Inception_Lymphoma.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from numpy import argmax\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import Sequential\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.layers.core import Activation\n",
    "from keras.layers.core import Flatten\n",
    "from keras.layers.core import Dropout\n",
    "from keras.layers.core import Dense\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.preprocessing.image import array_to_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.preprocessing.image import load_img\n",
    "from imutils import paths\n",
    "from keras.models import Model\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.applications.xception import Xception\n",
    "from keras.layers import Conv2D, AveragePooling2D, GlobalAveragePooling2D\n",
    "import cv2\n",
    "import os\n",
    "import sys\n",
    "import random\n",
    "import glob\n",
    "import keras\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "imagePaths_train=sorted(list(paths.list_images(\"lymphoma\")))\n",
    "random.seed(40)\n",
    "random.shuffle(imagePaths_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "imagePaths_train1=sorted(list(paths.list_images(\"lymphoma\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "374\n"
     ]
    }
   ],
   "source": [
    "data_train=[]\n",
    "labels_train=[]\n",
    "data_test=[]\n",
    "labels_test=[]\n",
    "data_validation=[]\n",
    "labels_validation=[]\n",
    "\n",
    "\n",
    "for imagePath in imagePaths_train:\n",
    "    img=cv2.imread(imagePath)\n",
    "    img=cv2.resize(img,(347,260)) #277,208\n",
    "    data_train.append(img)\n",
    "    \n",
    "    label=imagePath.split(os.path.sep)[-2]\n",
    "    labels_train.append(label)\n",
    "print(len(data_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Images only from 2 classes CLL and MCL are present in data_train1 as misclassifications were found out only in these 2 classes. FLL was predicted with 100% test accuracy. Later on only data_train1 can be used to train on only 2 classes of the 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "235\n"
     ]
    }
   ],
   "source": [
    "data_train1=[]\n",
    "labels_train1=[]\n",
    "for imgpath in imagePaths_train1:\n",
    "    label=imgpath.split(os.path.sep)[-2]\n",
    "    if(label=='CLL' or label=='MCL'):\n",
    "        img=cv2.imread(imgpath)\n",
    "        img=cv2.resize(img,(347,260))\n",
    "        data_train1.append(img)\n",
    "\n",
    "        if(label=='CLL'):\n",
    "            labels_train1.append(0)\n",
    "        if(label=='MCL'):\n",
    "            labels_train1.append(2)\n",
    "print(len(data_train1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,len(labels_train)):\n",
    "    if(labels_train[i]==\"CLL\"):\n",
    "        labels_train[i]=0\n",
    "    if(labels_train[i]==\"FL\"):\n",
    "        labels_train[i]=1\n",
    "    if(labels_train[i]==\"MCL\"):\n",
    "        labels_train[i]=2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train,data_test,labels_train,labels_test=train_test_split(data_train,labels_train,test_size=0.2, random_state=42)\n",
    "data_train, data_validation, labels_train, labels_validation = train_test_split(data_train, labels_train, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150\n",
      "38\n",
      "47\n"
     ]
    }
   ],
   "source": [
    "data_train2=[]\n",
    "labels_train2=[]\n",
    "data_test2=[]\n",
    "labels_test2=[]\n",
    "data_train2, data_test2, labels_train2, labels_test2 = train_test_split(data_train1, labels_train1, test_size=0.2, random_state=42)\n",
    "\n",
    "data_train2, data_validation2, labels_train2, labels_validation2 = train_test_split(data_train2, labels_train2, test_size=0.2, random_state=42)\n",
    "\n",
    "print(len(data_train2))\n",
    "print(len(data_validation2))\n",
    "print(len(data_test2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count Train\n",
      "86\n",
      "85\n",
      "68\n"
     ]
    }
   ],
   "source": [
    "print(\"Count Train\")\n",
    "print(labels_train.count(0))\n",
    "print(labels_train.count(1))\n",
    "print(labels_train.count(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113\n",
      "0\n",
      "122\n"
     ]
    }
   ],
   "source": [
    "print(labels_train1.count(0))\n",
    "print(labels_train1.count(1))\n",
    "print(labels_train1.count(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count Test\n",
      "14\n",
      "32\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "print(\"Count Test\")\n",
    "print(labels_test.count(0))\n",
    "print(labels_test.count(1))\n",
    "print(labels_test.count(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count Validation\n",
      "13\n",
      "22\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "print(\"Count Validation\")\n",
    "print(labels_validation.count(0))\n",
    "print(labels_validation.count(1))\n",
    "print(labels_validation.count(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_train=keras.utils.to_categorical(labels_train,3)\n",
    "labels_validation=keras.utils.to_categorical(labels_validation,3)\n",
    "labels_test=keras.utils.to_categorical(labels_test,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_train2=keras.utils.to_categorical(labels_train2,3)\n",
    "labels_validation2=keras.utils.to_categorical(labels_validation2,3)\n",
    "labels_test2=keras.utils.to_categorical(labels_test2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train=np.asarray(data_train)\n",
    "data_validation=np.asarray(data_validation)\n",
    "data_test=np.asarray(data_test)\n",
    "\n",
    "data_train = data_train.astype('float32')\n",
    "data_validation = data_validation.astype('float32')\n",
    "data_test = data_test.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 260, 347, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_validation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train2=np.asarray(data_train2)\n",
    "data_validation2=np.asarray(data_validation2)\n",
    "data_test2=np.asarray(data_test2)\n",
    "\n",
    "data_train2 = data_train2.astype('float32')\n",
    "data_validation2 = data_validation2.astype('float32')\n",
    "data_test2 = data_test2.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train/=255\n",
    "data_validation/=255\n",
    "data_test/=255\n",
    "acc=[]\n",
    "val_loss =[]\n",
    "loss =[]\n",
    "val_acc=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train2/=255\n",
    "data_validation2/=255\n",
    "data_test2/=255\n",
    "acc2=[]\n",
    "val_loss2 =[]\n",
    "loss2 =[]\n",
    "val_acc2=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n    lrate = initial_lrate\\n    if (epoch == 10) :\\n        lrate = 0.00001\\n    elif (epoch == 20) :\\n        lrate = 0.000001\\n    elif (epoch == 30) :\\n        lrate = 0.0000001\\n'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def step_decay(epoch):\n",
    "    initial_lrate=0.0001\n",
    "    drop=0.1\n",
    "    epochs_drop=16.0\n",
    "    lrate=initial_lrate * math.pow(drop,math.floor((1+epoch)/epochs_drop))\n",
    "    return lrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"input_1:0\", shape=(?, ?, ?, 3), dtype=float32)\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, None, 3 9216        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, None, 3 96          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, None, 3 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, None, 6 18432       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, None, 6 192         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None, 6 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, None, 8 5120        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, None, 8 240         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, None, 8 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, None, 1 138240      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, None, 1 576         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, None, 1 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 1 0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, None, None, 6 192         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, None, 6 0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, None, None, 9 55296       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, None, 4 144         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, None, 4 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, None, None, 1 0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, None, 6 76800       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, None, None, 9 82944       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, None, None, 3 6144        average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, None, 6 192         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, None, 6 192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, None, None, 9 288         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, None, None, 3 96          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, None, 6 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, None, None, 9 0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, None, None, 3 0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, None, None, 2 0           activation_6[0][0]               \n",
      "                                                                 activation_8[0][0]               \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, None, None, 6 192         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, None, None, 6 0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, None, None, 4 12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, None, None, 9 55296       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, None, None, 4 144         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, None, None, 9 288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, None, None, 4 0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, None, None, 9 0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, None, None, 2 0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, None, None, 6 76800       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, None, None, 9 82944       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, None, None, 6 16384       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, None, None, 6 192         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, None, None, 6 192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, None, None, 9 288         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, None, None, 6 192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, None, None, 6 0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, None, None, 6 0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, None, None, 9 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, None, None, 6 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, None, None, 2 0           activation_13[0][0]              \n",
      "                                                                 activation_15[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, None, None, 6 192         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, None, None, 6 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, None, None, 4 13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, None, None, 9 55296       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, None, None, 4 144         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, None, None, 9 288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, None, None, 4 0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, None, None, 9 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, None, None, 2 0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, None, None, 6 76800       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, None, None, 9 82944       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, None, None, 6 18432       average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, None, None, 6 192         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, None, None, 6 192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, None, None, 9 288         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, None, None, 6 192         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, None, None, 6 0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, None, None, 6 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, None, None, 9 0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, None, None, 6 0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, None, None, 2 0           activation_20[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, None, None, 6 18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, None, None, 6 192         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, None, None, 6 0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, None, None, 9 55296       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, None, None, 9 288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, None, None, 9 0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, None, None, 3 995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, None, None, 9 82944       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, None, None, 3 1152        conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, None, None, 9 288         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, None, None, 3 0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, None, None, 9 0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, None, None, 7 0           activation_27[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, None, None, 1 384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, None, None, 1 0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, None, None, 1 114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, None, None, 1 384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, None, None, 1 0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, None, None, 1 114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, None, None, 1 384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, None, None, 1 384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, None, None, 1 0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, None, None, 1 0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, None, None, 1 114688      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, None, None, 1 114688      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, None, None, 1 384         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, None, None, 1 384         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, None, None, 1 0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, None, None, 1 0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, None, None, 7 0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, None, None, 1 147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, None, None, 1 172032      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, None, None, 1 172032      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, None, None, 1 576         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, None, None, 1 576         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, None, None, 1 576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, None, None, 1 576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, None, None, 1 0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, None, None, 1 0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, None, None, 1 0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, None, None, 1 0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, None, None, 7 0           activation_31[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "                                                                 activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, None, None, 1 480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, None, None, 1 0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, None, None, 1 179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, None, None, 1 480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, None, None, 1 179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, None, None, 1 480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, None, None, 1 480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, None, None, 1 0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, None, None, 1 0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, None, None, 1 179200      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, None, None, 1 179200      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, None, None, 1 480         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, None, None, 1 480         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, None, None, 1 0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, None, None, 7 0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, None, None, 1 147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, None, None, 1 215040      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, None, None, 1 215040      activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, None, None, 1 576         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, None, None, 1 576         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, None, None, 1 576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, None, None, 1 576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, None, None, 1 0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, None, None, 1 0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, None, None, 7 0           activation_41[0][0]              \n",
      "                                                                 activation_44[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "                                                                 activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, None, None, 1 480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, None, None, 1 0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, None, None, 1 179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, None, None, 1 480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, None, None, 1 179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, None, None, 1 480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, None, None, 1 480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, None, None, 1 0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, None, None, 1 179200      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, None, None, 1 179200      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, None, None, 1 480         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, None, None, 1 480         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, None, None, 7 0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, None, None, 1 147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, None, None, 1 215040      activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, None, None, 1 215040      activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, None, None, 1 576         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, None, None, 1 576         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, None, None, 1 576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, None, None, 1 576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, None, None, 1 0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, None, None, 1 0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, None, None, 7 0           activation_51[0][0]              \n",
      "                                                                 activation_54[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "                                                                 activation_60[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, None, None, 1 576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, None, None, 1 0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, None, None, 1 258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, None, None, 1 576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, None, None, 1 258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, None, None, 1 576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, None, None, 1 576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, None, None, 1 0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, None, None, 1 258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, None, None, 1 258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, None, None, 1 576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, None, None, 1 576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, None, None, 1 0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, None, None, 1 0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, None, None, 7 0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, None, None, 1 258048      activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, None, None, 1 258048      activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, None, None, 1 576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, None, None, 1 576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, None, None, 1 576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, None, None, 1 576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, None, None, 1 0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, None, None, 1 0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, None, None, 1 0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, None, None, 7 0           activation_61[0][0]              \n",
      "                                                                 activation_64[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "                                                                 activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, None, None, 1 576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, None, None, 1 0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, None, None, 1 258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, None, None, 1 576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, None, None, 1 0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, None, None, 1 258048      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, None, None, 1 576         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, None, None, 1 576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, None, None, 1 0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, None, None, 1 0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, None, None, 3 552960      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, None, None, 1 331776      activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, None, None, 3 960         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, None, None, 1 576         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, None, None, 3 0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, None, None, 1 0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, None, None, 1 0           activation_72[0][0]              \n",
      "                                                                 activation_76[0][0]              \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, None, None, 4 573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, None, None, 4 1344        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, None, None, 4 0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, None, None, 3 491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, None, None, 3 1548288     activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, None, None, 3 1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, None, None, 3 1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, None, None, 3 0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, None, None, 3 0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, None, None, 1 0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, None, None, 3 409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, None, None, 3 1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, None, None, 3 1152        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, None, None, 3 1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, None, None, 3 1152        conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, None, None, 1 245760      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, None, None, 3 960         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, None, None, 3 0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, None, None, 3 0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, None, None, 3 0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, None, None, 3 0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, None, None, 1 576         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, None, None, 3 0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_79[0][0]              \n",
      "                                                                 activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 7 0           activation_83[0][0]              \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, None, None, 1 0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, None, None, 2 0           activation_77[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_85[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, None, None, 4 917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, None, None, 4 1344        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, None, None, 4 0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, None, None, 3 786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, None, None, 3 1548288     activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, None, None, 3 1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, None, None, 3 1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, None, None, 3 0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, None, None, 3 0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, None, None, 2 0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, None, None, 3 655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, None, None, 3 1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, None, None, 3 1152        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, None, None, 3 1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, None, None, 3 1152        conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, None, None, 1 393216      average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, None, None, 3 960         conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, None, None, 3 0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, None, None, 3 0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, None, None, 3 0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, None, None, 3 0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, None, None, 1 576         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, None, None, 3 0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_88[0][0]              \n",
      "                                                                 activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, None, None, 7 0           activation_92[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, None, None, 1 0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, None, None, 2 0           activation_86[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_2[0][0]              \n",
      "                                                                 activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 75)           153675      global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 75)           0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 3)            228         dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 21,956,687\n",
      "Trainable params: 21,922,255\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = InceptionV3(weights='imagenet', include_top=False)\n",
    "\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "\n",
    "# Let's add a fully-connected layer\n",
    "x = Dense(75, activation='relu')(x)\n",
    "\n",
    "# Now a dropout layer\n",
    "y = Dropout(0.3)(x)\n",
    "\n",
    "# And a logistic layer -- let's say we have 10 classes\n",
    "predictions = Dense(3, activation='softmax')(y)\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "print(base_model.input)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using real-time data augmentation.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use the retry module or similar alternatives.\n",
      "WARNING:tensorflow:Variable *= will be deprecated. Use variable.assign_mul if you want assignment to the variable value or 'x = x * y' if you want a new python Tensor object.\n",
      "Epoch 1/35\n",
      "\n",
      "Epoch 00001: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 24s 1s/step - loss: 1.0910 - acc: 0.4276 - val_loss: 0.9591 - val_acc: 0.5333\n",
      "Epoch 2/35\n",
      "\n",
      "Epoch 00002: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 450ms/step - loss: 0.8963 - acc: 0.6018 - val_loss: 0.8967 - val_acc: 0.6000\n",
      "Epoch 3/35\n",
      "\n",
      "Epoch 00003: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 472ms/step - loss: 0.6772 - acc: 0.7266 - val_loss: 0.8312 - val_acc: 0.6333\n",
      "Epoch 4/35\n",
      "\n",
      "Epoch 00004: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 460ms/step - loss: 0.5787 - acc: 0.7696 - val_loss: 0.5092 - val_acc: 0.7667\n",
      "Epoch 5/35\n",
      "\n",
      "Epoch 00005: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 450ms/step - loss: 0.4149 - acc: 0.8303 - val_loss: 0.3510 - val_acc: 0.8667\n",
      "Epoch 6/35\n",
      "\n",
      "Epoch 00006: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 459ms/step - loss: 0.3596 - acc: 0.8732 - val_loss: 0.4775 - val_acc: 0.8000\n",
      "Epoch 7/35\n",
      "\n",
      "Epoch 00007: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 465ms/step - loss: 0.3011 - acc: 0.8907 - val_loss: 0.3431 - val_acc: 0.8667\n",
      "Epoch 8/35\n",
      "\n",
      "Epoch 00008: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 461ms/step - loss: 0.2673 - acc: 0.8980 - val_loss: 0.4371 - val_acc: 0.8500\n",
      "Epoch 9/35\n",
      "\n",
      "Epoch 00009: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 473ms/step - loss: 0.2676 - acc: 0.9006 - val_loss: 0.2360 - val_acc: 0.9000\n",
      "Epoch 10/35\n",
      "\n",
      "Epoch 00010: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 453ms/step - loss: 0.2852 - acc: 0.9035 - val_loss: 0.1704 - val_acc: 0.9000\n",
      "Epoch 11/35\n",
      "\n",
      "Epoch 00011: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 446ms/step - loss: 0.1884 - acc: 0.9288 - val_loss: 0.1911 - val_acc: 0.9167\n",
      "Epoch 12/35\n",
      "\n",
      "Epoch 00012: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 465ms/step - loss: 0.2028 - acc: 0.9354 - val_loss: 0.0664 - val_acc: 0.9667\n",
      "Epoch 13/35\n",
      "\n",
      "Epoch 00013: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 461ms/step - loss: 0.1519 - acc: 0.9372 - val_loss: 0.0925 - val_acc: 0.9500\n",
      "Epoch 14/35\n",
      "\n",
      "Epoch 00014: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 459ms/step - loss: 0.1996 - acc: 0.9160 - val_loss: 0.2627 - val_acc: 0.9333\n",
      "Epoch 15/35\n",
      "\n",
      "Epoch 00015: LearningRateScheduler reducing learning rate to 0.0001.\n",
      "20/20 [==============================] - 9s 457ms/step - loss: 0.1447 - acc: 0.9572 - val_loss: 0.3491 - val_acc: 0.9500\n",
      "Epoch 16/35\n",
      "\n",
      "Epoch 00016: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 439ms/step - loss: 0.1753 - acc: 0.9509 - val_loss: 0.2566 - val_acc: 0.9500\n",
      "Epoch 17/35\n",
      "\n",
      "Epoch 00017: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 458ms/step - loss: 0.1520 - acc: 0.9402 - val_loss: 0.2033 - val_acc: 0.9500\n",
      "Epoch 18/35\n",
      "\n",
      "Epoch 00018: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 460ms/step - loss: 0.1151 - acc: 0.9684 - val_loss: 0.1213 - val_acc: 0.9667\n",
      "Epoch 19/35\n",
      "\n",
      "Epoch 00019: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 10s 475ms/step - loss: 0.0925 - acc: 0.9717 - val_loss: 0.0761 - val_acc: 0.9667\n",
      "Epoch 20/35\n",
      "\n",
      "Epoch 00020: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 442ms/step - loss: 0.1236 - acc: 0.9571 - val_loss: 0.0426 - val_acc: 0.9833\n",
      "Epoch 21/35\n",
      "\n",
      "Epoch 00021: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 455ms/step - loss: 0.0792 - acc: 0.9779 - val_loss: 0.0258 - val_acc: 1.0000\n",
      "Epoch 22/35\n",
      "\n",
      "Epoch 00022: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 460ms/step - loss: 0.0959 - acc: 0.9701 - val_loss: 0.0265 - val_acc: 1.0000\n",
      "Epoch 23/35\n",
      "\n",
      "Epoch 00023: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 469ms/step - loss: 0.0904 - acc: 0.9670 - val_loss: 0.0195 - val_acc: 1.0000\n",
      "Epoch 24/35\n",
      "\n",
      "Epoch 00024: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 459ms/step - loss: 0.1143 - acc: 0.9636 - val_loss: 0.0213 - val_acc: 1.0000\n",
      "Epoch 25/35\n",
      "\n",
      "Epoch 00025: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 452ms/step - loss: 0.1238 - acc: 0.9587 - val_loss: 0.0214 - val_acc: 1.0000\n",
      "Epoch 26/35\n",
      "\n",
      "Epoch 00026: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 10s 476ms/step - loss: 0.0895 - acc: 0.9781 - val_loss: 0.0209 - val_acc: 0.9833\n",
      "Epoch 27/35\n",
      "\n",
      "Epoch 00027: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 475ms/step - loss: 0.0759 - acc: 0.9732 - val_loss: 0.0122 - val_acc: 1.0000\n",
      "Epoch 28/35\n",
      "\n",
      "Epoch 00028: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 469ms/step - loss: 0.1165 - acc: 0.9654 - val_loss: 0.0165 - val_acc: 1.0000\n",
      "Epoch 29/35\n",
      "\n",
      "Epoch 00029: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 469ms/step - loss: 0.1432 - acc: 0.9475 - val_loss: 0.0163 - val_acc: 1.0000\n",
      "Epoch 30/35\n",
      "\n",
      "Epoch 00030: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 9s 469ms/step - loss: 0.0866 - acc: 0.9743 - val_loss: 0.0246 - val_acc: 0.9833\n",
      "Epoch 31/35\n",
      "\n",
      "Epoch 00031: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "20/20 [==============================] - 10s 477ms/step - loss: 0.1112 - acc: 0.9652 - val_loss: 0.0396 - val_acc: 0.9833\n",
      "Epoch 32/35\n",
      "\n",
      "Epoch 00032: LearningRateScheduler reducing learning rate to 1.0000000000000002e-06.\n",
      "20/20 [==============================] - 9s 456ms/step - loss: 0.0716 - acc: 0.9825 - val_loss: 0.0369 - val_acc: 0.9833\n",
      "Epoch 33/35\n",
      "\n",
      "Epoch 00033: LearningRateScheduler reducing learning rate to 1.0000000000000002e-06.\n",
      "20/20 [==============================] - 10s 486ms/step - loss: 0.0778 - acc: 0.9667 - val_loss: 0.0342 - val_acc: 0.9833\n",
      "Epoch 34/35\n",
      "\n",
      "Epoch 00034: LearningRateScheduler reducing learning rate to 1.0000000000000002e-06.\n",
      "20/20 [==============================] - 9s 468ms/step - loss: 0.1644 - acc: 0.9409 - val_loss: 0.0290 - val_acc: 0.9833\n",
      "Epoch 35/35\n",
      "\n",
      "Epoch 00035: LearningRateScheduler reducing learning rate to 1.0000000000000002e-06.\n",
      "20/20 [==============================] - 9s 456ms/step - loss: 0.1001 - acc: 0.9748 - val_loss: 0.0251 - val_acc: 0.9833\n"
     ]
    }
   ],
   "source": [
    "data_aug = True\n",
    "model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001,decay = 1e-03),metrics=['accuracy'])\n",
    "\n",
    "if not data_aug:\n",
    "    print('Not using real-time data augmentation.')\n",
    "    history = model.fit(data_train,labels_train,validation_data=(data_validation,labels_validation),epochs=15,batch_size=4,verbose=2,steps_per_epoch=None)\n",
    "    \n",
    "\n",
    "else:\n",
    "    print('Using real-time data augmentation.')\n",
    "    lr=keras.callbacks.LearningRateScheduler(step_decay,verbose=1)\n",
    "   # This will do preprocessing and realtime data augmentation:\n",
    "    tbCallback=keras.callbacks.TensorBoard(log_dir='./Logs/Tensorboard_Logs', histogram_freq=0, \n",
    "                                           batch_size=32, write_graph=False, write_grads=False, \n",
    "                                           write_images=False, embeddings_freq=0, embeddings_layer_names=None, \n",
    "                                           embeddings_metadata=None)\n",
    "    checkpoint=keras.callbacks.ModelCheckpoint('./Logs/Checkpoint', monitor='val_loss', \n",
    "                                               verbose=0, save_best_only=False, \n",
    "                                               save_weights_only=False, \n",
    "                                               mode='auto',\n",
    "                                               period=1)\n",
    "\n",
    "    datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range=360,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        width_shift_range=0.7,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.7,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=True,  # randomly flip images\n",
    "        vertical_flip=True)  # randomly flip images\n",
    "\n",
    "\n",
    "#     Compute quantities required for feature-wise normalization\n",
    "#     (std, mean, and principal components if ZCA whitening is applied).\n",
    "    datagen.fit(data_train)\n",
    "\n",
    "    # Fit the model on the batches generated by datagen.flow().\n",
    "    history = model.fit_generator(datagen.flow(data_train, labels_train,batch_size=32),steps_per_epoch=20,epochs=35, validation_data=(data_validation, labels_validation),workers=4,callbacks=[tbCallback, checkpoint,lr])\n",
    "\n",
    "loss.append(history.history['loss'])\n",
    "val_loss.append(history.history['val_loss'])\n",
    "acc.append(history.history['acc'])\n",
    "val_acc.append(history.history['val_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from keras import backend as K\n",
    "import numpy as np\n",
    "img=data_train[4]            #pass any sample image for which we have to find features\n",
    "img=np.expand_dims(img,axis=0)                      #expand the dimensions\n",
    "def layer_to_visualize(layer):\n",
    "    inputs = [K.learning_phase()] + model.inputs\n",
    "\n",
    "    _convout1_f = K.function(inputs, [layer.output])\n",
    "    def convout1_f(X):\n",
    "     # The [0] is to disable the training phase flag\n",
    "        return _convout1_f([0] + [X])\n",
    "\n",
    "    convolutions = convout1_f(img)\n",
    "    convolutions = np.squeeze(convolutions)\n",
    "    print ('Shape of conv:', convolutions.shape)\n",
    "    \n",
    "    n = convolutions.shape[0]\n",
    "    n = int(np.ceil(np.sqrt(n)))\n",
    "    print(convolutions.shape)\n",
    "    \n",
    "    \n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    for i in range(len(convolutions)):\n",
    "        ax = fig.add_subplot(n,n,i+1)\n",
    "        ax.imshow(convolutions[i])#, cmap='gray')\n",
    "       \n",
    "    return convolutions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of conv: (129, 173, 32)\n",
      "(129, 173, 32)\n",
      "Shape of conv: (129, 173, 32)\n",
      "(129, 173, 32)\n",
      "Shape of conv: (127, 171, 32)\n",
      "(127, 171, 32)\n",
      "Shape of conv: (14, 20, 384)\n",
      "(14, 20, 384)\n",
      "Shape of conv: (14, 20, 96)\n",
      "(14, 20, 96)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 129 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 129 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 127 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization of conv layers on activation relu\n",
    "f1=layer_to_visualize(model.layers[1])\n",
    "f2=layer_to_visualize(model.layers[3])\n",
    "f3=layer_to_visualize(model.layers[6])\n",
    "f4=layer_to_visualize(model.layers[93])\n",
    "f5=layer_to_visualize(model.layers[94])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 173, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "convolutions=f1[0:2]\n",
    "n = convolutions.shape[0]\n",
    "n = int(np.ceil(np.sqrt(n)))\n",
    "print(convolutions.shape)\n",
    "    \n",
    "    \n",
    "fig = plt.figure(figsize=(12,8))\n",
    "for i in range(len(convolutions)):\n",
    "    ax = fig.add_subplot(n,n,i+1)\n",
    "    ax.imshow(convolutions[i],cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=history.history['loss']\n",
    "x=range(0,35)\n",
    "plt.plot(x,y)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1642627677631875\n",
      "0.9733333333333334\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "score=model.evaluate(data_test,labels_test,verbose=2)\n",
    "print(score[0])\n",
    "print(score[1])\n",
    "cm=confusion_matrix(np.argmax(labels_test,axis=1), np.argmax(model.predict(data_test),axis=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[13  0  1]\n",
      " [ 0 31  1]\n",
      " [ 0  0 29]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import itertools\n",
    "cm_plot_labels=[\"CLL\",\"FL\",\"MCL\"]\n",
    "plot_confusion_matrix(cm, cm_plot_labels, title=\"Confusion Matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json = model.to_json()\n",
    "with open(\"model_final_973.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "model.save_weights(\"model_final_973.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}