Transfer-Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<module 'tensorflow_core._api.v2.version' from '/home/gautham/anaconda3/lib/python3.7/site-packages/tensorflow_core/_api/v2/version/__init__.py'>\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_path = 'elephant.jpg'\n",
    "img = image.load_img(img_path, target_size=(224, 224))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Actual Image\")\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "plt.title(\"Normalised Image\")\n",
    "plt.imshow(x[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: [('n01871265', 'tusker', 0.5334064), ('n02504458', 'African_elephant', 0.28311172), ('n02504013', 'Indian_elephant', 0.1827518)]\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(x)\n",
    "# decode the results into a list of tuples (class, description, probability)\n",
    "# (one such list for each sample in the batch)\n",
    "print('Predicted:', decode_predictions(preds, top=3)[0])\n",
    "# Predicted: [(u'n02504013', u'Indian_elephant', 0.82658225), (u'n01871265', u'tusker', 0.1122357), (u'n02504458', u'African_elephant', 0.061040461)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dogs', 'humans', 'horses', 'cats']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "folders = os.listdir(\"images\")\n",
    "print(folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_data = []\n",
    "\n",
    "labels = []\n",
    "\n",
    "label_dict = {\n",
    "    \"cats\":0,\n",
    "    \"dogs\":1,\n",
    "    \"horses\":2,\n",
    "    \"humans\":3\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dogs\n",
      "humans\n",
      "horses\n",
      "cats\n"
     ]
    }
   ],
   "source": [
    "for ix in folders:\n",
    "    print(ix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "for ix in folders:\n",
    "    path = os.path.join(\"images\",ix)\n",
    "    for im in os.listdir(path):\n",
    "        img = image.load_img(os.path.join(path,im),target_size=((224,224)))\n",
    "        img_array = image.img_to_array(img)\n",
    "        image_data.append(img_array)\n",
    "        labels.append(label_dict[ix])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "808 808\n"
     ]
    }
   ],
   "source": [
    "print(len(image_data),len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "combined = list(zip(image_data,labels))\n",
    "random.shuffle(combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_data[:],labels[:] = zip(*combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 2, 0, 3]\n"
     ]
    }
   ],
   "source": [
    "print(labels[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(808, 224, 224, 3) (808,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "X_train = np.array(image_data)\n",
    "Y_train = np.array(labels)\n",
    "print(X_train.shape,Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(808, 224, 224, 3) (808, 4)\n"
     ]
    }
   ],
   "source": [
    "# One hot encoding\n",
    "from keras.utils import np_utils\n",
    "Y_train = np_utils.to_categorical(Y_train)\n",
    "print(X_train.shape,Y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Model for Resnet50\n",
    "\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers import *\n",
    "from keras.models import Model\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gautham/anaconda3/lib/python3.7/site-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94658560/94653016 [==============================] - 286s 3us/step\n"
     ]
    }
   ],
   "source": [
    "model = ResNet50(include_top=False,weights = 'imagenet',input_shape=(224,224,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 23,534,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          524544      global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 256)          0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 4)            1028        dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 24,113,284\n",
      "Trainable params: 24,060,164\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gautham/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor(\"de..., inputs=Tensor(\"in...)`\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Here there is a problem we input 224,224,3 shapes image and finally we get output which has the size of 7,7,2048 therefore\n",
    "# 7x7x2048 = big no of param so we need to minimize it inorder to minimize we need to use GAP layer which converts this shape\n",
    "# into 1x1x2048 So we need to use GAP=global average pooling layer after the convolutional base\n",
    "av1 = GlobalAveragePooling2D()(model.output)\n",
    "fc1 = Dense(256,activation='relu')(av1)\n",
    "d1 = Dropout(0.5)(fc1)\n",
    "fc2 = Dense(4,activation='softmax')(d1)\n",
    "model_new = Model(input=model.input,outputs=fc2)\n",
    "model_new.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Our new model is end to end trainable model \n",
    "# Fine Tuning\n",
    "adam=Adam(lr=0.00003)\n",
    "model_new.compile(loss='categorical_crossentropy',optimizer=adam,metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 <keras.engine.input_layer.InputLayer object at 0x7f74cf06f110>\n",
      "1 <keras.layers.convolutional.ZeroPadding2D object at 0x7f74cea4fe10>\n",
      "2 <keras.layers.convolutional.Conv2D object at 0x7f7507dccf90>\n",
      "3 <keras.layers.normalization.BatchNormalization object at 0x7f74ceb09f10>\n",
      "4 <keras.layers.core.Activation object at 0x7f74cef3f250>\n",
      "5 <keras.layers.convolutional.ZeroPadding2D object at 0x7f74ceb09d90>\n",
      "6 <keras.layers.pooling.MaxPooling2D object at 0x7f74ceb09990>\n",
      "7 <keras.layers.convolutional.Conv2D object at 0x7f74cea54990>\n",
      "8 <keras.layers.normalization.BatchNormalization object at 0x7f74cea4f110>\n",
      "9 <keras.layers.core.Activation object at 0x7f74cea18f10>\n",
      "10 <keras.layers.convolutional.Conv2D object at 0x7f74cea1bf90>\n",
      "11 <keras.layers.normalization.BatchNormalization object at 0x7f74cea13790>\n",
      "12 <keras.layers.core.Activation object at 0x7f74c4173410>\n",
      "13 <keras.layers.convolutional.Conv2D object at 0x7f74c417a990>\n",
      "14 <keras.layers.convolutional.Conv2D object at 0x7f74c4190dd0>\n",
      "15 <keras.layers.normalization.BatchNormalization object at 0x7f74c4190c50>\n",
      "16 <keras.layers.normalization.BatchNormalization object at 0x7f74c41b0a50>\n",
      "17 <keras.layers.merge.Add object at 0x7f74c41b0ad0>\n",
      "18 <keras.layers.core.Activation object at 0x7f74cee685d0>\n",
      "19 <keras.layers.convolutional.Conv2D object at 0x7f74c4147fd0>\n",
      "20 <keras.layers.normalization.BatchNormalization object at 0x7f74cf06c3d0>\n",
      "21 <keras.layers.core.Activation object at 0x7f74c410b350>\n",
      "22 <keras.layers.convolutional.Conv2D object at 0x7f74c411f8d0>\n",
      "23 <keras.layers.normalization.BatchNormalization object at 0x7f74c40bd290>\n",
      "24 <keras.layers.core.Activation object at 0x7f74c40c5f50>\n",
      "25 <keras.layers.convolutional.Conv2D object at 0x7f74c40e0290>\n",
      "26 <keras.layers.normalization.BatchNormalization object at 0x7f74c40764d0>\n",
      "27 <keras.layers.merge.Add object at 0x7f74c4091f90>\n",
      "28 <keras.layers.core.Activation object at 0x7f74c4076c10>\n",
      "29 <keras.layers.convolutional.Conv2D object at 0x7f74c4097850>\n",
      "30 <keras.layers.normalization.BatchNormalization object at 0x7f74c40aefd0>\n",
      "31 <keras.layers.core.Activation object at 0x7f74c403bb10>\n",
      "32 <keras.layers.convolutional.Conv2D object at 0x7f74c4055290>\n",
      "33 <keras.layers.normalization.BatchNormalization object at 0x7f74c4067d90>\n",
      "34 <keras.layers.core.Activation object at 0x7f74b27be0d0>\n",
      "35 <keras.layers.convolutional.Conv2D object at 0x7f74b27d6210>\n",
      "36 <keras.layers.normalization.BatchNormalization object at 0x7f74b27ed410>\n",
      "37 <keras.layers.merge.Add object at 0x7f74b2789ed0>\n",
      "38 <keras.layers.core.Activation object at 0x7f74b27edb50>\n",
      "39 <keras.layers.convolutional.Conv2D object at 0x7f74b278f790>\n",
      "40 <keras.layers.normalization.BatchNormalization object at 0x7f74b27a5ed0>\n",
      "41 <keras.layers.core.Activation object at 0x7f74b27ba1d0>\n",
      "42 <keras.layers.convolutional.Conv2D object at 0x7f74b274e390>\n",
      "43 <keras.layers.normalization.BatchNormalization object at 0x7f74b275fc90>\n",
      "44 <keras.layers.core.Activation object at 0x7f74b276d1d0>\n",
      "45 <keras.layers.convolutional.Conv2D object at 0x7f74b2706f10>\n",
      "46 <keras.layers.convolutional.Conv2D object at 0x7f74b271d190>\n",
      "47 <keras.layers.normalization.BatchNormalization object at 0x7f74b271d350>\n",
      "48 <keras.layers.normalization.BatchNormalization object at 0x7f74cea18cd0>\n",
      "49 <keras.layers.merge.Add object at 0x7f74cf95fe90>\n",
      "50 <keras.layers.core.Activation object at 0x7f74cf9a8dd0>\n",
      "51 <keras.layers.convolutional.Conv2D object at 0x7f74cea18ed0>\n",
      "52 <keras.layers.normalization.BatchNormalization object at 0x7f74cf5b36d0>\n",
      "53 <keras.layers.core.Activation object at 0x7f74cf2f7e50>\n",
      "54 <keras.layers.convolutional.Conv2D object at 0x7f74cf58d1d0>\n",
      "55 <keras.layers.normalization.BatchNormalization object at 0x7f74cf595f90>\n",
      "56 <keras.layers.core.Activation object at 0x7f74b26f7c90>\n",
      "57 <keras.layers.convolutional.Conv2D object at 0x7f74b2691110>\n",
      "58 <keras.layers.normalization.BatchNormalization object at 0x7f74b26a7490>\n",
      "59 <keras.layers.merge.Add object at 0x7f74b2642f90>\n",
      "60 <keras.layers.core.Activation object at 0x7f74b26a7bd0>\n",
      "61 <keras.layers.convolutional.Conv2D object at 0x7f74b2648850>\n",
      "62 <keras.layers.normalization.BatchNormalization object at 0x7f74b2661ed0>\n",
      "63 <keras.layers.core.Activation object at 0x7f74b26732d0>\n",
      "64 <keras.layers.convolutional.Conv2D object at 0x7f74b2600890>\n",
      "65 <keras.layers.normalization.BatchNormalization object at 0x7f74b261e250>\n",
      "66 <keras.layers.core.Activation object at 0x7f74b2638410>\n",
      "67 <keras.layers.convolutional.Conv2D object at 0x7f74b261e750>\n",
      "68 <keras.layers.normalization.BatchNormalization object at 0x7f74b25d5e10>\n",
      "69 <keras.layers.merge.Add object at 0x7f74b25be1d0>\n",
      "70 <keras.layers.core.Activation object at 0x7f74b25d5d50>\n",
      "71 <keras.layers.convolutional.Conv2D object at 0x7f74b25f4d90>\n",
      "72 <keras.layers.normalization.BatchNormalization object at 0x7f74b2591450>\n",
      "73 <keras.layers.core.Activation object at 0x7f74b2597550>\n",
      "74 <keras.layers.convolutional.Conv2D object at 0x7f74b25add90>\n",
      "75 <keras.layers.normalization.BatchNormalization object at 0x7f74b254b4d0>\n",
      "76 <keras.layers.core.Activation object at 0x7f74b25656d0>\n",
      "77 <keras.layers.convolutional.Conv2D object at 0x7f74b254b9d0>\n",
      "78 <keras.layers.normalization.BatchNormalization object at 0x7f74b2502990>\n",
      "79 <keras.layers.merge.Add object at 0x7f74b256c610>\n",
      "80 <keras.layers.core.Activation object at 0x7f74b2502f50>\n",
      "81 <keras.layers.convolutional.Conv2D object at 0x7f74b252c350>\n",
      "82 <keras.layers.normalization.BatchNormalization object at 0x7f74b24c19d0>\n",
      "83 <keras.layers.core.Activation object at 0x7f74b24c1fd0>\n",
      "84 <keras.layers.convolutional.Conv2D object at 0x7f74b24c8a10>\n",
      "85 <keras.layers.normalization.BatchNormalization object at 0x7f74b24faed0>\n",
      "86 <keras.layers.core.Activation object at 0x7f74b2485c90>\n",
      "87 <keras.layers.convolutional.Conv2D object at 0x7f74b24fac90>\n",
      "88 <keras.layers.convolutional.Conv2D object at 0x7f74b24b73d0>\n",
      "89 <keras.layers.normalization.BatchNormalization object at 0x7f74b24b0c90>\n",
      "90 <keras.layers.normalization.BatchNormalization object at 0x7f74b2459450>\n",
      "91 <keras.layers.merge.Add object at 0x7f74b2459e50>\n",
      "92 <keras.layers.core.Activation object at 0x7f74b246f0d0>\n",
      "93 <keras.layers.convolutional.Conv2D object at 0x7f74b2474310>\n",
      "94 <keras.layers.normalization.BatchNormalization object at 0x7f74b2428890>\n",
      "95 <keras.layers.core.Activation object at 0x7f74cec52a50>\n",
      "96 <keras.layers.convolutional.Conv2D object at 0x7f74b242cfd0>\n",
      "97 <keras.layers.normalization.BatchNormalization object at 0x7f74b23cb310>\n",
      "98 <keras.layers.core.Activation object at 0x7f74b23cbd10>\n",
      "99 <keras.layers.convolutional.Conv2D object at 0x7f74b2381f90>\n",
      "100 <keras.layers.normalization.BatchNormalization object at 0x7f74b239d1d0>\n",
      "101 <keras.layers.merge.Add object at 0x7f74b23b8d10>\n",
      "102 <keras.layers.core.Activation object at 0x7f74b239d8d0>\n",
      "103 <keras.layers.convolutional.Conv2D object at 0x7f74b233d590>\n",
      "104 <keras.layers.normalization.BatchNormalization object at 0x7f74b2355ed0>\n",
      "105 <keras.layers.core.Activation object at 0x7f74b2355e10>\n",
      "106 <keras.layers.convolutional.Conv2D object at 0x7f74b22ff110>\n",
      "107 <keras.layers.normalization.BatchNormalization object at 0x7f74b2310e10>\n",
      "108 <keras.layers.core.Activation object at 0x7f74b2378550>\n",
      "109 <keras.layers.convolutional.Conv2D object at 0x7f74b2313590>\n",
      "110 <keras.layers.normalization.BatchNormalization object at 0x7f74942a1450>\n",
      "111 <keras.layers.merge.Add object at 0x7f74942bff50>\n",
      "112 <keras.layers.core.Activation object at 0x7f74942a1b90>\n",
      "113 <keras.layers.convolutional.Conv2D object at 0x7f74942c3810>\n",
      "114 <keras.layers.normalization.BatchNormalization object at 0x7f749425ce90>\n",
      "115 <keras.layers.core.Activation object at 0x7f7494268310>\n",
      "116 <keras.layers.convolutional.Conv2D object at 0x7f749427d890>\n",
      "117 <keras.layers.normalization.BatchNormalization object at 0x7f7494219250>\n",
      "118 <keras.layers.core.Activation object at 0x7f7494234f90>\n",
      "119 <keras.layers.convolutional.Conv2D object at 0x7f7494219750>\n",
      "120 <keras.layers.normalization.BatchNormalization object at 0x7f74941d3dd0>\n",
      "121 <keras.layers.merge.Add object at 0x7f749423b190>\n",
      "122 <keras.layers.core.Activation object at 0x7f74941d3d10>\n",
      "123 <keras.layers.convolutional.Conv2D object at 0x7f74941f4dd0>\n",
      "124 <keras.layers.normalization.BatchNormalization object at 0x7f7494210410>\n",
      "125 <keras.layers.core.Activation object at 0x7f74941adc10>\n",
      "126 <keras.layers.convolutional.Conv2D object at 0x7f7494198c10>\n",
      "127 <keras.layers.normalization.BatchNormalization object at 0x7f74941c7990>\n",
      "128 <keras.layers.core.Activation object at 0x7f74941b1f50>\n",
      "129 <keras.layers.convolutional.Conv2D object at 0x7f74941c7f10>\n",
      "130 <keras.layers.normalization.BatchNormalization object at 0x7f749416a6d0>\n",
      "131 <keras.layers.merge.Add object at 0x7f749416a790>\n",
      "132 <keras.layers.core.Activation object at 0x7f7494186050>\n",
      "133 <keras.layers.convolutional.Conv2D object at 0x7f7494122cd0>\n",
      "134 <keras.layers.normalization.BatchNormalization object at 0x7f749413f5d0>\n",
      "135 <keras.layers.core.Activation object at 0x7f749413ffd0>\n",
      "136 <keras.layers.convolutional.Conv2D object at 0x7f7494145410>\n",
      "137 <keras.layers.normalization.BatchNormalization object at 0x7f74940f7d10>\n",
      "138 <keras.layers.core.Activation object at 0x7f74940fcd50>\n",
      "139 <keras.layers.convolutional.Conv2D object at 0x7f74940a0110>\n",
      "140 <keras.layers.normalization.BatchNormalization object at 0x7f74940998d0>\n",
      "141 <keras.layers.merge.Add object at 0x7f74940bcd10>\n",
      "142 <keras.layers.core.Activation object at 0x7f74940b53d0>\n",
      "143 <keras.layers.convolutional.Conv2D object at 0x7f74940573d0>\n",
      "144 <keras.layers.normalization.BatchNormalization object at 0x7f749406c8d0>\n",
      "145 <keras.layers.core.Activation object at 0x7f749406c810>\n",
      "146 <keras.layers.convolutional.Conv2D object at 0x7f74940736d0>\n",
      "147 <keras.layers.normalization.BatchNormalization object at 0x7f7494026fd0>\n",
      "148 <keras.layers.core.Activation object at 0x7f7494026a90>\n",
      "149 <keras.layers.convolutional.Conv2D object at 0x7f7494045fd0>\n",
      "150 <keras.layers.convolutional.Conv2D object at 0x7f74647cc090>\n",
      "151 <keras.layers.normalization.BatchNormalization object at 0x7f74647cc210>\n",
      "152 <keras.layers.normalization.BatchNormalization object at 0x7f74647eb390>\n",
      "153 <keras.layers.merge.Add object at 0x7f7464789a50>\n",
      "154 <keras.layers.core.Activation object at 0x7f7464783650>\n",
      "155 <keras.layers.convolutional.Conv2D object at 0x7f74647a6910>\n",
      "156 <keras.layers.normalization.BatchNormalization object at 0x7f746473cf50>\n",
      "157 <keras.layers.core.Activation object at 0x7f7464748c50>\n",
      "158 <keras.layers.convolutional.Conv2D object at 0x7f7464764d90>\n",
      "159 <keras.layers.normalization.BatchNormalization object at 0x7f74646fb2d0>\n",
      "160 <keras.layers.core.Activation object at 0x7f7464716e10>\n",
      "161 <keras.layers.convolutional.Conv2D object at 0x7f74646fb7d0>\n",
      "162 <keras.layers.normalization.BatchNormalization object at 0x7f7464734fd0>\n",
      "163 <keras.layers.merge.Add object at 0x7f7464739b90>\n",
      "164 <keras.layers.core.Activation object at 0x7f7464734e10>\n",
      "165 <keras.layers.convolutional.Conv2D object at 0x7f74646d5f90>\n",
      "166 <keras.layers.normalization.BatchNormalization object at 0x7f74646f2890>\n",
      "167 <keras.layers.core.Activation object at 0x7f746468bf90>\n",
      "168 <keras.layers.convolutional.Conv2D object at 0x7f74646f9d50>\n",
      "169 <keras.layers.normalization.BatchNormalization object at 0x7f74646a9d50>\n",
      "170 <keras.layers.core.Activation object at 0x7f7464693590>\n",
      "171 <keras.layers.convolutional.Conv2D object at 0x7f74646a9bd0>\n",
      "172 <keras.layers.normalization.BatchNormalization object at 0x7f7464660fd0>\n",
      "173 <keras.layers.merge.Add object at 0x7f7464660c10>\n",
      "174 <keras.layers.core.Activation object at 0x7f7464667050>\n",
      "175 <keras.layers.pooling.GlobalAveragePooling2D object at 0x7f744d38ae90>\n",
      "176 <keras.layers.core.Dense object at 0x7f744d38ae10>\n",
      "177 <keras.layers.core.Dropout object at 0x7f744d38a3d0>\n",
      "178 <keras.layers.core.Dense object at 0x7f744d38a390>\n"
     ]
    }
   ],
   "source": [
    "for ix in range(len(model_new.layers)):\n",
    "    print(ix,model_new.layers[ix])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          524544      global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 256)          0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 4)            1028        dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 24,113,284\n",
      "Trainable params: 1,581,316\n",
      "Non-trainable params: 22,531,968\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "for ix in range(169):\n",
    "    model_new.layers[ix].trainable=False\n",
    "model_new.compile(loss='categorical_crossentropy',optimizer=adam,metrics=['accuracy'])\n",
    "model_new.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 646 samples, validate on 162 samples\n",
      "Epoch 1/5\n",
      "646/646 [==============================] - 201s 311ms/step - loss: 1.2737 - accuracy: 0.4396 - val_loss: 0.6315 - val_accuracy: 0.8272\n",
      "Epoch 2/5\n",
      "646/646 [==============================] - 177s 273ms/step - loss: 0.6661 - accuracy: 0.7492 - val_loss: 0.3480 - val_accuracy: 0.9012\n",
      "Epoch 3/5\n",
      "646/646 [==============================] - 157s 243ms/step - loss: 0.4066 - accuracy: 0.8777 - val_loss: 0.2544 - val_accuracy: 0.9259\n",
      "Epoch 4/5\n",
      "646/646 [==============================] - 156s 241ms/step - loss: 0.3938 - accuracy: 0.8793 - val_loss: 0.2048 - val_accuracy: 0.9444\n",
      "Epoch 5/5\n",
      "646/646 [==============================] - 155s 239ms/step - loss: 0.2749 - accuracy: 0.9303 - val_loss: 0.1805 - val_accuracy: 0.9383\n"
     ]
    }
   ],
   "source": [
    "hist = model_new.fit(X_train,Y_train,\n",
    "                    shuffle=True,\n",
    "                    batch_size=16,\n",
    "                    epochs=5,\n",
    "                    validation_split=0.20\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_path = 's.jpeg'\n",
    "img = image.load_img(img_path, target_size=(224, 224))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Actual Image\")\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "plt.title(\"Normalised Image\")\n",
    "plt.imshow(x[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model_new.predict(x)\n",
    "# decode the results into a list of tuples (class, description, probability)\n",
    "# (one such list for each sample in the batch)\n",
    "num_to_labels = {0:'cat',1:'dog',2:'horse',3:'human'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decodermlal(preds):\n",
    "    max_class = np.argmax(preds,axis=1)\n",
    "    return [num_to_labels[x] for x in max_class]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: ['horse']\n"
     ]
    }
   ],
   "source": [
    "print('Predicted:', decodermlal(preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.2941737e-04 1.3835172e-03 9.2926246e-01 6.8724632e-02]]\n"
     ]
    }
   ],
   "source": [
    "print(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}