.shape)\n",
        "  mu = mu.reshape((1,-1))\n",
        "  centered_data = layer_output - mu\n",
        "  S_bar = np.matmul(centered_data.T,centered_data)\n",
        "  # compute between class matrix\n",
        "  total_classes = true_labels.max()\n",
        "  S_B = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  S_W = np.zeros(shape = (mu.shape[1],mu.shape[1]))\n",
        "  unique_classes = np.unique(true_labels)\n",
        "  unique_classes_list = np.ndarray.tolist(unique_classes)\n",
        "  for l in unique_classes_list:\n",
        "    class_l_index = np.where(true_labels == l)\n",
        "    class_l_output = layer_output[class_l_index[0],:]\n",
        "    mu_l = class_l_output.mean(axis=0)\n",
        "    mu_l = mu_l.reshape((1,-1))\n",
        "    total_examples_this_class = class_l_index[0].shape   \n",
        "    ss = mu_l - mu\n",
        "    S_B = S_B + total_examples_this_class*np.matmul((mu_l - mu).T,(mu_l - mu))\n",
        "\n",
        "  DI_Score = np.trace(np.matmul(np.linalg.inv(S_bar + rho*np.eye(S_bar.shape[0])),S_B))\n",
        "  return DI_Score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubrpaIpFgBzn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDIScoreOfLayerCNNLayer(cnn_outputs, true_labels):\n",
        "  # computeScatterMatrix\n",
        "  number_of_filters = cnn_outputs.shape[3]\n",
        "  cnn_di_scores = []\n",
        "  for filt in range(0,number_of_filters):\n",
        "    outputs_filter = cnn_outputs[:,:,:,filt]\n",
        "    outputs_filter_reshaped = np.reshape(outputs_filter, newshape=(cnn_outputs.shape[0],-1))\n",
        "    this_filter_score = getDIScoreOfLayer(outputs_filter_reshaped, true_labels)\n",
        "    cnn_di_scores.append(this_filter_score)\n",
        "  return cnn_di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQ5H0p_KjVCw",
        "colab_type": "text"
      },
      "source": [
        "## define a function to get and plot di score of a layer. It takes inputs - trained model, layer name to get DI scores and returns a plot with some statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9MHI1LFjUZs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def createCNNLayerDIScorePlot(trained_model, layer_name, data_to_use = train_three_data , data_label =train_three_label ):\n",
        "#   print(trained_model.layers)\n",
        "#   get_cnn1_output_function = K.function([trained_model.layers[0].input],\n",
        "#                                   [trained_model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "  \n",
        "  model = trained_model\n",
        "  intermediate_layer_model = Model(inputs=model.input,\n",
        "                                 outputs=model.get_layer(layer_name).output)\n",
        "  cnn1_output = intermediate_layer_model.predict(data_to_use)\n",
        "#   cnn1_output = get_cnn1_output_function([data_to_use])[0]\n",
        "  di_scores = getDIScoreOfLayerCNNLayer(cnn1_output, data_label)\n",
        "  # plot DI score\n",
        "  y_pos  = np.arange(len(di_scores))\n",
        "  plt.bar(y_pos, di_scores, align='center', alpha=0.5)\n",
        "  plt.xlabel('FilterNumber')\n",
        "  plt.ylabel('DI Score')\n",
        "\n",
        "  print('Mean of DI SCore: ',np.mean(di_scores))\n",
        "  print('STD of DI Score: ', np.std(di_scores))\n",
        "  print('MAX of DI Score: ', np.max(di_scores))\n",
        "#   plt.title('DI score - Data - D_L, Network -N_L ' + layer_name)\n",
        "  plt.title('DI score ' + layer_name)\n",
        "\n",
        "#   plt.ylim([0,1])\n",
        "  return di_scores"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCCl7-vOfSZ9",
        "colab_type": "text"
      },
      "source": [
        "## load pretrained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqIkmDGkfRtD",
        "colab_type": "code",
        "outputId": "4af47f71-5df4-4eed-c89a-6a9055f2edc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "loaded_model_low_accuracy = keras.models.load_model('vgg16lowAccuracyModelDataV3.h5')\n",
        "loaded_model_high_accuracy = keras.models.load_model('vgg16HighAccuracyModelData.h5')\n",
        "\n",
        "# loaded_model_low_accuracy = keras.models.load_model('vgg16lowAccuracyModelDataSchedule60EpochRun2.h5')\n",
        "# loaded_model_high_accuracy = keras.models.load_model('vgg16HighAccuracyModelDataForTransferHiEpoch.h5')\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7J98O5qfi8Z",
        "colab_type": "code",
        "outputId": "807a00b6-95ef-4b89-bbc5-f84f86030515",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "loaded_model_low_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4554041419029236, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rZ5L_L9Uc3v",
        "colab_type": "code",
        "outputId": "6aca0ebb-24f2-44dd-c7b5-66196f632e26",
        "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": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4510676923592885, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rddbxdrtXcR7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_model = keras.models.load_model('vgg16lowAccuracyModelDataV3.h5')\n",
        "# temp_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsXuB6NbXmGB",
        "colab_type": "code",
        "outputId": "8342ae7f-67f5-4fed-9377-fbd3d6693ce6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "valid_ground"
      ],
      "execution_count": 27,
      "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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVSvyoZ8Uw_y",
        "colab_type": "code",
        "outputId": "fc106805-a29d-493f-e98c-ff7f50a1bc78",
        "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": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4510676923592885, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAY0LGCNSMBc",
        "colab_type": "code",
        "outputId": "6d728d1f-65a4-4a48-c1cc-cf66be41dac6",
        "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": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.825879515329997, 0.3953333332538605]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jsm88PV3TlmL",
        "colab_type": "code",
        "outputId": "65788bff-0e68-405d-bcb1-0bc8ee0a4699",
        "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": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3.1267358474731446, 0.3673333334128062]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "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": "9daabdcb-a061-4142-866e-24801a5a6c24",
        "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": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4510676923592885, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzbIXvcEoNMj",
        "colab_type": "code",
        "outputId": "efc92118-0f9f-4a59-d91b-ad10947ffeec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1054
        }
      },
      "source": [
        "loaded_model_low_accuracy.summary()"
      ],
      "execution_count": 32,
      "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": "13a6314f-1249-427e-9115-61cefceedb01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "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": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.8694144080993281\n",
            "STD of DI Score:  0.6205526663901321\n",
            "MAX of DI Score:  1.5949378094135989\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\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": "21599e10-214c-4ad8-a56d-1ad16227824f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "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": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.09890966478271793\n",
            "STD of DI Score:  0.08029777448636094\n",
            "MAX of DI Score:  0.2144610349284688\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\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": "514cb203-a96a-4706-d52c-b03ed561d1df",
        "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": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "512 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1cAvZpx9G8_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Ks = [256]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3zlCJ3R9jjk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_low_accuracy_model = loaded_model_low_accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZY28-KyEAhdg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_high_accuracy_model = loaded_model_high_accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCeCjHCzbJj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# low_accuracy_model_weight.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLHD3oVSU_yA",
        "colab_type": "code",
        "outputId": "5ce8b876-f4c0-4e65-e4d6-b094d40dfddb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Ks"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[256]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "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",
        "  this_high_accuracy_di_scores_sorted_list = np.random.choice(len(all_di_acores_high_accuracy),Ks[l])\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 = np.random.choice(len(all_di_acores_low_accuracy),Ks[l])\n",
        "\n",
        "  bottom_channels = this_low_accuracy_di_scores_sorted_list[0:Ks[l]]\n",
        "  \n",
        "  # now replace the weights\n",
        "  low_accuracy_model_weights_bias = copied_low_accuracy_model.get_layer(this_layer_name).get_weights()\n",
        "  high_accuracy_model_weights_bias = copied_high_accuracy_model.get_layer(this_layer_name).get_weights()\n",
        "\n",
        "  low_accuracy_model_weight,low_accuracy_model_bias = low_accuracy_model_weights_bias[0],low_accuracy_model_weights_bias[1]\n",
        "  high_accuracy_model_weight,high_accuracy_model_bias = high_accuracy_model_weights_bias[0],high_accuracy_model_weights_bias[1]\n",
        "#   print(low_accuracy_model_weight.shape,low_accuracy_model_bias.shape)\n",
        "  low_accuracy_model_weight[:,:,:,bottom_channels] = high_accuracy_model_weight[:,:,:,top_channels]\n",
        "#   print(bottom_channels)\n",
        "  low_accuracy_model_bias[bottom_channels] = high_accuracy_model_bias[top_channels]\n",
        "  \n",
        "  copied_low_accuracy_model.get_layer(this_layer_name).set_weights([low_accuracy_model_weight, high_accuracy_model_bias])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCGk9cmhU5vO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# copied_low_accuracy_model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OodsSlYDCesc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_accuracy_model_weights_bias.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AtLgL9ICGJE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# high_accuracy_model_bias"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzN_W73aldFU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# # (discores_high_acc_model)\n",
        "# K= 50 # how many channels to pick\n",
        "# high_acc_di_sorted = np.argsort(discores_high_acc_model)\n",
        "# high_acc_di_sorted_list = np.ndarray.tolist(high_acc_di_sorted)\n",
        "# top_channels = high_acc_di_sorted_list[-K:]#np.random.choice(32,K)#\n",
        "# print('Top Channels: ',top_channels)\n",
        "\n",
        "\n",
        "# low_acc_di_sorted = np.argsort(discores_low_acc_model)\n",
        "# low_acc_di_sorted_list = np.ndarray.tolist(low_acc_di_sorted)\n",
        "# low_channels = low_acc_di_sorted_list[0:K]#np.random.choice(32,K)\n",
        "# print('Low Channels: ', low_channels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_BDN-T-BaGz",
        "colab_type": "text"
      },
      "source": [
        "#####  get low accuracy DI channels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z5DgBrzXCGB8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# discores_low_acc_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5B34pIwCbXN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# plt.plot(discores_low_acc_model,'-*')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRrtIpLVBdK4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SD8nswMnxWVC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# total_channels = len(discores_high_acc_model)\n",
        "# total_channels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsCtxzRVokuR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "enable_training = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sk_0a6JlFux5",
        "colab_type": "code",
        "outputId": "3fb78e09-f0e1-4324-e06d-0df15eb6ccbf",
        "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": 58,
      "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": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZchwmWh7la9",
        "colab_type": "code",
        "outputId": "7160d72b-3431-4d92-9508-681f7658cb5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1054
        }
      },
      "source": [
        "copied_low_accuracy_model.summary()"
      ],
      "execution_count": 59,
      "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": "s17-wc7ff6Q0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # #high_acc_output = highAccuracyModelTillConv2(input_img)\n",
        "# # modelFilt = Model(input_img, \n",
        "# #               cifar10ClassifierTransfer(input_img))md\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",
        "\n",
        "# #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",
        "# #training\n",
        "# batch_size = 64\n",
        "# opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "# # model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "# modelFilt.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n",
        "# modelFilt.summary()"
      ],
      "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": "markdown",
      "metadata": {
        "id": "wRWAVstvGhht",
        "colab_type": "text"
      },
      "source": [
        "#### # now replace weights in conv2d layer for filters correspondng to lowest 10 DI scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcnUQrPtGay_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fev8cENxbr-P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "file_save_name = 'weights_top_k_model2.hdf5'\n",
        "mcp_save = ModelCheckpoint(file_save_name, save_best_only=True, monitor='val_acc', mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BTKJa7Zbwtc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.0001\n",
        "    if epoch > 30:\n",
        "        lrate = 0.0001/3\n",
        "    if epoch > 100:\n",
        "        lrate = 0.0001/9\n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdiZwWSth97S",
        "colab_type": "code",
        "outputId": "a0e4ef2a-bf39-4ec4-87cc-1d13f795983a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.1070284808476765, 0.7726666668256124]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIOMXQsjdSNX",
        "colab_type": "code",
        "outputId": "168f90c0-8410-4866-af44-e990f0679994",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1394
        }
      },
      "source": [
        "copied_low_accuracy_model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=40,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "187/187 [==============================] - 12s 66ms/step - loss: 0.3070 - acc: 0.9319 - val_loss: 0.4563 - val_acc: 0.8740\n",
            "Epoch 2/40\n",
            "187/187 [==============================] - 9s 51ms/step - loss: 0.2806 - acc: 0.9389 - val_loss: 0.4775 - val_acc: 0.8720\n",
            "Epoch 3/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2737 - acc: 0.9426 - val_loss: 0.4693 - val_acc: 0.8850\n",
            "Epoch 4/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2625 - acc: 0.9480 - val_loss: 0.4950 - val_acc: 0.8823\n",
            "Epoch 5/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2567 - acc: 0.9463 - val_loss: 0.5050 - val_acc: 0.8827\n",
            "Epoch 6/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2485 - acc: 0.9533 - val_loss: 0.5237 - val_acc: 0.8790\n",
            "Epoch 7/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2504 - acc: 0.9520 - val_loss: 0.5048 - val_acc: 0.8837\n",
            "Epoch 8/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2370 - acc: 0.9555 - val_loss: 0.4771 - val_acc: 0.8853\n",
            "Epoch 9/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2426 - acc: 0.9539 - val_loss: 0.4826 - val_acc: 0.8867\n",
            "Epoch 10/40\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.2285 - acc: 0.9561 - val_loss: 0.5380 - val_acc: 0.8800\n",
            "Epoch 11/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2241 - acc: 0.9596 - val_loss: 0.5303 - val_acc: 0.8860\n",
            "Epoch 12/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2289 - acc: 0.9575 - val_loss: 0.5744 - val_acc: 0.8840\n",
            "Epoch 13/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2299 - acc: 0.9558 - val_loss: 0.5752 - val_acc: 0.8857\n",
            "Epoch 14/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2237 - acc: 0.9596 - val_loss: 0.5032 - val_acc: 0.8903\n",
            "Epoch 15/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2112 - acc: 0.9629 - val_loss: 0.5795 - val_acc: 0.8847\n",
            "Epoch 16/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2119 - acc: 0.9626 - val_loss: 0.5495 - val_acc: 0.8903\n",
            "Epoch 17/40\n",
            "187/187 [==============================] - 9s 51ms/step - loss: 0.2094 - acc: 0.9640 - val_loss: 0.5506 - val_acc: 0.8873\n",
            "Epoch 18/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2132 - acc: 0.9614 - val_loss: 0.5354 - val_acc: 0.8883\n",
            "Epoch 19/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2118 - acc: 0.9642 - val_loss: 0.5664 - val_acc: 0.8883\n",
            "Epoch 20/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2138 - acc: 0.9639 - val_loss: 0.5391 - val_acc: 0.8847\n",
            "Epoch 21/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2041 - acc: 0.9647 - val_loss: 0.5209 - val_acc: 0.8853\n",
            "Epoch 22/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2063 - acc: 0.9652 - val_loss: 0.5421 - val_acc: 0.8810\n",
            "Epoch 23/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1942 - acc: 0.9698 - val_loss: 0.5883 - val_acc: 0.8883\n",
            "Epoch 24/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1905 - acc: 0.9702 - val_loss: 0.6214 - val_acc: 0.8853\n",
            "Epoch 25/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2036 - acc: 0.9678 - val_loss: 0.5782 - val_acc: 0.8890\n",
            "Epoch 26/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1933 - acc: 0.9703 - val_loss: 0.5272 - val_acc: 0.8887\n",
            "Epoch 27/40\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.1870 - acc: 0.9712 - val_loss: 0.5741 - val_acc: 0.8843\n",
            "Epoch 28/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1933 - acc: 0.9691 - val_loss: 0.5543 - val_acc: 0.8897\n",
            "Epoch 29/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1889 - acc: 0.9723 - val_loss: 0.5848 - val_acc: 0.8847\n",
            "Epoch 30/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1831 - acc: 0.9735 - val_loss: 0.5324 - val_acc: 0.8913\n",
            "Epoch 31/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1849 - acc: 0.9722 - val_loss: 0.6201 - val_acc: 0.8907\n",
            "Epoch 32/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1674 - acc: 0.9779 - val_loss: 0.6155 - val_acc: 0.8870\n",
            "Epoch 33/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1644 - acc: 0.9789 - val_loss: 0.5817 - val_acc: 0.8913\n",
            "Epoch 34/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1645 - acc: 0.9794 - val_loss: 0.6309 - val_acc: 0.8917\n",
            "Epoch 35/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1609 - acc: 0.9800 - val_loss: 0.6636 - val_acc: 0.8897\n",
            "Epoch 36/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1697 - acc: 0.9768 - val_loss: 0.5907 - val_acc: 0.8913\n",
            "Epoch 37/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1615 - acc: 0.9791 - val_loss: 0.6152 - val_acc: 0.8880\n",
            "Epoch 38/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1681 - acc: 0.9766 - val_loss: 0.5672 - val_acc: 0.8903\n",
            "Epoch 39/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1600 - acc: 0.9789 - val_loss: 0.6100 - val_acc: 0.8917\n",
            "Epoch 40/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1681 - acc: 0.9786 - val_loss: 0.6071 - val_acc: 0.8913\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc1a594bba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAnK7D_Mm_dz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "copied_low_accuracy_model.load_weights(file_save_name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8ZXIw1Um_ao",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# copied_low_accuracy_model.save('final1_'+str(K)+'_'+file_save_name)\n",
        "# copied_low_accuracy_model.save('final1_Transfer1')\n",
        "copied_low_accuracy_model.save('Top128TransferReplace')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZsEELS6m_WM",
        "colab_type": "code",
        "outputId": "f8b7c45d-733e-4f30-b730-0343c49fbb80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "accuracy = copied_low_accuracy_model.history.history['acc']\n",
        "val_accuracy = copied_low_accuracy_model.history.history['val_acc']\n",
        "loss = copied_low_accuracy_model.history.history['loss']\n",
        "val_loss = copied_low_accuracy_model.history.history['val_loss']\n",
        "epochs = range(len(accuracy))\n",
        "plt.plot(epochs, accuracy, 'b', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'r', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWryT3wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqc6aYvX6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S9iJ-HGnRFW",
        "colab_type": "code",
        "outputId": "297d5bf4-b4d9-4e78-f83d-3ffda26793a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6308506917158763, 0.8916666668256124]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8o16S6NPmfn",
        "colab_type": "code",
        "outputId": "e30cfb50-32ec-41d7-a440-411ee3215646",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#testing\n",
        "test_eval = copied_low_accuracy_model.evaluate(test_three_data, test_three_label_one_hot, verbose=0)\n",
        "print('Test loss:', test_eval[0])\n",
        "print('Test accuracy:', test_eval[1])\n",
        "\n"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.5849130133787791\n",
            "Test accuracy: 0.8976666668256124\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z06QupNsYeLZ",
        "colab_type": "code",
        "outputId": "c804cef2-26e5-43d9-c4fa-4c4b68b1ebc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "layer_names"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['conv8_1']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRuSLttWnRCR",
        "colab_type": "code",
        "outputId": "741b155c-63fd-4161-9d8b-0f368376607a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "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": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-114-4cb67efdc4fa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdiscores_low_acc_modified_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreateCNNLayerDIScorePlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodelFilt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_to_replace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'modelFilt' is not defined"
          ]
        }
      ]
    },
    {
      "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": [
        " "
      ]
    }
  ]
}