Transfer Learning via InceptionV3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning via InceptionV3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Below is a demo and a brief introduction of how to work with transfer learning . I have used imagenet model \"INCEPTIONV3\" as my base model for better diagnosis purpose than the self model that I have laid architecture of and trained in the other notebook .**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['val', 'train', 'test', '.DS_Store']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import cv2                 \n",
    "import numpy as np         \n",
    "import os                  \n",
    "from random import shuffle\n",
    "from tqdm import tqdm  \n",
    "import scipy\n",
    "import skimage\n",
    "from skimage.transform import resize\n",
    "print(os.listdir(\"../input/chest-xray-pneumonia/chest_xray/chest_xray/\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NORMAL', '.DS_Store', 'PNEUMONIA']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(\"../input/chest-xray-pneumonia/chest_xray/chest_xray/train/\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "c857b24a29f6a9c08b97db1061d1ca787a2b903c",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TRAIN_DIR = \"../input/chest-xray-pneumonia/chest_xray/chest_xray/train/\"\n",
    "TEST_DIR =  \"../input/chest-xray-pneumonia/chest_xray/chest_xray/test/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "693676686090d22160ab9c2f01ade2e3b276f2df",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_label(Dir):\n",
    "    for nextdir in os.listdir(Dir):\n",
    "        if not nextdir.startswith('.'):\n",
    "            if nextdir in ['NORMAL']:\n",
    "                label = 0\n",
    "            elif nextdir in ['PNEUMONIA']:\n",
    "                label = 1\n",
    "            else:\n",
    "                label = 2\n",
    "    return nextdir, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pre-processing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "9ca7276d8b0491c0a80a07d2a62fad3f2191f916",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def preprocessing_data(Dir):\n",
    "    X = []\n",
    "    y = []\n",
    "    \n",
    "    for nextdir in os.listdir(Dir):\n",
    "        nextdir, label = get_label(Dir)\n",
    "        temp = Dir + nextdir\n",
    "        \n",
    "        for image_filename in tqdm(os.listdir(temp)):\n",
    "            path = os.path.join(temp + '/' , image_filename)\n",
    "            img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)\n",
    "            if img is not None:\n",
    "                img = skimage.transform.resize(img, (150, 150, 3))\n",
    "                img = np.asarray(img)\n",
    "                X.append(img)\n",
    "                y.append(label)\n",
    "            \n",
    "    X = np.asarray(X)\n",
    "    y = np.asarray(y)\n",
    "    \n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "70b9a29f77e86bc117c6cbbb56b8ef3351be0d8e",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_data(Dir):\n",
    "    X = []\n",
    "    y = []\n",
    "    for nextDir in os.listdir(Dir):\n",
    "        if not nextDir.startswith('.'):\n",
    "            if nextDir in ['NORMAL']:\n",
    "                label = 0\n",
    "            elif nextDir in ['PNEUMONIA']:\n",
    "                label = 1\n",
    "            else:\n",
    "                label = 2\n",
    "                \n",
    "            temp = Dir + nextDir\n",
    "                \n",
    "            for file in tqdm(os.listdir(temp)):\n",
    "                img = cv2.imread(temp + '/' + file)\n",
    "                if img is not None:\n",
    "                    img = skimage.transform.resize(img, (150, 150, 3))\n",
    "                    #img_file = scipy.misc.imresize(arr=img_file, size=(299, 299, 3))\n",
    "                    img = np.asarray(img)\n",
    "                    X.append(img)\n",
    "                    y.append(label)\n",
    "                    \n",
    "    X = np.asarray(X)\n",
    "    y = np.asarray(y)\n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "ac724e88df1a7a3045e4b94e6b21498230b093f4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1342 [00:00<?, ?it/s]/opt/conda/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
      "100%|██████████| 1342/1342 [03:18<00:00,  6.75it/s]\n",
      "100%|██████████| 3876/3876 [02:57<00:00, 21.83it/s]\n"
     ]
    }
   ],
   "source": [
    "X_train, y_train = get_data(TRAIN_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "4f87ce1ada949fb741257569c2761cc4d7ed4264"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/234 [00:00<?, ?it/s]/opt/conda/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
      "100%|██████████| 234/234 [00:21<00:00, 10.70it/s]\n",
      "100%|██████████| 390/390 [00:11<00:00, 34.20it/s]\n"
     ]
    }
   ],
   "source": [
    "X_test , y_test = get_data(TEST_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "7b6282130ca6091b541670d6a34f63725ca1d0c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5216, 150, 150, 3) \n",
      " (624, 150, 150, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,'\\n',X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "b5d30f44384d75880d8d7ca6a57e570e8025bd50"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5216,) \n",
      " (624,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape,'\\n',y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "78e6bd8959bde4dc63470dcca8e5c692e08a5a28"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-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": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train = to_categorical(y_train, 2)\n",
    "y_test = to_categorical(y_test, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "29e7a18a73fbcdab4d54a8b85fe58fabc4b8a2ed",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Pimages = os.listdir(TRAIN_DIR + \"PNEUMONIA\")\n",
    "Nimages = os.listdir(TRAIN_DIR + \"NORMAL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the XRays of No Pneumonia and Pneumonia patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "69032bcfc05b40dd968aff77974a6f7a5c192f19"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Left) - No Pneumonia Vs (Right) - Pneumonia\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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332cdQVePT0cTlWvn3qr85h5gGjjYEVGeT5oovc/nUK/Xy9BrTdJNvbjHEVvK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GEQ2P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\nBAI9+H87O4uSO+rQlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcaeba5c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Left) - No Pneumonia Vs (Right) - Pneumonia\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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ftfeS54XzwNwrSWPHgGTAeUJr0UBqHsfEY1CVvMQ8yaLyKZz/giamyA2iHina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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcaeb807940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Left) - No Pneumonia Vs (Right) - Pneumonia\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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XlMLCXCRuFjl//nzW/ZbS6/Ui+JaD66QzfaYfanSC+YbjG//y8nI4UPCZr776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\nJtgdhyz5C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FNPpYHNBMuA9TDhu+66K53nIpdaPu4FLM8DRT03NzfTC02zXEeMHgD93u/F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\npno5J4iyLC8v1yI1ESP0bXNzM1588cWIGAUO0NaU7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THQ+naagJHTMmBjpuGpeC4sMsXY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\n/H8Y4iwmh4mAMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcaeba2eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Left) - No Pneumonia Vs (Right) - Pneumonia\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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4nqdcXxwdHXUapoS5/jzoqDRMiRAcaW0ecFJmG9Xdzceqh9AEEovd/MK0MLpW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\ndsKvJLruY6Wx1dx2hEnzSwi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O1pJc6ACiSF4/IqFsWw4FvFupyFKVKlWqVKlSpcoaeWiR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\nxdPJryXt3d2RdQ3LdU4J3c9ZzsnJSTcWChjKdBF6jhOz1QdCAzVmW1t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wQH/jxo1E6SW9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T1nP3djpm/1nlpe/T/Kw8dWlFpCBxCLkTGx6ISK+kf5eDGN3/CJ7RLORYg+gJQE+Htra8tefvll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\nnS5ldB4TR2YW5D5rlmM3P9uZasp9V2b642vSfsKJ1B6td+/jWT2dR0l2s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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcaeb880b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Left) - No Pneumonia Vs (Right) - Pneumonia\n",
      "-----------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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AK5brPOX0Ap83vg4r23DFihUrVqxYsWJn2LlAlqqqWiK1KSG7TYCuKXzft1J0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\nvF4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcaeb9e5a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plotter(i):\n",
    "    imagep1 = cv2.imread(TRAIN_DIR+\"PNEUMONIA/\"+Pimages[i])\n",
    "    imagep1 = skimage.transform.resize(imagep1, (150, 150, 3) , mode = 'reflect')\n",
    "    imagen1 = cv2.imread(TRAIN_DIR+\"NORMAL/\"+Nimages[i])\n",
    "    imagen1 = skimage.transform.resize(imagen1, (150, 150, 3))\n",
    "    pair = np.concatenate((imagen1, imagep1), axis=1)\n",
    "    print(\"(Left) - No Pneumonia Vs (Right) - Pneumonia\")\n",
    "    print(\"-----------------------------------------------------------------------------------------------------------------------------------\")\n",
    "    plt.figure(figsize=(10,5))\n",
    "    plt.imshow(pair)\n",
    "    plt.show()\n",
    "for i in range(5,10):\n",
    "    plotter(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "9e574dcb15d90d3d7ac16e6c6ec99870122fea8b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/Keras-2.1.5-py3.6.egg/keras/callbacks.py:919: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` insted.\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ReduceLROnPlateau , ModelCheckpoint , LearningRateScheduler\n",
    "lr_reduce = ReduceLROnPlateau(monitor='val_acc', factor=0.1, epsilon=0.0001, patience=1, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reducing the learning rate timely using callbacks in Keras and also checkpointing the model when achieved the best so far quantity that is monitored , in this dataset I am monitoring validation accuracy.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Saving the weights of the best model after checkpointing in transferlearning_weights.hdf5 .**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "3bc49c68134527f941e377bc2951707391efa701",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filepath=\"transferlearning_weights.hdf5\"\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "e052d66b2bc0230cba4c258ca2b203c6ee3c49ff",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential , Model\n",
    "from keras.layers import Dense , Activation\n",
    "from keras.layers import Dropout , GlobalAveragePooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.constraints import maxnorm\n",
    "from keras.optimizers import SGD , RMSprop , Adadelta , Adam\n",
    "from keras.layers import Conv2D , BatchNormalization\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.utils import np_utils\n",
    "from keras import backend as K\n",
    "K.set_image_dim_ordering('th')\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "93d0dc1d3cb51d33fe42061f9fd4a04622747359",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train=X_train.reshape(5216,3,150,150)\n",
    "X_test=X_test.reshape(624,3,150,150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing InceptionV3 from Keras but with no weights. Also define the necessary input shape of the resized images which were resized initially. The default image size is 299 X 299 for InceptionV3.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "b3e8e62ba15d81e498e99c343b1a2726c35f5d02",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.applications.inception_v3 import InceptionV3\n",
    "# create the base pre-trained model\n",
    "base_model = InceptionV3(weights=None, include_top=False , input_shape=(3, 150, 150))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The output of the above model was being fed into the after layers that we are laying down. There is no rule of thumb for laying down the after layers after InceptionV3 but practice and intuition. **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The layers were checked multiple times for the output and finally the second last layer of BatchNormalization() did some good for the model. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "a360cfa0fb59bdc0320654f4d2f8ebdf17699606",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = base_model.output\n",
    "x = Dropout(0.5)(x)\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = BatchNormalization()(x)\n",
    "predictions = Dense(2, activation='sigmoid')(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here the I have not debarred any of the InceptionV3 layers to train themselves and rather training only the added layers. I have trained the whole network from start for the different hyperparameters. This approach gave me one of the best results so far. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "802363ca2a8d86b07ee0ae5339f274204d098f9d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base_model.load_weights(\"../input/inceptionweights/inception_v3_weights.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loading the best set of weights on the model which were attained by checkpointing and saving the weights in between the epochs and also prior to overfitting of the model. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "faaf2b3ccc4577a1055a4be288b6ec0e59fd1a91",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Model(inputs=base_model.input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "d3e2aea33939fbddbbb74196e3e626c64eac6ee6",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', \n",
    "                  optimizer='adam', \n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Architecture of  Layers. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "faf6a1570df7072b1854400be0e7fbf797e3f54f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 3, 150, 150)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 32, 74, 74)   864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 32, 74, 74)   96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 32, 74, 74)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 32, 72, 72)   9216        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 32, 72, 72)   96          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 32, 72, 72)   0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 64, 72, 72)   18432       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 64, 72, 72)   192         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 64, 72, 72)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 64, 35, 35)   0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 80, 35, 35)   5120        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 80, 35, 35)   240         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 80, 35, 35)   0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 192, 33, 33)  138240      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 192, 33, 33)  576         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 192, 33, 33)  0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 192, 16, 16)  0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 64, 16, 16)   12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 64, 16, 16)   192         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 64, 16, 16)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 48, 16, 16)   9216        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 96, 16, 16)   55296       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 48, 16, 16)   144         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 96, 16, 16)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 48, 16, 16)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 96, 16, 16)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 192, 16, 16)  0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 64, 16, 16)   12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 64, 16, 16)   76800       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 96, 16, 16)   82944       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 32, 16, 16)   6144        average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 64, 16, 16)   192         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 64, 16, 16)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 96, 16, 16)   288         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 32, 16, 16)   96          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 64, 16, 16)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 64, 16, 16)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 96, 16, 16)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 32, 16, 16)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 256, 16, 16)  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, 64, 16, 16)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 64, 16, 16)   192         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 64, 16, 16)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 48, 16, 16)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 96, 16, 16)   55296       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 48, 16, 16)   144         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 96, 16, 16)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 48, 16, 16)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 96, 16, 16)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 256, 16, 16)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 64, 16, 16)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 64, 16, 16)   76800       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 96, 16, 16)   82944       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 64, 16, 16)   16384       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 64, 16, 16)   192         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 64, 16, 16)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 96, 16, 16)   288         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 64, 16, 16)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 64, 16, 16)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 64, 16, 16)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 96, 16, 16)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 64, 16, 16)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 288, 16, 16)  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, 64, 16, 16)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 64, 16, 16)   192         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 64, 16, 16)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 48, 16, 16)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 96, 16, 16)   55296       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 48, 16, 16)   144         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 96, 16, 16)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 48, 16, 16)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 96, 16, 16)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 288, 16, 16)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 64, 16, 16)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 64, 16, 16)   76800       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 96, 16, 16)   82944       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 64, 16, 16)   18432       average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 64, 16, 16)   192         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 64, 16, 16)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 96, 16, 16)   288         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 64, 16, 16)   192         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 64, 16, 16)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 64, 16, 16)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 96, 16, 16)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 64, 16, 16)   0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 288, 16, 16)  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, 64, 16, 16)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 64, 16, 16)   192         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 64, 16, 16)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 96, 16, 16)   55296       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 96, 16, 16)   288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 96, 16, 16)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 384, 7, 7)    995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 96, 7, 7)     82944       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 384, 7, 7)    1152        conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 96, 7, 7)     288         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 384, 7, 7)    0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 96, 7, 7)     0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 288, 7, 7)    0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 768, 7, 7)    0           activation_27[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 128, 7, 7)    98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 128, 7, 7)    384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 128, 7, 7)    0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 128, 7, 7)    114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 128, 7, 7)    384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 128, 7, 7)    0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 128, 7, 7)    98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 128, 7, 7)    114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 128, 7, 7)    384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 128, 7, 7)    384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 128, 7, 7)    0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 128, 7, 7)    0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 128, 7, 7)    114688      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 128, 7, 7)    114688      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 128, 7, 7)    384         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 128, 7, 7)    384         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 128, 7, 7)    0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 128, 7, 7)    0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 768, 7, 7)    0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 192, 7, 7)    147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 192, 7, 7)    172032      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 192, 7, 7)    172032      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 192, 7, 7)    147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 192, 7, 7)    576         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 192, 7, 7)    576         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 192, 7, 7)    576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 192, 7, 7)    576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 192, 7, 7)    0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 192, 7, 7)    0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 192, 7, 7)    0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 192, 7, 7)    0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 768, 7, 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, 160, 7, 7)    122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 160, 7, 7)    480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 160, 7, 7)    0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 160, 7, 7)    179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 160, 7, 7)    480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 160, 7, 7)    0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 160, 7, 7)    122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 160, 7, 7)    179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 160, 7, 7)    480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 160, 7, 7)    480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 160, 7, 7)    0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 160, 7, 7)    0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 160, 7, 7)    179200      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 160, 7, 7)    179200      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 160, 7, 7)    480         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 160, 7, 7)    480         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 160, 7, 7)    0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 160, 7, 7)    0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 768, 7, 7)    0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 192, 7, 7)    147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 192, 7, 7)    215040      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 192, 7, 7)    215040      activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 192, 7, 7)    147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 192, 7, 7)    576         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 192, 7, 7)    576         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 192, 7, 7)    576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 192, 7, 7)    576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 192, 7, 7)    0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 192, 7, 7)    0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 192, 7, 7)    0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 192, 7, 7)    0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 768, 7, 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, 160, 7, 7)    122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 160, 7, 7)    480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 160, 7, 7)    0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 160, 7, 7)    179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 160, 7, 7)    480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 160, 7, 7)    0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 160, 7, 7)    122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 160, 7, 7)    179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 160, 7, 7)    480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 160, 7, 7)    480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 160, 7, 7)    0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 160, 7, 7)    0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 160, 7, 7)    179200      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 160, 7, 7)    179200      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 160, 7, 7)    480         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 160, 7, 7)    480         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 160, 7, 7)    0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 160, 7, 7)    0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 768, 7, 7)    0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 192, 7, 7)    147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 192, 7, 7)    215040      activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 192, 7, 7)    215040      activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 192, 7, 7)    147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 192, 7, 7)    576         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 192, 7, 7)    576         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 192, 7, 7)    576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 192, 7, 7)    576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 192, 7, 7)    0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 192, 7, 7)    0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 192, 7, 7)    0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 192, 7, 7)    0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 768, 7, 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, 192, 7, 7)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 192, 7, 7)    576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 192, 7, 7)    0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 192, 7, 7)    258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 192, 7, 7)    576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 192, 7, 7)    0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 192, 7, 7)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 192, 7, 7)    258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 192, 7, 7)    576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 192, 7, 7)    576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 192, 7, 7)    0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 192, 7, 7)    0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 192, 7, 7)    258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 192, 7, 7)    258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 192, 7, 7)    576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 192, 7, 7)    576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 192, 7, 7)    0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 192, 7, 7)    0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 768, 7, 7)    0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 192, 7, 7)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 192, 7, 7)    258048      activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 192, 7, 7)    258048      activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 192, 7, 7)    147456      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 192, 7, 7)    576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 192, 7, 7)    576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 192, 7, 7)    576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 192, 7, 7)    576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 192, 7, 7)    0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 192, 7, 7)    0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 192, 7, 7)    0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 192, 7, 7)    0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 768, 7, 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, 192, 7, 7)    147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 192, 7, 7)    576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 192, 7, 7)    0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 192, 7, 7)    258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 192, 7, 7)    576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 192, 7, 7)    0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 192, 7, 7)    147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 192, 7, 7)    258048      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 192, 7, 7)    576         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 192, 7, 7)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 192, 7, 7)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 192, 7, 7)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 320, 3, 3)    552960      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 192, 3, 3)    331776      activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 320, 3, 3)    960         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 192, 3, 3)    576         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 320, 3, 3)    0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 192, 3, 3)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 768, 3, 3)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 1280, 3, 3)   0           activation_72[0][0]              \n",
      "                                                                 activation_76[0][0]              \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 448, 3, 3)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 448, 3, 3)    1344        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 448, 3, 3)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 384, 3, 3)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 384, 3, 3)    1548288     activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 384, 3, 3)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 384, 3, 3)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 384, 3, 3)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 384, 3, 3)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 384, 3, 3)    442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 384, 3, 3)    442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 384, 3, 3)    442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 384, 3, 3)    442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 1280, 3, 3)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 320, 3, 3)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 384, 3, 3)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 384, 3, 3)    1152        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 384, 3, 3)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 384, 3, 3)    1152        conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 192, 3, 3)    245760      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 320, 3, 3)    960         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 384, 3, 3)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 384, 3, 3)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 384, 3, 3)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 384, 3, 3)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 192, 3, 3)    576         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 320, 3, 3)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 768, 3, 3)    0           activation_79[0][0]              \n",
      "                                                                 activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 768, 3, 3)    0           activation_83[0][0]              \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 192, 3, 3)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 2048, 3, 3)   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, 448, 3, 3)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 448, 3, 3)    1344        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 448, 3, 3)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 384, 3, 3)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 384, 3, 3)    1548288     activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 384, 3, 3)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 384, 3, 3)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 384, 3, 3)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 384, 3, 3)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 384, 3, 3)    442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 384, 3, 3)    442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 384, 3, 3)    442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 384, 3, 3)    442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, 2048, 3, 3)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 320, 3, 3)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 384, 3, 3)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 384, 3, 3)    1152        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 384, 3, 3)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 384, 3, 3)    1152        conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, 192, 3, 3)    393216      average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 320, 3, 3)    960         conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 384, 3, 3)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 384, 3, 3)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 384, 3, 3)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 384, 3, 3)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, 192, 3, 3)    576         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 320, 3, 3)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 768, 3, 3)    0           activation_88[0][0]              \n",
      "                                                                 activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 768, 3, 3)    0           activation_92[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, 192, 3, 3)    0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 2048, 3, 3)   0           activation_86[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_2[0][0]              \n",
      "                                                                 activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 2048, 3, 3)   0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 128)          262272      global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_95 (BatchNo (None, 128)          512         dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 2)            258         batch_normalization_95[0][0]     \n",
      "==================================================================================================\n",
      "Total params: 22,065,826\n",
      "Trainable params: 22,031,138\n",
      "Non-trainable params: 34,688\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "fe3abae19909bb628acbf32bedf3f3e6e5c0f837",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 64\n",
    "epochs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "98e83615fceafdd6c733bf579734647e16616122"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5216 samples, validate on 624 samples\n",
      "Epoch 1/10\n",
      "5216/5216 [==============================] - 87s 17ms/step - loss: 0.2498 - acc: 0.8949 - val_loss: 1.4732 - val_acc: 0.6891\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.68910, saving model to transferlearning_weights.hdf5\n",
      "Epoch 2/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.1147 - acc: 0.9553 - val_loss: 1.1212 - val_acc: 0.7436\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.68910 to 0.74359, saving model to transferlearning_weights.hdf5\n",
      "Epoch 3/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.0885 - acc: 0.9659 - val_loss: 0.4242 - val_acc: 0.8686\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.74359 to 0.86859, saving model to transferlearning_weights.hdf5\n",
      "Epoch 4/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.0700 - acc: 0.9737 - val_loss: 0.6816 - val_acc: 0.8077\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "\n",
      "Epoch 00004: val_acc did not improve\n",
      "Epoch 5/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.0394 - acc: 0.9849 - val_loss: 1.2407 - val_acc: 0.7420\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "\n",
      "Epoch 00005: val_acc did not improve\n",
      "Epoch 6/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.0181 - acc: 0.9941 - val_loss: 1.2713 - val_acc: 0.7404\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
      "\n",
      "Epoch 00006: val_acc did not improve\n",
      "Epoch 7/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.0193 - acc: 0.9927 - val_loss: 1.2743 - val_acc: 0.7404\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 1.0000001111620805e-07.\n",
      "\n",
      "Epoch 00007: val_acc did not improve\n",
      "Epoch 8/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.0187 - acc: 0.9941 - val_loss: 1.2677 - val_acc: 0.7388\n",
      "\n",
      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-08.\n",
      "\n",
      "Epoch 00008: val_acc did not improve\n",
      "Epoch 9/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.0184 - acc: 0.9941 - val_loss: 1.2560 - val_acc: 0.7404\n",
      "\n",
      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-09.\n",
      "\n",
      "Epoch 00009: val_acc did not improve\n",
      "Epoch 10/10\n",
      "5216/5216 [==============================] - 64s 12ms/step - loss: 0.0179 - acc: 0.9942 - val_loss: 1.2550 - val_acc: 0.7404\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-10.\n",
      "\n",
      "Epoch 00010: val_acc did not improve\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, validation_data = (X_test , y_test) ,callbacks=[lr_reduce,checkpoint] ,\n",
    "          epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "9abdddbb4b318d827cbb76d408ca686108e4d2bc",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.load_weights(\"transferlearning_weights.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The best validation accuracy achieved is 86.86 % which is better than before. The model will tend to overfit because the model architecture is very much complex and also we are short of data . So better save the weights before 4th epoch prior to overfitting and also not deteriorating the performance of the model. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "dcb097634f5105372bbd0d7f65d6e75cc6db6807"
   },
   "outputs": [
    {
     "data": {
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KDJEOpOMkixTw4osv8uKLLzJmzBgAqqqqWLJkCUceeSRXX301P/vZzzj55JM58sgjW/3Y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\n8qbKtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca98c2d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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27j7N3UvdvbSkpKT7itsVQw6BE34Ei5+BefdHXY20ZeMnsGgmlF4WjFwVESDiUDCzccC9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\nfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca98be1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "b58d4df389b2a2022c786676a6962992caf484c2",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "pred = model.predict(X_test)\n",
    "pred = np.argmax(pred,axis = 1) \n",
    "y_true = np.argmax(y_test,axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confusion matrix is very much necessary for the above model because we are having unequal number of people with pneumonia and no pneumonia. In the above dataset we are having more people suffering from pneumonia than normal people. So, as I told in other notebook , accuracy won't be the soul criteria for determining model performance.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "0c66f57329ca29eda05a7d7a25bcbd7bd5da3b10"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pK0ZPUlaMnqSsGD1JWTF6krJi9CRlxehJyorRk5QVoycpK0ZPUlaMnqSsGD1JWTF6krJi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca98daab00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CM = confusion_matrix(y_true, pred)\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "fig, ax = plot_confusion_matrix(conf_mat=CM ,  figsize=(5, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice how the confusion matrix changed from other notebook and became even better than before. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8564814814814815"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "370 / (370 + 62)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This model even has greater precision than before . Earlier the precision was 78.4 % but now it is having 85.64 % as its precision . Therefore I have minimized false positive in the denominator of the formula . This will even make more sure than before that a person not suffering from pneumonia shouldn't be diagnosed as being suffering from pneumonia. This is what precision is ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9487179487179487"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "370 / (370 + 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The recall for the above trained model is approx. 95 % which is also quite impressive . Also as I told in other notebook more priority is to given to recall when compare to precision for this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9001902166084982"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*0.9487*0.8564 / (0.9487 + 0.8564)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The f1 score is 90.01 % . The harmonic mean of precision and recall."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "8ba8ae0fe59e177b5b602ea37a3b008ebf660a84",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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