(Conv2d1_Layer)cifar103ClassAccuracyHighDIScoreCrossCrossVer05.ipynb

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  "metadata": {
    "colab": {
      "name": "(Conv2d1_Layer)cifar103ClassAccuracyHighDIScoreCrossCrossVer05.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/(Conv2d1_Layer)cifar103ClassAccuracyHighDIScoreCrossCrossVer05.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "FgwWyMb6iCkO",
        "colab_type": "code",
        "outputId": "0502367e-9aec-4827-cdf8-20aa3f7c4ad2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Mqm8mJJu0AU4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "txdR9fEabt2P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from keras.models import model_from_json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PYdWJtFsiF1a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "9m3ehGh2iIBA",
        "colab_type": "code",
        "outputId": "0f3c40b6-71ef-4162-cf09-d97febf8f79f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "cell_type": "code",
      "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": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 4s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "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": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "hVAjMdeSrloa",
        "colab_type": "code",
        "outputId": "e0a4958f-248d-4c8c-c4a9-e2eec88d070a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "ngfK-2Qewhtq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "3ZJV35fkrj3O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "       "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2ekn-CkLikYJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Get only three classes of data and train the model on it"
      ]
    },
    {
      "metadata": {
        "id": "VPYv1L-uigzL",
        "colab_type": "code",
        "outputId": "f4f3406a-bab2-442c-be6b-d7c21f18e9a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "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": 7,
      "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"
        }
      ]
    },
    {
      "metadata": {
        "id": "9luxTYeM3aZO",
        "colab_type": "code",
        "outputId": "587e6462-0448-426b-aaa4-0c30c0b16e35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "plt.hist(train_three_label)"
      ],
      "execution_count": 8,
      "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": 8
        },
        {
          "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": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Dni1oqIcyPai",
        "colab_type": "code",
        "outputId": "16ff4671-ee23-424b-f5d6-4827bd670dad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "# test_three_label.shape\n",
        "plt.hist(test_three_label)"
      ],
      "execution_count": 9,
      "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": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YfavtJW3acWgTwBnA1VV1OvATYMl83Xqbknon8C9D1wKQ5HhGX0R5CvBa4NgkfzlsVVBV\nO4Arga8xmv65D/j5kRrPAFhEbY79JuD6qrp56HoO1KYM7gLOHboW4GzgnW2+/QvAW5L887AljbRP\nj1TVXuBLjOZrh7YL2DXr7G0Lo0BYKs4D7qmqJ4YupHkr8L2qmqmqnwE3A388cE0AVNWmqvqjqjoH\neAr4ryM1lgGwSNrF1k3Ajqr65ND17JdkMsmytvwq4G3Ad4atCqrqo1W1sqpWM5o6uLOqBv+EluTY\ndhGfNsXydkan7YOqqseBnUle35rWAIPeYHCA97JEpn+ax4CzkhzT/m2uYXRdbnBJfqe9nsxo/v/z\nR2qsJfdtoONKcgPwZuDEJLuAK6pq07BVAaNPtO8D7m/z7QAfa09GD+kkYHO7Q+NlwI1VtWRuuVyC\nlgNfGr1nMAF8vqq+MmxJv/QB4Po23fIIcPHA9QC/DMq3AX81dC37VdXWJFuAe4B9wL0snSeCb0py\nAvAz4JIjeTH/JXcbqCTpxXEKSJI6ZQBIUqcMAEnqlAEgSZ0yACSpUwaAJHXKAJCkThkAktSp/wO6\n8/vXYIPl/gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "fuM8CeuRsDzW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "EshrdJ78fp7U",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mbK7iwsZx8QW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "jYJt52bU0ooK",
        "colab_type": "code",
        "outputId": "26b08196-e2d4-4037-e449-3fe5bde7b5d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_label_cat.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "r3Ydm3cykUFn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "_O4_VLoJ6vSF",
        "colab_type": "code",
        "outputId": "cabf3fef-7c84-4ea3-e525-bf69b33c1f2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_label_one_hot.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "UIrWUPz_lGvF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "H6AkQaz-kRh0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "num_classes = test_three_label_one_hot.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "duFF_pGbj2i1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "Pt1coy0mkFrj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def lr_schedule(epoch):\n",
        "    lrate = 0.001\n",
        "    if epoch > 75:\n",
        "        lrate = 0.0005\n",
        "    if epoch > 100:\n",
        "        lrate = 0.0003\n",
        "    return lrate"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4IbtMUdhiscG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "-VPIvSX1jytM",
        "colab_type": "code",
        "outputId": "74b3b6d3-e13b-4984-901e-a1313e3dd0ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        }
      },
      "cell_type": "code",
      "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",
        "# 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": 20,
      "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/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "_________________________________________________________________\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"
        }
      ]
    },
    {
      "metadata": {
        "id": "2jabvkmcr1KR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mcp_save = ModelCheckpoint('weights.hdf5', save_best_only=True, monitor='val_loss', mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yBK4UwK8lffO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "train_X,valid_X,train_ground,valid_ground"
      ]
    },
    {
      "metadata": {
        "id": "0x-iDMcukCCT",
        "colab_type": "code",
        "outputId": "a0eb8d79-5798-471e-d840-c88ba7ccf158",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        }
      },
      "cell_type": "code",
      "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=20,\\\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('modelHighAcc.json', 'w') as json_file:\n",
        "    json_file.write(model_json)\n",
        "model.save_weights('modelHighAcc.h5') \n",
        "model.save('high_acc_complete_model.h5')\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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",
            "Epoch 1/20\n",
            "187/187 [==============================] - 16s 84ms/step - loss: 1.1731 - acc: 0.6251 - val_loss: 1.0542 - val_acc: 0.6167\n",
            "Epoch 2/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.7915 - acc: 0.7389 - val_loss: 0.9263 - val_acc: 0.7047\n",
            "Epoch 3/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.6515 - acc: 0.7970 - val_loss: 0.5510 - val_acc: 0.8387\n",
            "Epoch 4/20\n",
            "187/187 [==============================] - 11s 57ms/step - loss: 0.6124 - acc: 0.8241 - val_loss: 0.5202 - val_acc: 0.8293\n",
            "Epoch 5/20\n",
            "187/187 [==============================] - 11s 59ms/step - loss: 0.5732 - acc: 0.8409 - val_loss: 0.4492 - val_acc: 0.8747\n",
            "Epoch 6/20\n",
            "187/187 [==============================] - 11s 59ms/step - loss: 0.5418 - acc: 0.8535 - val_loss: 0.4929 - val_acc: 0.8800\n",
            "Epoch 7/20\n",
            "187/187 [==============================] - 11s 59ms/step - loss: 0.4937 - acc: 0.8684 - val_loss: 0.3795 - val_acc: 0.8857\n",
            "Epoch 8/20\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4669 - acc: 0.8740 - val_loss: 0.5995 - val_acc: 0.8483\n",
            "Epoch 9/20\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4632 - acc: 0.8826 - val_loss: 0.4754 - val_acc: 0.8627\n",
            "Epoch 10/20\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4387 - acc: 0.8875 - val_loss: 0.8312 - val_acc: 0.7980\n",
            "Epoch 11/20\n",
            "187/187 [==============================] - 11s 58ms/step - loss: 0.4499 - acc: 0.8883 - val_loss: 0.3425 - val_acc: 0.9010\n",
            "Epoch 12/20\n",
            "187/187 [==============================] - 11s 59ms/step - loss: 0.3684 - acc: 0.8961 - val_loss: 0.3679 - val_acc: 0.8947\n",
            "Epoch 13/20\n",
            "187/187 [==============================] - 11s 60ms/step - loss: 0.3431 - acc: 0.9042 - val_loss: 0.3543 - val_acc: 0.9033\n",
            "Epoch 14/20\n",
            "187/187 [==============================] - 12s 64ms/step - loss: 0.3197 - acc: 0.9095 - val_loss: 0.3179 - val_acc: 0.9127\n",
            "Epoch 15/20\n",
            "187/187 [==============================] - 12s 63ms/step - loss: 0.3104 - acc: 0.9127 - val_loss: 0.2843 - val_acc: 0.9227\n",
            "Epoch 16/20\n",
            "187/187 [==============================] - 12s 63ms/step - loss: 0.3080 - acc: 0.9120 - val_loss: 0.2596 - val_acc: 0.9360\n",
            "Epoch 17/20\n",
            "187/187 [==============================] - 12s 64ms/step - loss: 0.3031 - acc: 0.9172 - val_loss: 0.2968 - val_acc: 0.9210\n",
            "Epoch 18/20\n",
            "187/187 [==============================] - 12s 64ms/step - loss: 0.2897 - acc: 0.9174 - val_loss: 0.5157 - val_acc: 0.8643\n",
            "Epoch 19/20\n",
            "187/187 [==============================] - 12s 64ms/step - loss: 0.2819 - acc: 0.9241 - val_loss: 0.2580 - val_acc: 0.9370\n",
            "Epoch 20/20\n",
            "187/187 [==============================] - 12s 63ms/step - loss: 0.2822 - acc: 0.9248 - val_loss: 0.3302 - val_acc: 0.9033\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "DYZeckSZ1FA4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights('weights.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fJHowfrN1HQg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3c07e8fd-f472-49ad-db28-cbc14b0d6521"
      },
      "cell_type": "code",
      "source": [
        "model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.25797582081953685, 0.937]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "w4M2DfGjmT3I",
        "colab_type": "code",
        "outputId": "0f9eedb2-4c1f-42d2-adf5-fb2ac395feae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "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": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.26665770562489827\n",
            "Test accuracy: 0.9283333334922791\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M1_boK_fprtB",
        "colab_type": "code",
        "outputId": "22dbc937-39fb-4c03-c72a-aaaaa30e3dba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_data.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "ZuH6igT7nbza",
        "colab_type": "code",
        "outputId": "3cd5f1e3-ff95-4ce2-9bde-dc40fde0f94c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "cell_type": "code",
      "source": [
        "# show_imgs(test_three_data[:,1])\n",
        "show_imgs(test_three_data[:16])"
      ],
      "execution_count": 27,
      "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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dq92i3hbrNpqbp+YS3ZVqhcLIz7OUuVlJqmsdEgZyXwIV+VjMTL2BiWT93bhy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0K3SB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\no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hYUBZvLskG98fp3+d//1/8NgCRr88prwgtx8myT5z4pOnnofAXsIP6YUVgZ6LnLqI/L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\npEovDeXc1MUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cE1UG2RavZW3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "2_q12H9HU7n-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "mPTmOl-QERZN",
        "colab_type": "code",
        "outputId": "df611a09-20f2-4570-afb9-a8e6332d17ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# layer_name\n",
        "valid_X.shape"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "bRQ1YTnsd6zM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# train_three_data, train_three_label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pe0g2T0IA7sG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_1'\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": []
    },
    {
      "metadata": {
        "id": "4Yy-y5xIWdz6",
        "colab_type": "code",
        "outputId": "31c9b657-0abd-4cc2-e6a2-2d8890c11db9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "EF3q_KOgYTxb",
        "colab_type": "code",
        "outputId": "458cf9d9-2beb-4beb-cd85-02919f5f2bb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "B0Hfr4ZwB8dX",
        "colab_type": "code",
        "outputId": "707eb1c3-e429-434a-8924-14e859b5edfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "np.ndarray.tolist(np.unique(test_three_label))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 8, 9]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "pM8xfShbdHJV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# valid_ground_cat = np.argmax(train_ground, axis=1)\n",
        "# # valid_ground_cat\n",
        "# valid_ground.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jZ1IST7pBZ92",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "di_scores = getDIScoreOfLayerCNNLayer(cnn1_output, train_three_label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RZnzBi-7BcfM",
        "colab_type": "code",
        "outputId": "e2ffe4af-afa8-4109-8f4d-8e88f5e1771e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "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')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'DI Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sy1CQJLUMBUlSy1CQJLUMBUlSy1CQJLUMBUlSq9NQSLI5yd1JDiW5apb9P5Dkw83+zyZZ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\n/X+5oWvojMfOGAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gFs0BsyhzMXk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d1LLCxe9aHpX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## DI on different data\n",
        "\n",
        "1.   List item\n",
        "2.   List item\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "WeSV3VJA6lm2",
        "colab_type": "code",
        "outputId": "f696e33f-0135-4012-9e39-62320c8015b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train_three_data_low,train_three_label_low,test_three_data_low,test_three_label_low = getThreeClassesTrainTest([0,3,5],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[   29    30    35 ... 49941 49992 49994]\n",
            "[    9    17    21 ... 49979 49982 49983]\n",
            "[   27    40    51 ... 49964 49980 49988]\n",
            "(15000, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Y3R57M0i6liE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "0IlIKFz86le9",
        "colab_type": "code",
        "outputId": "af4bb103-b0c7-491f-d4ed-84b6ad14df84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "layer_name = 'conv2d_1'\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": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "uZIf7RZ86laf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "di_scores_low = getDIScoreOfLayerCNNLayer(cnn1_output_low, test_three_label_low)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "q9pMSCcD6lUm",
        "colab_type": "code",
        "outputId": "3a436b78-c065-412f-b25f-87c187aec030",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "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')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'DI Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "emT7m8pI6lP8",
        "colab_type": "code",
        "outputId": "0598c143-d566-4092-db34-6e2ec112a87f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 44,
      "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"
        }
      ]
    },
    {
      "metadata": {
        "id": "OcsyCU-caFc9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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": []
    },
    {
      "metadata": {
        "id": "n7Xmu2bLaFZa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Lxf2mXCG8nW9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "t3vXkMPaaFWT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1WLuuRgeFImF",
        "colab_type": "code",
        "outputId": "3b17de85-c8bb-4d7d-bbc4-dc80e58e29fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "sum(di_scores)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.558955910452138"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "w27VKTFQUI6d",
        "colab_type": "code",
        "outputId": "a17e55fd-3fdf-4d29-b277-396acfa6216c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "max(di_scores)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6730249143024407"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "metadata": {
        "id": "8VzlOOa1YB0I",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from scipy.io import savemat\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HDtz_epHYCT_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}