(Transfer1)cifar103ClassAccuracyHighDIScoreCrossCrossVer06.ipynb

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  "metadata": {
    "colab": {
      "name": "(Transfer)cifar103ClassAccuracyHighDIScoreCrossCrossVer06.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/CS231n-/blob/master/(Transfer1)cifar103ClassAccuracyHighDIScoreCrossCrossVer06.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": "fcc0d2e7-a584-4fba-d410-7b199071698a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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": "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": "32087c57-fd29-4a9f-b7dc-5b56e44ebdff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "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 [==============================] - 24s 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": "91ce4703-5a1e-4977-c01f-65dfffddfd62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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": "92c9021f-61f3-4b04-81d5-2e497cc6b270",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "train_three_data,train_three_label,test_three_data,test_three_label = getThreeClassesTrainTest([0,3,5 ],x_train,y_train,x_test,y_test)"
      ],
      "execution_count": 7,
      "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": "9luxTYeM3aZO",
        "colab_type": "code",
        "outputId": "4f39ef72-c16e-441b-f412-c200444819e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "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., 5000.,    0.,    0.,\n",
              "        5000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PAb8f90RGrIDvJjnYvh1hWazEAKgjSV4LfB34aFX9etzzWW5V9UJVXcTgU/Sbk6zYU35J\n3gMcraqD457LGLyzqi5m8M3Ju9pp3iW3EgOw4NdNaGVo58G/Dny5qr4x7vmMUlU9A9wNbB33XJbR\npcB72/nwfcBlSf59vFMajao60m6PAt9kcGp7ya3EAPh1Ex1ob4jeAhyqqs+Mez6jkGQiyZq2/BoG\nFzr8dLyzWj5VdX1VbaiqSQZ/j++qqr8b87SWXZJz24UNJDkX2AIsyxV+Ky4AVXUSOPV1E4eA/Sv9\n6yaSfAX4T+AtSQ4n2THuOY3ApcCHGPxW+GD7uWrck1pm64C7kzzE4Bed6arq5tLIjqwFfpDkR8B9\nwLer6jvL8UQr7jJQSdJwVtwrAEnScAyAJHXKAEhSpwyAJHXKAEhSpwyAJHXKAEhSpwyAJHXqfwHd\n/gESZomJ0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Dni1oqIcyPai",
        "colab_type": "code",
        "outputId": "68ecf245-c0a6-49fe-e6d8-58a4fda65bf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "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., 1000.,    0.,    0.,\n",
              "        1000.]),\n",
              " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qqpuXRnZkDfBwkm8BXwe+VFVfXo4TrbiXgUqSTs2KewYgSTo1BkCSOmUAJKlTBkCSOmUA\nJKlTBkCSOmUAJKlTBkCSOvW/lK9vO4tP9cMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": "4c0c21ed-8df1-488f-8005-90a9d5609f11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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": "ba21fe80-231a-4fe4-b28d-d361a5a4c7ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "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": "St7OPPbkpBRH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        },
        "outputId": "2769e31c-8626-427f-e2ab-82c62317ece4"
      },
      "cell_type": "code",
      "source": [
        "json_file_high = open('modelHigh.json', 'r')\n",
        "loaded_model_json_high = json_file_high.read()\n",
        "json_file_high.close()\n",
        "loaded_model_high = model_from_json(loaded_model_json_high)"
      ],
      "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qcVCXpeipRb_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model_high.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_5TlD_pwo6PU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model_high.get_layer('conv2d_1').get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Fmk0PSxIn55H",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Model(input_img, (cifar10Classifier(input_img)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "94hzQr3eo2t1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NHnJ3N1xn7AI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.get_layer('conv2d_1').set_weights(loaded_model_high.get_layer('conv2d_1').get_weights())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y1huVOnSqclT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.get_layer('conv2d_1').trainable = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M9d4nO7Fn_nZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# model.get_layer('conv2d_1').get_weights()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-VPIvSX1jytM",
        "colab_type": "code",
        "outputId": "753e5147-1b2b-4efa-fb4d-cdc36167cb11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 908
        }
      },
      "cell_type": "code",
      "source": [
        "\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": 27,
      "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": "2jabvkmcr1KR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mcp_save = ModelCheckpoint('weights.{epoch:02d}-{val_loss:.2f}.hdf5', save_best_only=False, 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": "107ef098-5186-44e4-edc2-352a5e5e0ea1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        }
      },
      "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('model.json', 'w') as json_file:\n",
        "#     json_file.write(model_json)\n",
        "# model.save_weights('model.h5') \n",
        " \n"
      ],
      "execution_count": 29,
      "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 [==============================] - 15s 80ms/step - loss: 1.1175 - acc: 0.6019 - val_loss: 0.7931 - val_acc: 0.6657\n",
            "Epoch 2/20\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.7826 - acc: 0.6745 - val_loss: 0.7238 - val_acc: 0.6997\n",
            "Epoch 3/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.6781 - acc: 0.7092 - val_loss: 0.6276 - val_acc: 0.7397\n",
            "Epoch 4/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.6300 - acc: 0.7322 - val_loss: 0.5697 - val_acc: 0.7650\n",
            "Epoch 5/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.5901 - acc: 0.7574 - val_loss: 0.5485 - val_acc: 0.7813\n",
            "Epoch 6/20\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.5530 - acc: 0.7741 - val_loss: 0.6835 - val_acc: 0.7373\n",
            "Epoch 7/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.5469 - acc: 0.7821 - val_loss: 0.5027 - val_acc: 0.7970\n",
            "Epoch 8/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.5227 - acc: 0.7920 - val_loss: 0.4972 - val_acc: 0.8033\n",
            "Epoch 9/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.5039 - acc: 0.8064 - val_loss: 0.5704 - val_acc: 0.7700\n",
            "Epoch 10/20\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.4908 - acc: 0.8108 - val_loss: 0.5113 - val_acc: 0.7990\n",
            "Epoch 11/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4854 - acc: 0.8160 - val_loss: 0.4485 - val_acc: 0.8410\n",
            "Epoch 12/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4680 - acc: 0.8239 - val_loss: 0.4740 - val_acc: 0.8190\n",
            "Epoch 13/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.4696 - acc: 0.8266 - val_loss: 0.4727 - val_acc: 0.8303\n",
            "Epoch 14/20\n",
            "187/187 [==============================] - 10s 54ms/step - loss: 0.4527 - acc: 0.8292 - val_loss: 0.4890 - val_acc: 0.8140\n",
            "Epoch 15/20\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.4454 - acc: 0.8354 - val_loss: 0.4563 - val_acc: 0.8367\n",
            "Epoch 16/20\n",
            "187/187 [==============================] - 11s 56ms/step - loss: 0.4460 - acc: 0.8359 - val_loss: 0.4591 - val_acc: 0.8307\n",
            "Epoch 17/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4417 - acc: 0.8363 - val_loss: 0.5108 - val_acc: 0.8073\n",
            "Epoch 18/20\n",
            "187/187 [==============================] - 10s 55ms/step - loss: 0.4234 - acc: 0.8478 - val_loss: 0.4129 - val_acc: 0.8563\n",
            "Epoch 19/20\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4206 - acc: 0.8477 - val_loss: 0.7372 - val_acc: 0.7513\n",
            "Epoch 20/20\n",
            "187/187 [==============================] - 10s 56ms/step - loss: 0.4267 - acc: 0.8484 - val_loss: 0.5451 - val_acc: 0.8040\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe52fe50550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "3KWBGDFQsmm9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# json_file = open('model.json', 'r')\n",
        "# loaded_model_json = json_file.read()\n",
        "# json_file.close()\n",
        "# loaded_model = model_from_json(loaded_model_json)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BXveyoT1ttYC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# loaded_model.compile(loss=losses.categorical_crossentropy, optimizer=opt_rms, metrics=['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kUy8WCS4xsYU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.load_weights('weights.18-0.41.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ta_XH9UirtQZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "49fdbe0a-c039-4e32-ede5-0a9a37335324"
      },
      "cell_type": "code",
      "source": [
        "model.evaluate(valid_X, valid_ground, verbose=0)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.412868101755778, 0.8563333333333333]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "w4M2DfGjmT3I",
        "colab_type": "code",
        "outputId": "cecee0a2-4a41-4261-d65d-c7bb9c03cd94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "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": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.3849576512972514\n",
            "Test accuracy: 0.872666666507721\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M1_boK_fprtB",
        "colab_type": "code",
        "outputId": "4af1b51f-bce7-45e7-e34f-7612223201e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_data.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "ZuH6igT7nbza",
        "colab_type": "code",
        "outputId": "6a8187c9-6456-493e-fda6-2661ada0d415",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        }
      },
      "cell_type": "code",
      "source": [
        "# show_imgs(test_three_data[:,1])\n",
        "show_imgs(test_three_data[:16])"
      ],
      "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": {
            "image/png": 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VDnY3ANjZ62LTBbQXFsCuMxVE5La5KB2PUKXYFF8rtO0klaoEmwMqcZ/NVFWR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\n9vFPU+WxIw0N6Xy0t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v4+GAxYWPCpmYVeL+gq43Kqq3gwGJE2qvRCjhHtAmsdRV5tEgrk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\nAKJAAAAAAElFTkSuQmCC\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": "911535f6-fa61-42bc-fc73-31e1eb9f63cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# layer_name\n",
        "valid_X.shape"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "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": "050b5f35-05b0-44aa-d9da-6f3cbccf66eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "EF3q_KOgYTxb",
        "colab_type": "code",
        "outputId": "0ab232cf-d6f6-41bf-a60e-b98166e49c32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "cnn1_output.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "B0Hfr4ZwB8dX",
        "colab_type": "code",
        "outputId": "474ae243-5949-48d5-ef56-fb3afa07569b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "np.ndarray.tolist(np.unique(test_three_label))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "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": "fea56e1c-39d3-4f0b-b9f3-68fc23283d9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "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": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'DI Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJKllKEiSWoaCJKn1/wHs8wtLm8aTFAAAAABJRU5ErkJggg==\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": "e5802ba9-05b8-4fe3-e37b-3b52e61c43ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "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": 0,
      "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": "a650a096-3324-475e-de3a-125717e557c6",
        "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": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15000, 32, 32, 32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "rk6twlNTc2cW",
        "colab_type": "code",
        "outputId": "69712138-f97d-4f16-8b3c-1ab4d2be9525",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "test_three_label_low"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 5, 5, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "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": "23e64f92-86f7-4ec4-df33-b9f74d45613e",
        "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')\n",
        "plt.ylim([0,.65])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 0.65)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3bQv1OZbkXOBvAvfPf6Kq2gPsOdMFJpmrqtkz/bwrxfony/onZ5prh9Vdf5/TRweALUk2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\nWxZxRz2rUwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "emT7m8pI6lP8",
        "colab_type": "code",
        "outputId": "b635e050-c94e-4ab6-bcf6-79281ed884d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        }
      },
      "cell_type": "code",
      "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"
        }
      ]
    },
    {
      "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": "4877db7a-cdf0-41ae-a1e5-5204e7d3bbc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "sum(di_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.38424550143677"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "w27VKTFQUI6d",
        "colab_type": "code",
        "outputId": "608f2c47-ea99-4e11-936e-f0e224d175fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "max(di_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6679229295993279"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "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": []
    }
  ]
}