Chapter08/Activity8.02/Activity8_02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Activity 8.02\n",
    "Import the required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.resnet50 import preprocess_input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initiate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "fc1000 (Dense)                  (None, 1000)         2049000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 25,636,712\n",
      "Trainable params: 25,583,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "classifier = ResNet50()\n",
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x141F5DBA8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_image= image.load_img('../Data/Prediction/test_image_4.jpg', target_size=(224, 224))\n",
    "new_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the image to array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_image = image.img_to_array(new_image)\n",
    "transformed_image.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expand the tranfromed image with 4th Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 224, 224, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_image = np.expand_dims(transformed_image, axis=0)\n",
    "transformed_image.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preprocess the Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[  33.060997  ,   51.221     ,   59.32      ],\n",
       "         [  29.060997  ,   50.221     ,   59.32      ],\n",
       "         [  25.060997  ,   47.221     ,   54.32      ],\n",
       "         ...,\n",
       "         [  30.060997  ,   50.221     ,   58.32      ],\n",
       "         [  37.060997  ,   55.221     ,   63.32      ],\n",
       "         [  33.060997  ,   51.221     ,   59.32      ]],\n",
       "\n",
       "        [[  28.060997  ,   46.221     ,   54.32      ],\n",
       "         [  30.060997  ,   52.221     ,   59.32      ],\n",
       "         [  27.060997  ,   47.221     ,   55.32      ],\n",
       "         ...,\n",
       "         [  26.060997  ,   48.221     ,   55.32      ],\n",
       "         [  30.060997  ,   52.221     ,   59.32      ],\n",
       "         [  32.060997  ,   52.221     ,   60.32      ]],\n",
       "\n",
       "        [[  28.060997  ,   48.221     ,   56.32      ],\n",
       "         [  29.060997  ,   49.221     ,   57.32      ],\n",
       "         [  31.060997  ,   51.221     ,   59.32      ],\n",
       "         ...,\n",
       "         [  28.060997  ,   48.221     ,   56.32      ],\n",
       "         [  21.060997  ,   39.221     ,   47.32      ],\n",
       "         [  33.060997  ,   50.221     ,   60.32      ]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-103.939     , -116.779     , -123.68      ],\n",
       "         [-103.939     , -116.779     , -123.68      ],\n",
       "         [-103.939     , -116.779     , -123.68      ],\n",
       "         ...,\n",
       "         [ -17.939003  ,    1.2210007 ,   13.32      ],\n",
       "         [  -7.939003  ,    5.2210007 ,   15.32      ],\n",
       "         [ -18.939003  ,    0.22100067,   12.32      ]],\n",
       "\n",
       "        [[-103.939     , -116.779     , -123.68      ],\n",
       "         [-103.939     , -116.779     , -123.68      ],\n",
       "         [-103.939     , -116.779     , -123.68      ],\n",
       "         ...,\n",
       "         [ -15.939003  ,    1.2210007 ,   13.32      ],\n",
       "         [ -25.939003  ,   -8.778999  ,    1.3199997 ],\n",
       "         [ -18.939003  ,    0.22100067,   12.32      ]],\n",
       "\n",
       "        [[-103.939     , -116.779     , -123.68      ],\n",
       "         [-103.939     , -116.779     , -123.68      ],\n",
       "         [-103.939     , -116.779     , -123.68      ],\n",
       "         ...,\n",
       "         [ -15.939003  ,    3.2210007 ,   15.32      ],\n",
       "         [ -17.939003  ,   -0.7789993 ,   11.32      ],\n",
       "         [ -15.939003  ,    3.2210007 ,   15.32      ]]]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_image = preprocess_input(transformed_image)\n",
    "transformed_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a predictor variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.36011115e-11, 6.69067191e-10, 1.04476122e-10, 1.33510564e-10,\n",
       "        6.83042928e-11, 1.76949271e-11, 1.61705635e-10, 3.06478481e-10,\n",
       "        1.08718395e-10, 1.63380059e-10, 2.64175265e-10, 1.29403444e-09,\n",
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       "        8.19412023e-08, 2.04980619e-08, 1.90126404e-09, 7.01114260e-08,\n",
       "        1.87942112e-06, 3.04031715e-08, 2.66845235e-09, 8.50868931e-10,\n",
       "        2.42180448e-07, 9.19648215e-08, 7.04579577e-08, 5.01449549e-10,\n",
       "        8.90177443e-09, 1.35002381e-07, 1.26289294e-07, 1.09688564e-07,\n",
       "        2.33415620e-09, 4.11438896e-06, 2.04126072e-09, 6.35045438e-08,\n",
       "        1.00617875e-07, 7.92688411e-07, 1.55869380e-08, 2.60552802e-10,\n",
       "        4.22873159e-09, 1.96871133e-06, 8.82914537e-06, 2.96427688e-10,\n",
       "        3.55827390e-09, 3.56969432e-09, 4.50744109e-09, 8.53989550e-05,\n",
       "        1.47406922e-06, 1.92023253e-06, 2.10934402e-07, 2.22090222e-07,\n",
       "        2.46105802e-08, 7.43362989e-08, 4.38596937e-09, 8.48379003e-08,\n",
       "        1.17773968e-09, 3.22954996e-08, 1.27140947e-08, 7.54278008e-05,\n",
       "        1.45407100e-08, 8.04626438e-07, 1.87469240e-09, 1.20210064e-09,\n",
       "        4.15400958e-09, 2.86207258e-09, 1.03225659e-06, 3.36986994e-09,\n",
       "        1.60307129e-10, 2.67604161e-09, 9.51111433e-04, 3.47072771e-08,\n",
       "        5.32027514e-07, 1.08139488e-07, 2.63340553e-07, 2.78010859e-08,\n",
       "        9.30811250e-09, 9.29080599e-08, 7.04725789e-09, 1.49646795e-07,\n",
       "        1.54821080e-08, 1.92014262e-08, 2.42989575e-08, 2.27798905e-10,\n",
       "        7.74843190e-10, 3.95178112e-09, 2.20075435e-07, 4.79526875e-07,\n",
       "        1.35343626e-05, 9.19815668e-10, 5.43094285e-11, 4.12624557e-09,\n",
       "        4.39469943e-07, 6.99631109e-09, 3.31548078e-09, 7.54624008e-09,\n",
       "        3.10386135e-08, 1.41977730e-09, 3.26543805e-08, 9.05575632e-07,\n",
       "        3.39560546e-10, 7.04694836e-09, 4.86849831e-11, 2.51256616e-09,\n",
       "        6.70889108e-11, 3.13139015e-10, 4.36337177e-06, 3.72311915e-09,\n",
       "        5.36247491e-10, 2.25526639e-10, 1.02777775e-09, 8.00857606e-07,\n",
       "        1.07292575e-08, 6.54626187e-10, 5.76191724e-07, 2.52924747e-06,\n",
       "        8.41237213e-09, 4.26264783e-08, 7.43176942e-09, 2.71522708e-07,\n",
       "        1.63917335e-09, 1.81549151e-11, 4.98292252e-08, 1.10962430e-08,\n",
       "        4.63379060e-08, 2.93876674e-08, 1.06466755e-08, 5.05507014e-09,\n",
       "        2.25557088e-07, 2.58983301e-09, 2.07062270e-10, 1.72309922e-09,\n",
       "        9.82982456e-04, 5.32555475e-07, 2.47113003e-05, 1.16352303e-10,\n",
       "        7.60081139e-06, 3.07210968e-08, 9.88233229e-09, 9.96738732e-01,\n",
       "        6.53187271e-07, 2.87727242e-09, 1.20725963e-08, 8.16002625e-08,\n",
       "        1.21416766e-10, 3.16216074e-07, 8.85474893e-09, 9.20965192e-07,\n",
       "        2.88878841e-08, 3.00596412e-07, 7.25807137e-10, 4.83223572e-10,\n",
       "        7.93696331e-10, 1.30551003e-09, 3.89632904e-09, 6.60112964e-10,\n",
       "        2.90634694e-06, 2.60585864e-08, 1.16382992e-09, 2.20217768e-11,\n",
       "        3.44003041e-07, 1.53030988e-09, 4.06821694e-11, 2.44098253e-09,\n",
       "        2.02968963e-07, 2.44594265e-08, 9.52701811e-08, 2.58062318e-08,\n",
       "        8.23091106e-09, 2.62377080e-06, 3.14238008e-07, 1.13007381e-07,\n",
       "        1.84021918e-08, 1.87974898e-08, 4.93608887e-08, 3.33607120e-09,\n",
       "        9.38345734e-10, 4.54598315e-09, 2.67640243e-10, 8.11064726e-09,\n",
       "        7.58398746e-05, 5.34348771e-07, 8.66278015e-07, 1.48599223e-11,\n",
       "        4.75769781e-07, 3.70432360e-07, 6.02480767e-08, 2.15659657e-06,\n",
       "        2.58170485e-09, 1.14400557e-07, 3.94166584e-08, 1.99678478e-08,\n",
       "        3.97309987e-06, 1.00628822e-05, 5.02634989e-10, 1.69365393e-08,\n",
       "        1.74013481e-09, 8.30772251e-09, 5.42499556e-09, 4.05094625e-09,\n",
       "        4.45445819e-10, 6.46305176e-10, 4.16632076e-11, 1.41889211e-09,\n",
       "        7.86988963e-08, 2.37230258e-09, 3.57572505e-09, 1.39069741e-07,\n",
       "        2.56377945e-07, 1.03727515e-07, 3.08129211e-09, 3.72430486e-09,\n",
       "        4.80521400e-10, 3.51490587e-10, 5.10459730e-10, 2.62168925e-10,\n",
       "        9.14441856e-10, 5.00425523e-09, 1.42130552e-09, 1.11450860e-09,\n",
       "        4.24849000e-09, 1.11788467e-10, 2.76146633e-10, 4.22866675e-09,\n",
       "        1.18150218e-08, 1.00027653e-09, 1.16236851e-08, 1.56073676e-09,\n",
       "        2.26602559e-09, 1.56041813e-09, 8.65753080e-10, 3.31331229e-09,\n",
       "        5.54959012e-09, 9.49553325e-10, 8.23363877e-10, 1.98144778e-10,\n",
       "        1.82191627e-07, 1.35079525e-09, 7.58643282e-09, 9.10580695e-08,\n",
       "        8.59977978e-10, 2.39721487e-09, 4.08292866e-09, 2.25456098e-10,\n",
       "        2.42262294e-10, 1.04618347e-09, 7.12775838e-09, 1.04147091e-09,\n",
       "        1.19636845e-09, 2.33740511e-08, 1.26198456e-08, 2.59008970e-09,\n",
       "        4.23000412e-10, 5.01864772e-10, 1.01318625e-08, 7.26457072e-09,\n",
       "        1.14907358e-07, 9.26597199e-09, 2.77169787e-10, 1.03904732e-07,\n",
       "        2.76758283e-10, 9.85591261e-11, 3.91932875e-10, 2.63931121e-09,\n",
       "        1.67637126e-10, 1.31222067e-09, 2.47631271e-09, 5.55049728e-10,\n",
       "        1.39760836e-09, 2.13440848e-10, 7.09607448e-08, 1.75427817e-09,\n",
       "        1.39455947e-09, 3.82675641e-10, 1.09756204e-10, 6.67084787e-09,\n",
       "        5.63795899e-10, 1.28266869e-10, 3.69125992e-08, 1.54148222e-10,\n",
       "        6.71620190e-11, 6.58279117e-12, 5.76570389e-11, 4.77755058e-10,\n",
       "        2.40758195e-11, 2.91171476e-10, 1.79200876e-10, 2.01149214e-06]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(transformed_image)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the shape of the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('n04404412', 'television', 0.99673873),\n",
       "  ('n04372370', 'switch', 0.0009829825),\n",
       "  ('n04152593', 'screen', 0.00095111143),\n",
       "  ('n03782006', 'monitor', 0.0006477369),\n",
       "  ('n04069434', 'reflex_camera', 8.5398955e-05)]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.applications.resnet50 import decode_predictions\n",
    "decode_predictions(y_pred, top=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the predictions in readable form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "television (99.67%)\n"
     ]
    }
   ],
   "source": [
    "label = decode_predictions(y_pred)\n",
    "# retrieve the most likely result, i.e. highest probability\n",
    "decoded_label = label[0][0]\n",
    "# print the classification\n",
    "print('%s (%.2f%%)' % (decoded_label[1], decoded_label[2]*100 ))"
   ]
  }
 ],
 "metadata": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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