ScheduleTransferConv8_1_K_256.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "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/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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "04470bf2-f873-41b3-ca04-7dd4e3922c49"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "5604ab0e-149c-4625-9a8c-a2186ed85a40"
      },
      "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": {
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O8NF2er2620g5XXfgeE5cX60xHA39KCJ8t8/127cBmE7G5EJIU5YlzVZM8RHA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\nvdxDuLu8zOOPPwLA0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c+VRdkF7nXkQczKNn1DO7O2do/z52VeHNvaz/PpmNHaoM0ipbMr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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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5652a038-59d7-4f7a-e08d-6624f680f731"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7332be16-6757-43f1-8f1c-36e44a484153"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "b1c813fa-57f2-4057-f8ca-38853e6974b8"
      },
      "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": 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PAb8f90RGrIDvJjnYvh1hWazEAKgjSV4LfB34aFX9etzzWW5V9UJVXcTgU/Sbk6zYU35J\n3gMcraqD457LGLyzqi5m8M3Ju9pp3iW3EgOw4NdNaGVo58G/Dny5qr4x7vmMUlU9A9wNbB33XJbR\npcB72/nwfcBlSf59vFMajao60m6PAt9kcGp7ya3EAPh1Ex1ob4jeAhyqqs+Mez6jkGQiyZq2/BoG\nFzr8dLyzWj5VdX1VbaiqSQZ/j++qqr8b87SWXZJz24UNJDkX2AIsyxV+Ky4AVXUSOPV1E4eA/Sv9\n6yaSfAX4T+AtSQ4n2THuOY3ApcCHGPxW+GD7uWrck1pm64C7kzzE4Bed6arq5tLIjqwFfpDkR8B9\nwLer6jvL8UQr7jJQSdJwVtwrAEnScAyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXqfwHd\n/gESZomJ0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEYcIMeqd4i1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "076b73fe-e965-42f1-e374-e55601c69d8c"
      },
      "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": 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qqpuXRnZkDfBwkm8BXwe+VFVfXo4TrbiXgUqSTs2KewYgSTo1BkCSOmUAJKlTBkCSOmUA\nJKlTBkCSOmUAJKlTBkCSOvW/lK9vO4tP9cMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4n_WiMNd_Hf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_three_data, train_three_label = shuffle(train_three_data, train_three_label )\n",
        "# test_three_data,test_three_label = shuffle(test_three_data,test_three_label )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VT_AKqQmeBk_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "le = preprocessing.LabelEncoder()\n",
        "le.fit_transform(train_three_label)\n",
        "train_three_label_cat = le.transform(train_three_label)\n",
        "test_three_label_cat = le.transform(test_three_label)\n",
        "train_three_label_one_hot = to_categorical(train_three_label_cat)\n",
        "test_three_label_one_hot = to_categorical(test_three_label_cat)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWEjK_exeEaJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# split train data and to create validation data\n",
        "train_X,valid_X,train_ground,valid_ground = train_test_split(train_three_data,\n",
        "                                                             train_three_label_one_hot,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SO2qr4-1KuwC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "88774dce-0c72-486e-ce88-51c0968e7f6b"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "864b9d66-d3b4-4ca3-f9dd-a4e570868ab3"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "72bfc9d0-f76a-4382-fcd6-5c1ccd8f882c"
      },
      "source": [
        "loaded_model_low_accuracy.evaluate(valid_X, valid_ground, verbose=0)\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4554041392008464, 0.8660000001589457]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rZ5L_L9Uc3v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3942221c-1fcd-41aa-89af-c25046ec5596"
      },
      "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": [
              "[0.45106768933931984, 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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "d28a5d59-e274-4295-ee2f-21a06291b5e2"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cbfd6d2e-de95-48f9-bead-6dd46202a617"
      },
      "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": [
              "[0.45106768933931984, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAY0LGCNSMBc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8d54717e-a3d3-460d-d670-a57adb6ab5da"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a869e4eb-e1e8-43e3-aa3b-e2fe5a193259"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "85882217-eda3-406e-9b28-fa5bc4c963d1"
      },
      "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": [
              "[0.45106768933931984, 0.8799999998410543]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzbIXvcEoNMj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1054
        },
        "outputId": "190bb82e-8bc4-4ac8-df86-b7c5af17ad6c"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "b7c48d88-5067-4840-a35a-c17b1f038c5b"
      },
      "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": 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rdzSwGaCqvpBkL2AfYOeA9yFJWkGL7Sm8ALiK3nmF36+qnzbzDwb+doB1nwMcmOSAJHvS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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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "add7b3a3-be57-44e0-d788-03eb92f2e1a9"
      },
      "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": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGjrlhMbVmUv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHVud4ykeRn8",
        "colab_type": "text"
      },
      "source": [
        "## Pick top 10 DI scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8k5Ob6gWeQ94",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyI8gOZuftmZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjMoET8nasck",
        "colab_type": "text"
      },
      "source": [
        "#### On validation data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPKQgmyXbACY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "valid_ground_actual = np.argmax(valid_ground, axis=1)\n",
        "# valid_ground_actual.shape\n",
        "# data_to_use = train_three_data , data_label =train_three_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbg5-HKhaxxj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = 'conv2d_2'\n",
        "# discores_low_acc_model_valid = createCNNLayerDIScorePlot(loaded_model_low_accuracy,\n",
        "#                                                    layer_name, data_to_use=valid_X, data_label=valid_ground_actual)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zaH4FbSFaxuc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "#layer_name = 'conv2d_2'\n",
        "\n",
        "# discores_high_acc_model_valid = createCNNLayerDIScorePlot(loaded_model_high_accuracy,\n",
        "#                                                    layer_name, data_to_use=valid_X, data_label=valid_ground_actual)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMpuiVdSf60g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# np.sort(discores_high_acc_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZueWb5hY7zZJ",
        "colab_type": "text"
      },
      "source": [
        "#### Pick top channels from the layers selected"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9HwamF674hP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "topChoose = True\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmggM8xc8c8G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8ab4480b-4bf9-48c1-9062-a7b60b68b93b"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "820f343d-ff72-4cc0-da8f-eb3ebad7717d"
      },
      "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",
        "  top_channels = this_high_accuracy_di_scores_sorted_list[-Ks[l]:]\n",
        "  \n",
        "  # do same thing for lowest DIs\n",
        "  this_low_accuracy_di_scores = all_di_acores_low_accuracy[l]\n",
        "  this_low_accuracy_di_scores_sorted = np.argsort(this_low_accuracy_di_scores)\n",
        "  this_low_accuracy_di_scores_sorted_list = np.ndarray.tolist(this_low_accuracy_di_scores_sorted)\n",
        "  bottom_channels = this_low_accuracy_di_scores_sorted_list[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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "9377e6a3-c567-4af0-b4bb-c00bfc6feb4d"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1054
        },
        "outputId": "1a39f0e6-4d61-442f-bb2d-2da4cf51d494"
      },
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7697a947-7ea6-46a9-a5e6-c36b024b826a"
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[10.704620407104493, 0.3253333332538605]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIOMXQsjdSNX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1394
        },
        "outputId": "a78c6d25-16ce-41d5-ff31-a69937caa5bb"
      },
      "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: 0.3262 - acc: 0.9266 - val_loss: 0.9044 - val_acc: 0.8443\n",
            "Epoch 2/40\n",
            "187/187 [==============================] - 9s 51ms/step - loss: 0.2863 - acc: 0.9401 - val_loss: 0.4877 - val_acc: 0.8760\n",
            "Epoch 3/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2732 - acc: 0.9408 - val_loss: 0.4575 - val_acc: 0.8783\n",
            "Epoch 4/40\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.2619 - acc: 0.9484 - val_loss: 0.5042 - val_acc: 0.8793\n",
            "Epoch 5/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2673 - acc: 0.9460 - val_loss: 0.4866 - val_acc: 0.8817\n",
            "Epoch 6/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2466 - acc: 0.9520 - val_loss: 0.5148 - val_acc: 0.8773\n",
            "Epoch 7/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2492 - acc: 0.9507 - val_loss: 0.4656 - val_acc: 0.8860\n",
            "Epoch 8/40\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.2422 - acc: 0.9531 - val_loss: 0.5038 - val_acc: 0.8840\n",
            "Epoch 9/40\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.2371 - acc: 0.9564 - val_loss: 0.5098 - val_acc: 0.8853\n",
            "Epoch 10/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2299 - acc: 0.9583 - val_loss: 0.5112 - val_acc: 0.8860\n",
            "Epoch 11/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2305 - acc: 0.9570 - val_loss: 0.5428 - val_acc: 0.8763\n",
            "Epoch 12/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2199 - acc: 0.9609 - val_loss: 0.5295 - val_acc: 0.8857\n",
            "Epoch 13/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2191 - acc: 0.9597 - val_loss: 0.5241 - val_acc: 0.8857\n",
            "Epoch 14/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2237 - acc: 0.9587 - val_loss: 0.4925 - val_acc: 0.8893\n",
            "Epoch 15/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2172 - acc: 0.9616 - val_loss: 0.5533 - val_acc: 0.8860\n",
            "Epoch 16/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2156 - acc: 0.9617 - val_loss: 0.5058 - val_acc: 0.8830\n",
            "Epoch 17/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2085 - acc: 0.9646 - val_loss: 0.5290 - val_acc: 0.8907\n",
            "Epoch 18/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2102 - acc: 0.9614 - val_loss: 0.5051 - val_acc: 0.8817\n",
            "Epoch 19/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2032 - acc: 0.9657 - val_loss: 0.5420 - val_acc: 0.8880\n",
            "Epoch 20/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2029 - acc: 0.9662 - val_loss: 0.5211 - val_acc: 0.8860\n",
            "Epoch 21/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1938 - acc: 0.9689 - val_loss: 0.5681 - val_acc: 0.8917\n",
            "Epoch 22/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1970 - acc: 0.9672 - val_loss: 0.5786 - val_acc: 0.8907\n",
            "Epoch 23/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1957 - acc: 0.9677 - val_loss: 0.5537 - val_acc: 0.8890\n",
            "Epoch 24/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1978 - acc: 0.9675 - val_loss: 0.5311 - val_acc: 0.8877\n",
            "Epoch 25/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1940 - acc: 0.9695 - val_loss: 0.5499 - val_acc: 0.8897\n",
            "Epoch 26/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1913 - acc: 0.9703 - val_loss: 0.5609 - val_acc: 0.8893\n",
            "Epoch 27/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1838 - acc: 0.9714 - val_loss: 0.6496 - val_acc: 0.8803\n",
            "Epoch 28/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1891 - acc: 0.9708 - val_loss: 0.6236 - val_acc: 0.8893\n",
            "Epoch 29/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1915 - acc: 0.9703 - val_loss: 0.5306 - val_acc: 0.8913\n",
            "Epoch 30/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1854 - acc: 0.9712 - val_loss: 0.5295 - val_acc: 0.8967\n",
            "Epoch 31/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1816 - acc: 0.9738 - val_loss: 0.5423 - val_acc: 0.8830\n",
            "Epoch 32/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1670 - acc: 0.9771 - val_loss: 0.5822 - val_acc: 0.8927\n",
            "Epoch 33/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1637 - acc: 0.9791 - val_loss: 0.6073 - val_acc: 0.8917\n",
            "Epoch 34/40\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.1641 - acc: 0.9795 - val_loss: 0.5996 - val_acc: 0.8913\n",
            "Epoch 35/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1607 - acc: 0.9820 - val_loss: 0.5877 - val_acc: 0.8940\n",
            "Epoch 36/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1629 - acc: 0.9793 - val_loss: 0.6227 - val_acc: 0.8920\n",
            "Epoch 37/40\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1625 - acc: 0.9782 - val_loss: 0.5772 - val_acc: 0.8913\n",
            "Epoch 38/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1607 - acc: 0.9801 - val_loss: 0.6007 - val_acc: 0.8907\n",
            "Epoch 39/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1643 - acc: 0.9791 - val_loss: 0.5901 - val_acc: 0.8903\n",
            "Epoch 40/40\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1664 - acc: 0.9784 - val_loss: 0.5688 - val_acc: 0.8893\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb4bf560470>"
            ]
          },
          "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "8e25b0b8-5935-4c7d-d8d4-ef054588716e"
      },
      "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": 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cSbR9O3TqBFd56+yiSrSInZETEhJ01qxZ0Q7DOZdb27bBWWfBvHkgAomJUK9e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\nMcSTvnPOxZD/B2IaHmBYdfeYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SrnjUCFwyH4633oKmTS3Gourb135Om1b0unIycaJdZXrffdC9e3T2UVKdeqot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\nkwoy428AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5S9iJ-HGnRFW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f76edfe1-144b-4e23-b985-bcd0b3a41659"
      },
      "source": [
        "copied_low_accuracy_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5294525777498881, 0.8966666668256124]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8o16S6NPmfn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3ca366b7-788e-488a-d673-d45c189207ea"
      },
      "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: 0.514632823308309\n",
            "Test accuracy: 0.8973333334922791\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z06QupNsYeLZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "279c3ab0-a2c1-49a8-e833-8feb0d488187"
      },
      "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": 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ZCguAMaXx0bmtbD5wa0S8BTwq6SFSSNxeYV1mLeXgsHbW1DEFSStJ+tASLvt2YJyksZKG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\nUDAzs4JDwczMCg4FMzMr/C+wNKsqDlWHzAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9AxL2k2cPvs",
        "colab_type": "code",
        "outputId": "05b2cbc6-ec55-4ffc-f3b7-47a0d819c614",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_1'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5324752079826454\n",
            "STD of DI Score:  0.08518245611608953\n",
            "MAX of DI Score:  0.6952950607026254\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmomNWqXclu8",
        "colab_type": "code",
        "outputId": "3a234d4d-d4e1-4831-c7a8-59acc185d0d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_2'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.5472756918767483\n",
            "STD of DI Score:  0.07193005751902407\n",
            "MAX of DI Score:  0.7008227385646286\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1iC11tO-c1ME",
        "colab_type": "code",
        "outputId": "d5e2ca0b-926e-4343-8a92-b002380a5ba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_3'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.4283726311323016\n",
            "STD of DI Score:  0.11606436878312644\n",
            "MAX of DI Score:  0.7211856134234298\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PYfByEs+iaT6ivbjdXX0WpfExzl/exTBpm/7S/pprS+HzSd5Kczbyw4GPAgyeyCZJS4kr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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3SsO1CXc09Q",
        "colab_type": "code",
        "outputId": "80ab8bbd-d50a-4858-f8aa-e0e29cf392d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "# now get DI score of a layer from high accuracy and low accuracy model\n",
        "layer_name = 'conv2d_4'\n",
        "discores_low_acc_modified_model = createCNNLayerDIScorePlot(modelFilt, layer_name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.4676357866426307\n",
            "STD of DI Score:  0.129639936407154\n",
            "MAX of DI Score:  0.9082884744169848\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Unii3onHcpx6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2XDOWtOP2p7",
        "colab_type": "code",
        "outputId": "5ba31b93-776d-4b0a-87ca-a773a09b26d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "modelFilt.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "lowAccuracyModelConv1_1 (Con (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "lowAccuracyModelBN1 (BatchNo (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "lowAccuracyModelConv2_1 (Con (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxnMxP6cTGLO",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    }
  ]
}