ForRetrainCrossTransferVgg11_ProposalHighAccuracyDataVer01_60Epoch.ipynb

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  "metadata": {
    "colab": {
      "name": "ForRetrainCrossTransferVgg11 ProposalHighAccuracyDataVer01_60Epoch.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preritt/KProj/blob/master/ForRetrainCrossTransferVgg11_ProposalHighAccuracyDataVer01_60Epoch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgwWyMb6iCkO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import gzip\n",
        "%matplotlib inline\n",
        "from keras.models import Model\n",
        "from keras.optimizers import RMSprop\n",
        "from keras.layers import Input,Dense,Flatten,Dropout,merge,Reshape,Conv2D,MaxPooling2D,UpSampling2D,Conv2DTranspose\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.models import Model,Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import Adadelta, RMSprop,SGD,Adam\n",
        "from keras import regularizers\n",
        "from keras import backend as K\n",
        "from keras.utils import to_categorical\n",
        "from keras.datasets import cifar10\n",
        "from sklearn import preprocessing\n",
        "from keras import losses"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mqm8mJJu0AU4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txdR9fEabt2P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from keras.models import model_from_json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYdWJtFsiF1a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.datasets import cifar10\n",
        "from keras import regularizers\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJ9XYVEOAprv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras.backend.clear_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9m3ehGh2iIBA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "5f76c68f-d274-4d6d-d1ac-40eba6d26307"
      },
      "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": 34,
      "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": "code",
      "metadata": {
        "id": "hVAjMdeSrloa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e406ad46-85f6-4e70-c0c3-57d500539805"
      },
      "source": [
        "x_test.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngfK-2Qewhtq",
        "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": "code",
      "metadata": {
        "id": "3ZJV35fkrj3O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "         "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ekn-CkLikYJ",
        "colab_type": "text"
      },
      "source": [
        "### Get only three classes of data and train the model on it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPYv1L-uigzL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "5998a988-5374-4034-95c9-68d62f340ec1"
      },
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = getThreeClassesTrainTest([ 1,8,9 ],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[    4     5    32 ... 49993 49998 49999]\n",
            "[    8    62    69 ... 49968 49976 49985]\n",
            "[    1     2    14 ... 49963 49971 49997]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLS-ReIgjevf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save('train_three_data_HiAcc.npy',train_three_data)\n",
        "np.save('train_three_label_HiAcc.npy',train_three_label)\n",
        "np.save('test_three_data_HiAcc.npy',test_three_data)\n",
        "np.save('test_three_label_HiAcc.npy',test_three_label)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9luxTYeM3aZO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "a93e5ddb-b842-4980-eddb-209146b1d305"
      },
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5000.,    0.,    0.,    0.,    0.,    0.,    0.,    0., 5000.,\n",
              "        5000.]),\n",
              " array([1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8, 6.6, 7.4, 8.2, 9. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VwTckrAR+AXwT+KuqGp+N473srgCq6tvAnlH3sb+qerKq7m7LPwW2Mw8+BV0Dz7XVw9vP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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dni1oqIcyPai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "a429b1af-0e96-4c7f-c11e-aa11280fcf99"
      },
      "source": [
        "# test_three_label.shape\n",
        "plt.hist(test_three_label)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1000.,    0.,    0.,    0.,    0.,    0.,    0.,    0., 1000.,\n",
              "        1000.]),\n",
              " array([1. , 1.8, 2.6, 3.4, 4.2, 5. , 5.8, 6.6, 7.4, 8.2, 9. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8/vXYIPl/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fuM8CeuRsDzW",
        "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": "EshrdJ78fp7U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbK7iwsZx8QW",
        "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYJt52bU0ooK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1379c8f1-3818-435a-9c43-1d2db4aeccfd"
      },
      "source": [
        "train_three_label_cat.shape"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3Ydm3cykUFn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_three_label_one_hot = to_categorical(train_three_label_cat)\n",
        "test_three_label_one_hot = to_categorical(test_three_label_cat)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_O4_VLoJ6vSF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9fa0b5ae-880e-4822-b000-0fbddccd2997"
      },
      "source": [
        "test_three_label_one_hot.shape"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UIrWUPz_lGvF",
        "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": "H6AkQaz-kRh0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "duFF_pGbj2i1",
        "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": "Pt1coy0mkFrj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.001\n",
        "    if epoch > 20:\n",
        "        lrate = 0.001/3\n",
        "    if epoch > 40:\n",
        "        lrate = 0.001/9\n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IbtMUdhiscG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# def cifar10Classifier(input_img):\n",
        "  \n",
        "#   conv1 = (Conv2D(32, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:], activation='elu'))(input_img)\n",
        "#   conv1 = BatchNormalization()(conv1)\n",
        "#   conv1 = (Conv2D(32, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv1)\n",
        "#   conv1 = BatchNormalization()(conv1)\n",
        "#   pool1 = MaxPooling2D(pool_size=(2, 2))(conv1) #14 x 14 x 32\n",
        "#   drop1= Dropout(.2)(pool1)\n",
        "\n",
        "#   conv2 = (Conv2D(64, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop1)\n",
        "#   conv2 = BatchNormalization()(conv2)\n",
        "#   conv2 = (Conv2D(64, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv2)\n",
        "#   conv2 = BatchNormalization()(conv2)\n",
        "#   pool2 = MaxPooling2D(pool_size=(2, 2))(conv2) #14 x 14 x 32\n",
        "#   drop2= Dropout(.3)(pool2)\n",
        "\n",
        "#   conv3 = (Conv2D(128, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), activation='elu'))(drop2)\n",
        "#   conv3 = BatchNormalization()(conv3)\n",
        "#   conv3 = (Conv2D(128, (3,3), padding='same', activation= 'elu',kernel_regularizer=regularizers.l2(weight_decay)))(conv3)\n",
        "#   conv3 = BatchNormalization()(conv3)\n",
        "#   pool3 = MaxPooling2D(pool_size=(2, 2))(conv3) #14 x 14 x 32\n",
        "#   drop3= Dropout(.4)(pool3)\n",
        "\n",
        "#   flat = Flatten()(drop3)\n",
        "#   out = Dense(num_classes, activation='softmax')(flat)\n",
        "  \n",
        "#   return out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxJhVRCha0m0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5d3b8605-1d8c-445e-d8b2-60d04632a6ae"
      },
      "source": [
        "weight_decay"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAZd7W_9bSi7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Activation"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCYvn_c9ahuB",
        "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": "code",
      "metadata": {
        "id": "-VPIvSX1jytM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1074
        },
        "outputId": "8ce849c7-a8d3-4406-a6ee-fecea3b5c6c1"
      },
      "source": [
        "model = Model(input_img, (cifar10Classifier(input_img)))\n",
        "#data augmentation\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "datagen.fit(train_three_data)\n",
        " \n",
        "#training\n",
        "batch_size = 64\n",
        " \n",
        "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
        "# opt_rms = keras.optimizers.Adam(lr=0.001,decay=1e-6,beta_1=0.5)\n",
        "# model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "model.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n",
        " "
      ],
      "execution_count": 54,
      "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": "9Wi6iL1A7DxR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# model.get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jabvkmcr1KR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mcp_save = ModelCheckpoint('weights.hdf5', save_best_only=True, monitor='val_acc', mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6x5OsaN9CxB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAxjpgNk9KF3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# for l1,l2 in zip(model.layers[0:],loaded_model_high.layers[0:]):\n",
        "#     l1.set_weights(l2.get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0x-iDMcukCCT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2097
        },
        "outputId": "c6ce7805-b7d7-4a33-96b4-65ebd785ca21"
      },
      "source": [
        "model.fit_generator(datagen.flow(train_X, train_ground, batch_size=batch_size),\\\n",
        "                    steps_per_epoch=train_X.shape[0] // batch_size,epochs=60,\\\n",
        "                    verbose=1,validation_data=(valid_X,valid_ground),\n",
        "                    shuffle = True,callbacks=[mcp_save,LearningRateScheduler(lr_schedule)])\n",
        "#save to disk\n",
        "model_json = model.to_json()\n",
        "with open('model.json', 'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights('model.h5') \n",
        " \n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/60\n",
            "187/187 [==============================] - 12s 63ms/step - loss: 10.7274 - acc: 0.3380 - val_loss: 10.8845 - val_acc: 0.3260\n",
            "Epoch 2/60\n",
            "187/187 [==============================] - 9s 47ms/step - loss: 6.0339 - acc: 0.3982 - val_loss: 2.9355 - val_acc: 0.3413\n",
            "Epoch 3/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.9580 - acc: 0.5426 - val_loss: 1.7240 - val_acc: 0.5207\n",
            "Epoch 4/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.8596 - acc: 0.6328 - val_loss: 0.8625 - val_acc: 0.6680\n",
            "Epoch 5/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.6905 - acc: 0.7433 - val_loss: 0.6977 - val_acc: 0.7487\n",
            "Epoch 6/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.5975 - acc: 0.8020 - val_loss: 0.8307 - val_acc: 0.7410\n",
            "Epoch 7/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.5497 - acc: 0.8234 - val_loss: 0.5961 - val_acc: 0.8050\n",
            "Epoch 8/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.5133 - acc: 0.8447 - val_loss: 0.5289 - val_acc: 0.8283\n",
            "Epoch 9/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.4845 - acc: 0.8588 - val_loss: 0.4223 - val_acc: 0.8763\n",
            "Epoch 10/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.4531 - acc: 0.8673 - val_loss: 0.5495 - val_acc: 0.8170\n",
            "Epoch 11/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.4348 - acc: 0.8759 - val_loss: 0.4290 - val_acc: 0.8920\n",
            "Epoch 12/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.4194 - acc: 0.8831 - val_loss: 1.0971 - val_acc: 0.7463\n",
            "Epoch 13/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.4065 - acc: 0.8874 - val_loss: 0.4963 - val_acc: 0.8593\n",
            "Epoch 14/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.3750 - acc: 0.9018 - val_loss: 0.4866 - val_acc: 0.8860\n",
            "Epoch 15/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.3760 - acc: 0.9037 - val_loss: 1.3062 - val_acc: 0.6937\n",
            "Epoch 16/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.3591 - acc: 0.9048 - val_loss: 0.3941 - val_acc: 0.9130\n",
            "Epoch 17/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.3483 - acc: 0.9106 - val_loss: 0.4032 - val_acc: 0.8930\n",
            "Epoch 18/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.3445 - acc: 0.9132 - val_loss: 0.4071 - val_acc: 0.8987\n",
            "Epoch 19/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.3398 - acc: 0.9126 - val_loss: 0.3955 - val_acc: 0.9030\n",
            "Epoch 20/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.3273 - acc: 0.9179 - val_loss: 0.8802 - val_acc: 0.8167\n",
            "Epoch 21/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.3222 - acc: 0.9235 - val_loss: 0.7297 - val_acc: 0.8067\n",
            "Epoch 22/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2427 - acc: 0.9489 - val_loss: 0.2496 - val_acc: 0.9433\n",
            "Epoch 23/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2254 - acc: 0.9536 - val_loss: 0.3167 - val_acc: 0.9190\n",
            "Epoch 24/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2135 - acc: 0.9546 - val_loss: 0.2356 - val_acc: 0.9403\n",
            "Epoch 25/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2037 - acc: 0.9574 - val_loss: 0.2467 - val_acc: 0.9443\n",
            "Epoch 26/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.2031 - acc: 0.9585 - val_loss: 0.2756 - val_acc: 0.9323\n",
            "Epoch 27/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.2002 - acc: 0.9589 - val_loss: 0.2680 - val_acc: 0.9317\n",
            "Epoch 28/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1906 - acc: 0.9613 - val_loss: 0.2627 - val_acc: 0.9417\n",
            "Epoch 29/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1901 - acc: 0.9614 - val_loss: 0.3712 - val_acc: 0.9127\n",
            "Epoch 30/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1801 - acc: 0.9647 - val_loss: 0.2831 - val_acc: 0.9473\n",
            "Epoch 31/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1835 - acc: 0.9649 - val_loss: 0.3711 - val_acc: 0.9387\n",
            "Epoch 32/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1804 - acc: 0.9640 - val_loss: 0.2969 - val_acc: 0.9393\n",
            "Epoch 33/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1781 - acc: 0.9688 - val_loss: 0.3055 - val_acc: 0.9330\n",
            "Epoch 34/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1709 - acc: 0.9679 - val_loss: 0.3001 - val_acc: 0.9427\n",
            "Epoch 35/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1750 - acc: 0.9673 - val_loss: 0.2520 - val_acc: 0.9497\n",
            "Epoch 36/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1641 - acc: 0.9708 - val_loss: 0.2716 - val_acc: 0.9423\n",
            "Epoch 37/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1661 - acc: 0.9693 - val_loss: 0.2760 - val_acc: 0.9457\n",
            "Epoch 38/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1632 - acc: 0.9703 - val_loss: 0.2652 - val_acc: 0.9420\n",
            "Epoch 39/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1600 - acc: 0.9711 - val_loss: 0.4255 - val_acc: 0.9217\n",
            "Epoch 40/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1583 - acc: 0.9712 - val_loss: 0.3080 - val_acc: 0.9417\n",
            "Epoch 41/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1575 - acc: 0.9723 - val_loss: 0.3478 - val_acc: 0.9227\n",
            "Epoch 42/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1343 - acc: 0.9780 - val_loss: 0.2744 - val_acc: 0.9470\n",
            "Epoch 43/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1321 - acc: 0.9816 - val_loss: 0.2965 - val_acc: 0.9503\n",
            "Epoch 44/60\n",
            "187/187 [==============================] - 9s 50ms/step - loss: 0.1235 - acc: 0.9839 - val_loss: 0.3078 - val_acc: 0.9533\n",
            "Epoch 45/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1216 - acc: 0.9846 - val_loss: 0.3117 - val_acc: 0.9523\n",
            "Epoch 46/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1204 - acc: 0.9838 - val_loss: 0.2973 - val_acc: 0.9540\n",
            "Epoch 47/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1184 - acc: 0.9849 - val_loss: 0.3139 - val_acc: 0.9547\n",
            "Epoch 48/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1180 - acc: 0.9865 - val_loss: 0.2860 - val_acc: 0.9557\n",
            "Epoch 49/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1082 - acc: 0.9875 - val_loss: 0.2774 - val_acc: 0.9517\n",
            "Epoch 50/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1092 - acc: 0.9880 - val_loss: 0.3357 - val_acc: 0.9527\n",
            "Epoch 51/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1169 - acc: 0.9848 - val_loss: 0.2887 - val_acc: 0.9477\n",
            "Epoch 52/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1035 - acc: 0.9879 - val_loss: 0.3103 - val_acc: 0.9537\n",
            "Epoch 53/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1068 - acc: 0.9875 - val_loss: 0.3132 - val_acc: 0.9537\n",
            "Epoch 54/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1060 - acc: 0.9886 - val_loss: 0.3562 - val_acc: 0.9473\n",
            "Epoch 55/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1053 - acc: 0.9870 - val_loss: 0.3252 - val_acc: 0.9467\n",
            "Epoch 56/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1096 - acc: 0.9860 - val_loss: 0.2924 - val_acc: 0.9537\n",
            "Epoch 57/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.0918 - acc: 0.9902 - val_loss: 0.3061 - val_acc: 0.9520\n",
            "Epoch 58/60\n",
            "187/187 [==============================] - 9s 49ms/step - loss: 0.1018 - acc: 0.9890 - val_loss: 0.2941 - val_acc: 0.9553\n",
            "Epoch 59/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1008 - acc: 0.9888 - val_loss: 0.3397 - val_acc: 0.9510\n",
            "Epoch 60/60\n",
            "187/187 [==============================] - 9s 48ms/step - loss: 0.1022 - acc: 0.9880 - val_loss: 0.3368 - val_acc: 0.9503\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYZeckSZ1FA4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.load_weights('weights.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlQJMg1YnI17",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('vgg16HighAccuracyModelDataForTransferHiEpoch.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0zPvx9oEN5_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# temp_model = keras.models.load_model('lowAccuracyModelData.h5')\n",
        "# temp_model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KltF5yv2AZES",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "9f91114b-6886-4306-8fd0-b7234b018e7e"
      },
      "source": [
        "accuracy = model.history.history['acc']\n",
        "val_accuracy = model.history.history['val_acc']\n",
        "loss = model.history.history['loss']\n",
        "val_loss = 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": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CPvkEYk/sc8I9blxBwfblootcRMusWa4j0tcl46WkgUwbNxbtbwc3cXWjRkWL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\nE3fDMIww5P8Bx5bnEQYJs6gAAAAASURBVDWvBh4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X99+HZcvC9Sef7NmyiEiPSrtAbzxR9FlnhWMZo9Ltsn5954YsePTRcIjP5Mnw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\ndBGRNKFAFxFJEwp0EZE08f8BTGLPw1+jiO0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJHowfrN1HQg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "82874ba4-a0ff-4129-f0d1-d6ef688732c0"
      },
      "source": [
        "model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.28599456000328066, 0.9556666666666667]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4M2DfGjmT3I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "ed931b49-0757-4ee8-e127-30b8b94a6012"
      },
      "source": [
        "#testing\n",
        "test_eval = 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])"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.33999149894714353\n",
            "Test accuracy: 0.9510000001589457\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1_boK_fprtB",
        "colab_type": "code",
        "outputId": "36ef4ae2-9714-4c0f-8e16-5e22559bd496",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_three_data.shape"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZuH6igT7nbza",
        "colab_type": "code",
        "outputId": "582f86e2-f5e3-45d6-f7a5-9579abb8be00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "# show_imgs(test_three_data[:,1])\n",
        "show_imgs(test_three_data[:16])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: DeprecationWarning: `toimage` is deprecated!\n",
            "`toimage` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use Pillow's ``Image.fromarray`` directly instead.\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MBiKLLRMRd2Qdj6yvcPO7rwMwWxllYkLkPrK2zKSSdd8bG2Gr47I6Bimpo290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\nqqFwsVMT41zsCHd7fWuVSGf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8fQ+1TblmdEGcxMR9eX6jwSA/ZmxsgjB00z7iqDSGXBfh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\nlakcsrwvQ6uU+rxS6g2l1NtKqV89qA81FZGpfg9Ppro9PJnq9l1EJiv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kEyW1UnaGFGBl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SzEZDvrDDvod6BEiRIlHnns74QFDFtK3rbGGHTONM92DWGqs6KizcEvahG1Ap3ltZhpTqRESkpi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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cE1UG2RavZW3",
        "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": "2_q12H9HU7n-",
        "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": "code",
      "metadata": {
        "id": "mPTmOl-QERZN",
        "colab_type": "code",
        "outputId": "68b9886e-61dd-4d52-9f06-c87f30917c64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# layer_name\n",
        "valid_X.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRQ1YTnsd6zM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_three_data, train_three_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pe0g2T0IA7sG",
        "colab_type": "code",
        "outputId": "c41af9f2-7ee8-4601-cad9-6c841dc4798d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "layer_name = 'conv2d_6'\n",
        "get_cnn1_output_function = K.function([model.layers[0].input],\n",
        "                                  [model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "cnn1_output = get_cnn1_output_function([train_three_data])[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-6173d5b8ab5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlayer_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'conv2d_6'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m get_cnn1_output_function = K.function([model.layers[0].input],\n\u001b[0;32m----> 3\u001b[0;31m                                   [model.get_layer(layer_name).output])   #get_layer test_three_label\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mcnn1_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_cnn1_output_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_three_data\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/network.py\u001b[0m in \u001b[0;36mget_layer\u001b[0;34m(self, name, index)\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 358\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No such layer: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\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    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: No such layer: conv2d_6"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Yy-y5xIWdz6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26Q6Hfa8rTd6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.utils.vis_utils import plot_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EF3q_KOgYTxb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_model(model, to_file=\"cifar10Architecture.png\", show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0Hfr4ZwB8dX",
        "colab_type": "code",
        "outputId": "186689eb-681e-4ee6-e6d2-35734439e701",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.ndarray.tolist(np.unique(test_three_label))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pM8xfShbdHJV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# valid_ground_cat = np.argmax(train_ground, axis=1)\n",
        "# # valid_ground_cat\n",
        "# valid_ground.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZ1IST7pBZ92",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "di_scores = getDIScoreOfLayerCNNLayer(cnn1_output, train_three_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZnzBi-7BcfM",
        "colab_type": "code",
        "outputId": "d47065e9-6842-403f-903c-537c8fda1bce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "y_pos  = np.arange(len(di_scores))\n",
        "plt.bar(y_pos, di_scores, align='center', alpha=0.5)\n",
        "plt.xlabel('FilterNumber')\n",
        "plt.ylabel('DI Score')\n",
        "\n",
        "print('Mean of DI SCore: ',np.mean(di_scores))\n",
        "print('STD of DI Score: ', np.std(di_scores))\n",
        "print('MAX of DI Score: ', np.max(di_scores))\n",
        "plt.title('DI score - Data - D_L, Network -N_L, Conv_6')\n",
        "plt.ylim([0,1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.46968247829146104\n",
            "STD of DI Score:  0.17012919206958768\n",
            "MAX of DI Score:  0.8879906467627727\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jvBPUHZoKBUnrSXrVEOd9CzBF0mRJ6wCHA3NqpvkRqZWApC1I3UlLhvh/zMzGnHaF6KCh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\nrOBQMDOzwv8HFvkwSAKN7XoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFs0BsyhzMXk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Xkj_8m7OpfxD"
      },
      "source": [
        "## DI on different data\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9c789423-23ba-478b-eeeb-64a19bd8cefe",
        "id": "JcsAbLG_pfwz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "\n",
        "train_three_data_low,train_three_label_low,test_three_data_low,test_three_label_low = getThreeClassesTrainTest([1,8,9],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[    4     5    32 ... 49993 49998 49999]\n",
            "[    8    62    69 ... 49968 49976 49985]\n",
            "[    1     2    14 ... 49963 49971 49997]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vA_9sjRGpfwn",
        "colab": {}
      },
      "source": [
        "\n",
        "# split train data and to create validation data\n",
        "train_X_low,valid_X_low,train_ground_low,valid_ground_low = train_test_split(train_three_data_low,\n",
        "                                                             train_three_label_low,\n",
        "                                                             test_size=0.2,\n",
        "                                                             random_state=13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "6939f482-af89-4f16-8050-cd1bd5d1116a",
        "id": "-zNgfVTPpfwa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "layer_name = 'conv2d_6'\n",
        "get_cnn1_output_function_low = K.function([model.layers[0].input],\n",
        "                                  [model.get_layer(layer_name).output])   #get_layer test_three_label\n",
        "cnn1_output_low = get_cnn1_output_function_low([train_three_data_low])[0]\n",
        "cnn1_output_low.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 8, 8, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "C0RAKBX5pfwR",
        "colab": {}
      },
      "source": [
        "di_scores_low = getDIScoreOfLayerCNNLayer(cnn1_output_low, train_three_label_low)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "dad4626d-35b5-4e26-b7ac-f7c83d2e5ad1",
        "id": "lYpxC9KMpfwD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "y_pos  = np.arange(len(di_scores_low))\n",
        "plt.bar(y_pos, di_scores_low, 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_low))\n",
        "print('STD of DI Score: ', np.std(di_scores_low))\n",
        "print('MAX of DI Score: ', np.max(di_scores_low))\n",
        "plt.title('DI score - Data - D_H, Network -N_L, Conv_6')\n",
        "plt.ylim([0,1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of DI SCore:  0.25325489539602447\n",
            "STD of DI Score:  0.07981976639878438\n",
            "MAX of DI Score:  0.48132421143127047\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SNylI2g24E7giD39IUt+D+xIiYjFwEHAlcB9wfkTMknSMpEm52JXAAkn3AtcCh0XEgqEt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\nZjg+a2V/5gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "964f1ccc-2699-487a-9298-1fb1a316a3f9",
        "id": "UH9t29tspfv1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 3)                 6147      \n",
            "=================================================================\n",
            "Total params: 294,947\n",
            "Trainable params: 294,051\n",
            "Non-trainable params: 896\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lK3KHL2Bpfvt",
        "colab": {}
      },
      "source": [
        "# # not working\n",
        "# mat_save = {'diScore': di_scores, 'conv2d_1': cnn1_output, 'trueLabel':train_three_label}\n",
        "# savemat('LowAccuracyConv2d_1MatrixMethod', mat_save) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Fvw5s5K8pfvk",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PMqp4Ao3pfvg"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jkozbZpypfvX",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "90f23b1a-a2ef-43cc-f7a8-e6d22f0c1645",
        "id": "x-H8BlZUpfuw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "sum(di_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.049834714257239"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "dc5ee49b-0567-4d1b-a541-5a8f8731c467",
        "id": "JvJ41KIfpfuc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "max(di_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6375973839423631"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uWwVdXn0pft-",
        "colab": {}
      },
      "source": [
        "from scipy.io import savemat\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7GwdcjmBpfs0",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}