Lab8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 8: Convolutional Neural Network Architectures\n",
    "## Lab 8: Implementation of Inception-v3 Network\n",
    "\n",
    "This chapter presents how to implement Inception-v3 network for image classification. The objective can be accomplished by using the TensorFlow library introduced in the previous chapters, but it takes time because of complex network architecture. The following introduces two methods, namely, Keras Application and TensorFlow Hub for building Inception-v3 network faster and more effectively. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/taipeitechmmslab/MMSLAB-DL/blob/master/Lab8.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Keras Applications\n",
    "\n",
    "Keras Applications is the applications module of Keras library, providing network declarations and pre-trained weights for the popular architectures such as Xception, VGG, ResNet, Inception, and so on. \n",
    "\n",
    "### 1. Create Inception-v3 network with Keras Applications\n",
    "\n",
    "- Input size (default): (299, 299, 3)\n",
    "- Weight (default): imagenet\n",
    "- Output category (default): 1000 categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5\n",
      "96116736/96112376 [==============================] - 8s 0us/step\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.applications.InceptionV3(include_top=True, weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"inception_v3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 149, 149, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 149, 149, 32) 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 149, 149, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 147, 147, 32) 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 147, 147, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 147, 147, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 147, 147, 64) 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 147, 147, 64) 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 147, 147, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 73, 73, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 73, 73, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 73, 73, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 73, 73, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 71, 71, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 71, 71, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 71, 71, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 35, 35, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 35, 35, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 35, 35, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 35, 35, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 35, 35, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 35, 35, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 35, 35, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 35, 35, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 35, 35, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 35, 35, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 35, 35, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 35, 35, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 35, 35, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 35, 35, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 35, 35, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 35, 35, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 35, 35, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 35, 35, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 35, 35, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 35, 35, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 35, 35, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 35, 35, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 35, 35, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 35, 35, 256)  0           activation_5[0][0]               \n",
      "                                                                 activation_7[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 35, 35, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 35, 35, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 35, 35, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 35, 35, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 35, 35, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 35, 35, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 35, 35, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 35, 35, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 35, 35, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 35, 35, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 35, 35, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 35, 35, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 35, 35, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 35, 35, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 35, 35, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 35, 35, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 35, 35, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 35, 35, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 35, 35, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 35, 35, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 35, 35, 288)  0           activation_12[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 35, 35, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 35, 35, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 35, 35, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 35, 35, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 35, 35, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 35, 35, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 35, 35, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 35, 35, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 35, 35, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 35, 35, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 35, 35, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 35, 35, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 35, 35, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 35, 35, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 35, 35, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 35, 35, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 35, 35, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 35, 35, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 35, 35, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 35, 35, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 35, 35, 288)  0           activation_19[0][0]              \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 35, 35, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 35, 35, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 35, 35, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 35, 35, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 35, 35, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 35, 35, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 17, 17, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 17, 17, 96)   82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 17, 17, 384)  1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 17, 17, 96)   288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 17, 17, 384)  0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 17, 17, 96)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 17, 17, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 17, 17, 768)  0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 17, 17, 128)  384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 17, 17, 128)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 17, 17, 128)  114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 17, 17, 128)  384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 17, 17, 128)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 17, 17, 128)  114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 17, 17, 128)  384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 17, 17, 128)  384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 17, 17, 128)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 17, 17, 128)  0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 17, 17, 128)  114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 17, 17, 128)  114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 17, 17, 128)  384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 17, 17, 128)  384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 17, 17, 128)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 17, 17, 128)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 17, 17, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 17, 17, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 17, 17, 192)  172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 17, 17, 192)  172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 17, 17, 192)  576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 17, 17, 192)  576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 17, 17, 192)  576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 17, 17, 192)  576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 17, 17, 192)  0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 17, 17, 192)  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 17, 17, 192)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 17, 17, 192)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 17, 17, 768)  0           activation_30[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 17, 17, 160)  480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 17, 17, 160)  0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 17, 17, 160)  179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 17, 17, 160)  480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 17, 17, 160)  0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 17, 17, 160)  179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 17, 17, 160)  480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 17, 17, 160)  480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 17, 17, 160)  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 17, 17, 160)  0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 17, 17, 160)  179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 17, 17, 160)  179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 17, 17, 160)  480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 17, 17, 160)  480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 17, 17, 160)  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 17, 17, 160)  0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 17, 17, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 17, 17, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 17, 17, 192)  215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 17, 17, 192)  215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 17, 17, 192)  576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 17, 17, 192)  576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 17, 17, 192)  576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 17, 17, 192)  576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 17, 17, 192)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 17, 17, 192)  0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 17, 17, 192)  0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 17, 17, 192)  0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 17, 17, 768)  0           activation_40[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "                                                                 activation_48[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 17, 17, 160)  480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 17, 17, 160)  0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 17, 17, 160)  179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 17, 17, 160)  480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 17, 17, 160)  0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 17, 17, 160)  179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 17, 17, 160)  480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 17, 17, 160)  480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 17, 17, 160)  0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 17, 17, 160)  0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 17, 17, 160)  179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 17, 17, 160)  179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 17, 17, 160)  480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 17, 17, 160)  480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 17, 17, 160)  0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 17, 17, 160)  0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 17, 17, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 17, 17, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 17, 17, 192)  215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 17, 17, 192)  215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 17, 17, 192)  576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 17, 17, 192)  576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 17, 17, 192)  576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 17, 17, 192)  576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 17, 17, 192)  0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 17, 17, 192)  0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 17, 17, 192)  0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 17, 17, 192)  0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 17, 17, 768)  0           activation_50[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "                                                                 activation_58[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 17, 17, 192)  576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 17, 17, 192)  0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 17, 17, 192)  258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 17, 17, 192)  576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 17, 17, 192)  0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 17, 17, 192)  258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 17, 17, 192)  576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 17, 17, 192)  576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 17, 17, 192)  0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 17, 17, 192)  0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 17, 17, 192)  258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 17, 17, 192)  258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 17, 17, 192)  576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 17, 17, 192)  576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 17, 17, 192)  0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 17, 17, 192)  0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 17, 17, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 17, 17, 192)  258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 17, 17, 192)  258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 17, 17, 192)  576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 17, 17, 192)  576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 17, 17, 192)  576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 17, 17, 192)  576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 17, 17, 192)  0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 17, 17, 192)  0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 17, 17, 192)  0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 17, 17, 192)  0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 17, 17, 768)  0           activation_60[0][0]              \n",
      "                                                                 activation_63[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 17, 17, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 17, 17, 192)  576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 17, 17, 192)  0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 17, 17, 192)  258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 17, 17, 192)  576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 17, 17, 192)  0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 17, 17, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 17, 17, 192)  258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 17, 17, 192)  576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 17, 17, 192)  576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 17, 17, 192)  0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 17, 17, 192)  0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 8, 8, 320)    552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 8, 8, 192)    331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 8, 8, 320)    960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 8, 8, 192)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 8, 8, 320)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 8, 8, 192)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 8, 8, 1280)   0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 8, 8, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 8, 8, 448)    1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 8, 8, 448)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 8, 8, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 8, 8, 384)    1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 8, 8, 384)    1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 8, 8, 384)    1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 8, 8, 384)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 8, 8, 384)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 8, 8, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 8, 8, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 8, 8, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 8, 8, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 8, 8, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 8, 8, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 8, 8, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 8, 8, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 8, 8, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 8, 8, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 8, 8, 192)    245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 8, 8, 320)    960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 8, 8, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 8, 8, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 8, 8, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 8, 8, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 8, 8, 192)    576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 8, 8, 320)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 8, 8, 768)    0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 8, 8, 768)    0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 8, 8, 192)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 8, 8, 2048)   0           activation_76[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 8, 8, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 8, 8, 448)    1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 8, 8, 448)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 8, 8, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 8, 8, 384)    1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 8, 8, 384)    1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 8, 8, 384)    1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 8, 8, 384)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 8, 8, 384)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 8, 8, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 8, 8, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 8, 8, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 8, 8, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 8, 8, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 8, 8, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 8, 8, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 8, 8, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 8, 8, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 8, 8, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 8, 8, 192)    393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 8, 8, 320)    960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 8, 8, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 8, 8, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 8, 8, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 8, 8, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 8, 8, 192)    576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 8, 8, 320)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 8, 8, 768)    0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 8, 8, 768)    0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 8, 8, 192)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 8, 8, 2048)   0           activation_85[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 23,851,784\n",
      "Trainable params: 23,817,352\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# show Inception_v3 architecture\n",
    "model.summary()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "•\tWrite the Inception-v3 network for displaying on TensorBoard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_tb = tf.keras.callbacks.TensorBoard(log_dir='lab8-logs-inceptionv3-keras')\n",
    "model_tb.set_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    }
   ],
   "source": [
    "# Open TensorBoard directly on the jupyter notebook\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 9600 (pid 7172), started 0:01:28 ago. (Use '!kill 7172' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-632e34c6b7ec64f4\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-632e34c6b7ec64f4\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 9600;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Open log file on TensorBoard\n",
    "%tensorboard --port 9600 --logdir lab8-logs-inceptionv3-keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Making prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a)\tData processing\n",
    "\n",
    "For each network model, Keras Applications provides the corresponding functions for processing data.\n",
    "\n",
    "-\tpreprocess_input: For processing input data following the format the network model requires. The input format of each model can be different. For example, some models require input image with the values range from -1 to +1, others use the value range from 0 to 1, and so on. \n",
    "\n",
    "-\tdecode_predictions: For decoding the predictions of the pre-trained models and mapping the prediction values to the actual class names.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "•\tImport data pre-processing and decoding functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.inception_v3 import preprocess_input\n",
    "from tensorflow.keras.applications.inception_v3 import decode_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b)\tReading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "•\tCreate a function for reading data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_img(img_path, resize=(299,299)):\n",
    "    img_string = tf.io.read_file(img_path)  # Read file\n",
    "    img_decode = tf.image.decode_image(img_string)  # Decode files in image format\n",
    "    img_decode = tf.image.resize(img_decode, resize)  # Resize the image to the network input size\n",
    "    # increase the dimension of the image to 4 (batch, height, width, channels)\n",
    "    img_decode = tf.expand_dims(img_decode, axis=0)\n",
    "    return img_decode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "•\tRead an image by using “read_img” funtion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17a5d7a6a60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "img_path = 'image/elephant.jpg'\n",
    "img = read_img(img_path)  # Read the image through the function just created\n",
    "plt.imshow(tf.cast(img, tf.uint8)[0])  # Display the image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c)\tUsing pre-trained model for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n",
      "40960/35363 [==================================] - 0s 0us/step\n",
      "Predicted: [('n02504458', 'African_elephant', 0.8037862), ('n01871265', 'tusker', 0.12163912), ('n02504013', 'Indian_elephant', 0.004299273)]\n"
     ]
    }
   ],
   "source": [
    "img = preprocess_input(img)  # Image pre-processing\n",
    "preds = model.predict(img)  # image prediction\n",
    "print(\"Predicted:\", decode_predictions(preds, top=3)[0])  # Show the three categories with the highest output prediction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## TensorFlow Hub\n",
    "\n",
    "TensorFlow Hub is a repository where the trained machine learning models are available for downloading and reusing with a minimum amount of code. There are various pre-trained models are provided through the “tfhub.dev” repository such as image classification models, text embedding models. The official website of TensorFlow Hub is https://tfhub.dev/\n",
    "\n",
    "Install:\n",
    "```\n",
    "pip install tensorflow-hub\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 1. Create Inception-v3 network with TensorFlow Hub\n",
    "\n",
    "Model:\n",
    "https://tfhub.dev/google/tf2-preview/inception_v3/classification/2\n",
    "\n",
    "num_classes = 1001 classes of the classification from the original training\n",
    " \n",
    "Image:height x width = 299 x 299 pixels, 3 RGB color values in the range 0~1\n",
    "\n",
    "labels file: https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "a) Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# URL of the pre-trained Inception-v3 model\n",
    "module_url = \"https://tfhub.dev/google/tf2-preview/inception_v3/classification/4\"\n",
    "\n",
    "# Buiding Inception-v3\n",
    "model = tf.keras.Sequential([\n",
    "    # hub.KerasLayer wraps the saved Inception-v3 model as a Keras layer\n",
    "    hub.KerasLayer(module_url, \n",
    "                  input_shape=(299, 299, 3),  \n",
    "                   output_shape=(1001, ),   \n",
    "                   name='Inception_v3')  \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Inception_v3 (KerasLayer)    (None, 1001)              23853833  \n",
      "=================================================================\n",
      "Total params: 23,853,833\n",
      "Trainable params: 0\n",
      "Non-trainable params: 23,853,833\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Show the architecuture \n",
    "model.summary() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.\tMaking prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a)\tReading data\n",
    "\n",
    "•\tCreate a function for reading data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_img(img_path, resize=(299,299)):\n",
    "    img_string = tf.io.read_file(img_path)  # Read file\n",
    "    img_decode = tf.image.decode_image(img_string)    # Decode files in image format\n",
    "    img_decode = tf.image.resize(img_decode, resize)  # Resize the image to the Inception-v3 input size\n",
    "    img_decode = img_decode / 255.0  # Normalization\n",
    "    # # expand dimension \n",
    "    img_decode = tf.expand_dims(img_decode, axis=0)  \n",
    "    return img_decode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "•\tReading the actual class names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt\n",
      "16384/10484 [==============================================] - 0s 0us/step\n",
      "['background', 'tench', 'goldfish', 'great white shark', 'tiger shark', 'hammerhead', 'electric ray', 'stingray', 'cock', 'hen', 'ostrich', 'brambling', 'goldfinch', 'house finch', 'junco', 'indigo bunting', 'robin', 'bulbul', 'jay', 'magpie', 'chickadee', 'water ouzel', 'kite', 'bald eagle', 'vulture', 'great grey owl', 'European fire salamander', 'common newt', 'eft', 'spotted salamander', 'axolotl', 'bullfrog', 'tree frog', 'tailed frog', 'loggerhead', 'leatherback turtle', 'mud turtle', 'terrapin', 'box turtle', 'banded gecko', 'common iguana', 'American chameleon', 'whiptail', 'agama', 'frilled lizard', 'alligator lizard', 'Gila monster', 'green lizard', 'African chameleon', 'Komodo dragon', 'African crocodile', 'American alligator', 'triceratops', 'thunder snake', 'ringneck snake', 'hognose snake', 'green snake', 'king snake', 'garter snake', 'water snake', 'vine snake', 'night snake', 'boa constrictor', 'rock python', 'Indian cobra', 'green mamba', 'sea snake', 'horned viper', 'diamondback', 'sidewinder', 'trilobite', 'harvestman', 'scorpion', 'black and gold garden spider', 'barn spider', 'garden spider', 'black widow', 'tarantula', 'wolf spider', 'tick', 'centipede', 'black grouse', 'ptarmigan', 'ruffed grouse', 'prairie chicken', 'peacock', 'quail', 'partridge', 'African grey', 'macaw', 'sulphur-crested cockatoo', 'lorikeet', 'coucal', 'bee eater', 'hornbill', 'hummingbird', 'jacamar', 'toucan', 'drake', 'red-breasted merganser', 'goose', 'black swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala', 'wombat', 'jellyfish', 'sea anemone', 'brain coral', 'flatworm', 'nematode', 'conch', 'snail', 'slug', 'sea slug', 'chiton', 'chambered nautilus', 'Dungeness crab', 'rock crab', 'fiddler crab', 'king crab', 'American lobster', 'spiny lobster', 'crayfish', 'hermit crab', 'isopod', 'white stork', 'black stork', 'spoonbill', 'flamingo', 'little blue heron', 'American egret', 'bittern', 'crane', 'limpkin', 'European gallinule', 'American coot', 'bustard', 'ruddy turnstone', 'red-backed sandpiper', 'redshank', 'dowitcher', 'oystercatcher', 'pelican', 'king penguin', 'albatross', 'grey whale', 'killer whale', 'dugong', 'sea lion', 'Chihuahua', 'Japanese spaniel', 'Maltese dog', 'Pekinese', 'Shih-Tzu', 'Blenheim spaniel', 'papillon', 'toy terrier', 'Rhodesian ridgeback', 'Afghan hound', 'basset', 'beagle', 'bloodhound', 'bluetick', 'black-and-tan coonhound', 'Walker hound', 'English foxhound', 'redbone', 'borzoi', 'Irish wolfhound', 'Italian greyhound', 'whippet', 'Ibizan hound', 'Norwegian elkhound', 'otterhound', 'Saluki', 'Scottish deerhound', 'Weimaraner', 'Staffordshire bullterrier', 'American Staffordshire terrier', 'Bedlington terrier', 'Border terrier', 'Kerry blue terrier', 'Irish terrier', 'Norfolk terrier', 'Norwich terrier', 'Yorkshire terrier', 'wire-haired fox terrier', 'Lakeland terrier', 'Sealyham terrier', 'Airedale', 'cairn', 'Australian terrier', 'Dandie Dinmont', 'Boston bull', 'miniature schnauzer', 'giant schnauzer', 'standard schnauzer', 'Scotch terrier', 'Tibetan terrier', 'silky terrier', 'soft-coated wheaten terrier', 'West Highland white terrier', 'Lhasa', 'flat-coated retriever', 'curly-coated retriever', 'golden retriever', 'Labrador retriever', 'Chesapeake Bay retriever', 'German short-haired pointer', 'vizsla', 'English setter', 'Irish setter', 'Gordon setter', 'Brittany spaniel', 'clumber', 'English springer', 'Welsh springer spaniel', 'cocker spaniel', 'Sussex spaniel', 'Irish water spaniel', 'kuvasz', 'schipperke', 'groenendael', 'malinois', 'briard', 'kelpie', 'komondor', 'Old English sheepdog', 'Shetland sheepdog', 'collie', 'Border collie', 'Bouvier des Flandres', 'Rottweiler', 'German shepherd', 'Doberman', 'miniature pinscher', 'Greater Swiss Mountain dog', 'Bernese mountain dog', 'Appenzeller', 'EntleBucher', 'boxer', 'bull mastiff', 'Tibetan mastiff', 'French bulldog', 'Great Dane', 'Saint Bernard', 'Eskimo dog', 'malamute', 'Siberian husky', 'dalmatian', 'affenpinscher', 'basenji', 'pug', 'Leonberg', 'Newfoundland', 'Great Pyrenees', 'Samoyed', 'Pomeranian', 'chow', 'keeshond', 'Brabancon griffon', 'Pembroke', 'Cardigan', 'toy poodle', 'miniature poodle', 'standard poodle', 'Mexican hairless', 'timber wolf', 'white wolf', 'red wolf', 'coyote', 'dingo', 'dhole', 'African hunting dog', 'hyena', 'red fox', 'kit fox', 'Arctic fox', 'grey fox', 'tabby', 'tiger cat', 'Persian cat', 'Siamese cat', 'Egyptian cat', 'cougar', 'lynx', 'leopard', 'snow leopard', 'jaguar', 'lion', 'tiger', 'cheetah', 'brown bear', 'American black bear', 'ice bear', 'sloth bear', 'mongoose', 'meerkat', 'tiger beetle', 'ladybug', 'ground beetle', 'long-horned beetle', 'leaf beetle', 'dung beetle', 'rhinoceros beetle', 'weevil', 'fly', 'bee', 'ant', 'grasshopper', 'cricket', 'walking stick', 'cockroach', 'mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly', 'damselfly', 'admiral', 'ringlet', 'monarch', 'cabbage butterfly', 'sulphur butterfly', 'lycaenid', 'starfish', 'sea urchin', 'sea cucumber', 'wood rabbit', 'hare', 'Angora', 'hamster', 'porcupine', 'fox squirrel', 'marmot', 'beaver', 'guinea pig', 'sorrel', 'zebra', 'hog', 'wild boar', 'warthog', 'hippopotamus', 'ox', 'water buffalo', 'bison', 'ram', 'bighorn', 'ibex', 'hartebeest', 'impala', 'gazelle', 'Arabian camel', 'llama', 'weasel', 'mink', 'polecat', 'black-footed ferret', 'otter', 'skunk', 'badger', 'armadillo', 'three-toed sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon', 'siamang', 'guenon', 'patas', 'baboon', 'macaque', 'langur', 'colobus', 'proboscis monkey', 'marmoset', 'capuchin', 'howler monkey', 'titi', 'spider monkey', 'squirrel monkey', 'Madagascar cat', 'indri', 'Indian elephant', 'African elephant', 'lesser panda', 'giant panda', 'barracouta', 'eel', 'coho', 'rock beauty', 'anemone fish', 'sturgeon', 'gar', 'lionfish', 'puffer', 'abacus', 'abaya', 'academic gown', 'accordion', 'acoustic guitar', 'aircraft carrier', 'airliner', 'airship', 'altar', 'ambulance', 'amphibian', 'analog clock', 'apiary', 'apron', 'ashcan', 'assault rifle', 'backpack', 'bakery', 'balance beam', 'balloon', 'ballpoint', 'Band Aid', 'banjo', 'bannister', 'barbell', 'barber chair', 'barbershop', 'barn', 'barometer', 'barrel', 'barrow', 'baseball', 'basketball', 'bassinet', 'bassoon', 'bathing cap', 'bath towel', 'bathtub', 'beach wagon', 'beacon', 'beaker', 'bearskin', 'beer bottle', 'beer glass', 'bell cote', 'bib', 'bicycle-built-for-two', 'bikini', 'binder', 'binoculars', 'birdhouse', 'boathouse', 'bobsled', 'bolo tie', 'bonnet', 'bookcase', 'bookshop', 'bottlecap', 'bow', 'bow tie', 'brass', 'brassiere', 'breakwater', 'breastplate', 'broom', 'bucket', 'buckle', 'bulletproof vest', 'bullet train', 'butcher shop', 'cab', 'caldron', 'candle', 'cannon', 'canoe', 'can opener', 'cardigan', 'car mirror', 'carousel', \"carpenter's kit\", 'carton', 'car wheel', 'cash machine', 'cassette', 'cassette player', 'castle', 'catamaran', 'CD player', 'cello', 'cellular telephone', 'chain', 'chainlink fence', 'chain mail', 'chain saw', 'chest', 'chiffonier', 'chime', 'china cabinet', 'Christmas stocking', 'church', 'cinema', 'cleaver', 'cliff dwelling', 'cloak', 'clog', 'cocktail shaker', 'coffee mug', 'coffeepot', 'coil', 'combination lock', 'computer keyboard', 'confectionery', 'container ship', 'convertible', 'corkscrew', 'cornet', 'cowboy boot', 'cowboy hat', 'cradle', 'crane', 'crash helmet', 'crate', 'crib', 'Crock Pot', 'croquet ball', 'crutch', 'cuirass', 'dam', 'desk', 'desktop computer', 'dial telephone', 'diaper', 'digital clock', 'digital watch', 'dining table', 'dishrag', 'dishwasher', 'disk brake', 'dock', 'dogsled', 'dome', 'doormat', 'drilling platform', 'drum', 'drumstick', 'dumbbell', 'Dutch oven', 'electric fan', 'electric guitar', 'electric locomotive', 'entertainment center', 'envelope', 'espresso maker', 'face powder', 'feather boa', 'file', 'fireboat', 'fire engine', 'fire screen', 'flagpole', 'flute', 'folding chair', 'football helmet', 'forklift', 'fountain', 'fountain pen', 'four-poster', 'freight car', 'French horn', 'frying pan', 'fur coat', 'garbage truck', 'gasmask', 'gas pump', 'goblet', 'go-kart', 'golf ball', 'golfcart', 'gondola', 'gong', 'gown', 'grand piano', 'greenhouse', 'grille', 'grocery store', 'guillotine', 'hair slide', 'hair spray', 'half track', 'hammer', 'hamper', 'hand blower', 'hand-held computer', 'handkerchief', 'hard disc', 'harmonica', 'harp', 'harvester', 'hatchet', 'holster', 'home theater', 'honeycomb', 'hook', 'hoopskirt', 'horizontal bar', 'horse cart', 'hourglass', 'iPod', 'iron', \"jack-o'-lantern\", 'jean', 'jeep', 'jersey', 'jigsaw puzzle', 'jinrikisha', 'joystick', 'kimono', 'knee pad', 'knot', 'lab coat', 'ladle', 'lampshade', 'laptop', 'lawn mower', 'lens cap', 'letter opener', 'library', 'lifeboat', 'lighter', 'limousine', 'liner', 'lipstick', 'Loafer', 'lotion', 'loudspeaker', 'loupe', 'lumbermill', 'magnetic compass', 'mailbag', 'mailbox', 'maillot', 'maillot', 'manhole cover', 'maraca', 'marimba', 'mask', 'matchstick', 'maypole', 'maze', 'measuring cup', 'medicine chest', 'megalith', 'microphone', 'microwave', 'military uniform', 'milk can', 'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing bowl', 'mobile home', 'Model T', 'modem', 'monastery', 'monitor', 'moped', 'mortar', 'mortarboard', 'mosque', 'mosquito net', 'motor scooter', 'mountain bike', 'mountain tent', 'mouse', 'mousetrap', 'moving van', 'muzzle', 'nail', 'neck brace', 'necklace', 'nipple', 'notebook', 'obelisk', 'oboe', 'ocarina', 'odometer', 'oil filter', 'organ', 'oscilloscope', 'overskirt', 'oxcart', 'oxygen mask', 'packet', 'paddle', 'paddlewheel', 'padlock', 'paintbrush', 'pajama', 'palace', 'panpipe', 'paper towel', 'parachute', 'parallel bars', 'park bench', 'parking meter', 'passenger car', 'patio', 'pay-phone', 'pedestal', 'pencil box', 'pencil sharpener', 'perfume', 'Petri dish', 'photocopier', 'pick', 'pickelhaube', 'picket fence', 'pickup', 'pier', 'piggy bank', 'pill bottle', 'pillow', 'ping-pong ball', 'pinwheel', 'pirate', 'pitcher', 'plane', 'planetarium', 'plastic bag', 'plate rack', 'plow', 'plunger', 'Polaroid camera', 'pole', 'police van', 'poncho', 'pool table', 'pop bottle', 'pot', \"potter's wheel\", 'power drill', 'prayer rug', 'printer', 'prison', 'projectile', 'projector', 'puck', 'punching bag', 'purse', 'quill', 'quilt', 'racer', 'racket', 'radiator', 'radio', 'radio telescope', 'rain barrel', 'recreational vehicle', 'reel', 'reflex camera', 'refrigerator', 'remote control', 'restaurant', 'revolver', 'rifle', 'rocking chair', 'rotisserie', 'rubber eraser', 'rugby ball', 'rule', 'running shoe', 'safe', 'safety pin', 'saltshaker', 'sandal', 'sarong', 'sax', 'scabbard', 'scale', 'school bus', 'schooner', 'scoreboard', 'screen', 'screw', 'screwdriver', 'seat belt', 'sewing machine', 'shield', 'shoe shop', 'shoji', 'shopping basket', 'shopping cart', 'shovel', 'shower cap', 'shower curtain', 'ski', 'ski mask', 'sleeping bag', 'slide rule', 'sliding door', 'slot', 'snorkel', 'snowmobile', 'snowplow', 'soap dispenser', 'soccer ball', 'sock', 'solar dish', 'sombrero', 'soup bowl', 'space bar', 'space heater', 'space shuttle', 'spatula', 'speedboat', 'spider web', 'spindle', 'sports car', 'spotlight', 'stage', 'steam locomotive', 'steel arch bridge', 'steel drum', 'stethoscope', 'stole', 'stone wall', 'stopwatch', 'stove', 'strainer', 'streetcar', 'stretcher', 'studio couch', 'stupa', 'submarine', 'suit', 'sundial', 'sunglass', 'sunglasses', 'sunscreen', 'suspension bridge', 'swab', 'sweatshirt', 'swimming trunks', 'swing', 'switch', 'syringe', 'table lamp', 'tank', 'tape player', 'teapot', 'teddy', 'television', 'tennis ball', 'thatch', 'theater curtain', 'thimble', 'thresher', 'throne', 'tile roof', 'toaster', 'tobacco shop', 'toilet seat', 'torch', 'totem pole', 'tow truck', 'toyshop', 'tractor', 'trailer truck', 'tray', 'trench coat', 'tricycle', 'trimaran', 'tripod', 'triumphal arch', 'trolleybus', 'trombone', 'tub', 'turnstile', 'typewriter keyboard', 'umbrella', 'unicycle', 'upright', 'vacuum', 'vase', 'vault', 'velvet', 'vending machine', 'vestment', 'viaduct', 'violin', 'volleyball', 'waffle iron', 'wall clock', 'wallet', 'wardrobe', 'warplane', 'washbasin', 'washer', 'water bottle', 'water jug', 'water tower', 'whiskey jug', 'whistle', 'wig', 'window screen', 'window shade', 'Windsor tie', 'wine bottle', 'wing', 'wok', 'wooden spoon', 'wool', 'worm fence', 'wreck', 'yawl', 'yurt', 'web site', 'comic book', 'crossword puzzle', 'street sign', 'traffic light', 'book jacket', 'menu', 'plate', 'guacamole', 'consomme', 'hot pot', 'trifle', 'ice cream', 'ice lolly', 'French loaf', 'bagel', 'pretzel', 'cheeseburger', 'hotdog', 'mashed potato', 'head cabbage', 'broccoli', 'cauliflower', 'zucchini', 'spaghetti squash', 'acorn squash', 'butternut squash', 'cucumber', 'artichoke', 'bell pepper', 'cardoon', 'mushroom', 'Granny Smith', 'strawberry', 'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit', 'custard apple', 'pomegranate', 'hay', 'carbonara', 'chocolate sauce', 'dough', 'meat loaf', 'pizza', 'potpie', 'burrito', 'red wine', 'espresso', 'cup', 'eggnog', 'alp', 'bubble', 'cliff', 'coral reef', 'geyser', 'lakeside', 'promontory', 'sandbar', 'seashore', 'valley', 'volcano', 'ballplayer', 'groom', 'scuba diver', 'rapeseed', 'daisy', \"yellow lady's slipper\", 'corn', 'acorn', 'hip', 'buckeye', 'coral fungus', 'agaric', 'gyromitra', 'stinkhorn', 'earthstar', 'hen-of-the-woods', 'bolete', 'ear', 'toilet tissue']\n"
     ]
    }
   ],
   "source": [
    "# Download ImageNet lable file\n",
    "labels_path = tf.keras.utils.get_file('ImageNetLabels.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')\n",
    "# Reading data \n",
    "with open(labels_path) as file:\n",
    "    lines = file.read().splitlines()\n",
    "print(lines)  \n",
    "\n",
    "imagenet_labels = np.array(lines) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "•\tRead an image by using “read_img” function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x17a5d66e7c0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "img_path = 'image/elephant.jpg'\n",
    "img = read_img(img_path)  \n",
    "plt.imshow(img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b)\tUsing pre-trained model for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: African elephant\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(img)  # image prediction\n",
    "index = np.argmax(preds)  # Get the Index with the largest prediction result\n",
    "print(\"Predicted:\", imagenet_labels[index])  # print the predicted result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c)\tDisplay three best predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: ['African elephant' 'tusker' 'Indian elephant']\n"
     ]
    }
   ],
   "source": [
    "# Get the three indexes with the largest prediction results\n",
    "top3_indexs = np.argsort(preds)[0, ::-1][:3]  \n",
    "print(\"Predicted:\", imagenet_labels[top3_indexs])  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf2",
   "language": "python",
   "name": "tf2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}