image_classification_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Classification with CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# miscellaneous\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.9.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras_tuner as kt\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Getting images data into keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['book', 'phone', 'fork', 'mug']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this are the folder names\n",
    "classes = os.listdir('./train_images')\n",
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define an image data generator\n",
    "data_gen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    # define the preprocessing function that should be applied to all images\n",
    "    preprocessing_function=keras.applications.mobilenet_v2.preprocess_input,\n",
    "    \n",
    "# rotation_range=20,\n",
    "    # width_shift_range=0.2,\n",
    "    # height_shift_range=0.2,\n",
    "    # horizontal_flip=True, \n",
    "    # zoom_range=0.2,\n",
    "   \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 480 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "# a generator that returns batches of X and y arrays\n",
    "train_data_gen = data_gen.flow_from_directory(\n",
    "        directory='./train_images',\n",
    "        class_mode=\"categorical\",\n",
    "        classes=classes,\n",
    "        batch_size=480,\n",
    "        target_size=(224, 224)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((480, 224, 224, 3), (480, 4))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in all images at once\n",
    "xtrain, ytrain = next(train_data_gen)\n",
    "xtrain.shape, ytrain.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Model Building "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Select the convolutional base and freeze the weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = keras.applications.mobilenet_v2.MobileNetV2(\n",
    "    weights='imagenet', \n",
    "    alpha=1.0,         # specific parameter of this model, small alpha reduces the number of overall weights\n",
    "    pooling='avg',      # applies global average pooling to the output of the last conv layer (like a flattening)\n",
    "    include_top=False,  # we only want to have the base, not the final dense layers \n",
    "    input_shape=(224, 224, 3)\n",
    ")\n",
    "\n",
    "# freeze it!\n",
    "base_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"mobilenetv2_1.00_224\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " Conv1 (Conv2D)                 (None, 112, 112, 32  864         ['input_1[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " bn_Conv1 (BatchNormalization)  (None, 112, 112, 32  128         ['Conv1[0][0]']                  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " Conv1_relu (ReLU)              (None, 112, 112, 32  0           ['bn_Conv1[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " expanded_conv_depthwise (Depth  (None, 112, 112, 32  288        ['Conv1_relu[0][0]']             \n",
      " wiseConv2D)                    )                                                                 \n",
      "                                                                                                  \n",
      " expanded_conv_depthwise_BN (Ba  (None, 112, 112, 32  128        ['expanded_conv_depthwise[0][0]']\n",
      " tchNormalization)              )                                                                 \n",
      "                                                                                                  \n",
      " expanded_conv_depthwise_relu (  (None, 112, 112, 32  0          ['expanded_conv_depthwise_BN[0][0\n",
      " ReLU)                          )                                ]']                              \n",
      "                                                                                                  \n",
      " expanded_conv_project (Conv2D)  (None, 112, 112, 16  512        ['expanded_conv_depthwise_relu[0]\n",
      "                                )                                [0]']                            \n",
      "                                                                                                  \n",
      " expanded_conv_project_BN (Batc  (None, 112, 112, 16  64         ['expanded_conv_project[0][0]']  \n",
      " hNormalization)                )                                                                 \n",
      "                                                                                                  \n",
      " block_1_expand (Conv2D)        (None, 112, 112, 96  1536        ['expanded_conv_project_BN[0][0]'\n",
      "                                )                                ]                                \n",
      "                                                                                                  \n",
      " block_1_expand_BN (BatchNormal  (None, 112, 112, 96  384        ['block_1_expand[0][0]']         \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " block_1_expand_relu (ReLU)     (None, 112, 112, 96  0           ['block_1_expand_BN[0][0]']      \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block_1_pad (ZeroPadding2D)    (None, 113, 113, 96  0           ['block_1_expand_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " block_1_depthwise (DepthwiseCo  (None, 56, 56, 96)  864         ['block_1_pad[0][0]']            \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_1_depthwise_BN (BatchNor  (None, 56, 56, 96)  384         ['block_1_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_1_depthwise_relu (ReLU)  (None, 56, 56, 96)   0           ['block_1_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_1_project (Conv2D)       (None, 56, 56, 24)   2304        ['block_1_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_1_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_1_project[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_2_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_1_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_2_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_2_expand[0][0]']         \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block_2_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_2_expand_BN[0][0]']      \n",
      "                                                                                                  \n",
      " block_2_depthwise (DepthwiseCo  (None, 56, 56, 144)  1296       ['block_2_expand_relu[0][0]']    \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_2_depthwise_BN (BatchNor  (None, 56, 56, 144)  576        ['block_2_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_2_depthwise_relu (ReLU)  (None, 56, 56, 144)  0           ['block_2_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_2_project (Conv2D)       (None, 56, 56, 24)   3456        ['block_2_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_2_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_2_project[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_2_add (Add)              (None, 56, 56, 24)   0           ['block_1_project_BN[0][0]',     \n",
      "                                                                  'block_2_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_3_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_2_add[0][0]']            \n",
      "                                                                                                  \n",
      " block_3_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_3_expand[0][0]']         \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block_3_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_3_expand_BN[0][0]']      \n",
      "                                                                                                  \n",
      " block_3_pad (ZeroPadding2D)    (None, 57, 57, 144)  0           ['block_3_expand_relu[0][0]']    \n",
      "                                                                                                  \n",
      " block_3_depthwise (DepthwiseCo  (None, 28, 28, 144)  1296       ['block_3_pad[0][0]']            \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_3_depthwise_BN (BatchNor  (None, 28, 28, 144)  576        ['block_3_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_3_depthwise_relu (ReLU)  (None, 28, 28, 144)  0           ['block_3_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_3_project (Conv2D)       (None, 28, 28, 32)   4608        ['block_3_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_3_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_3_project[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_4_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_3_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_4_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_4_expand[0][0]']         \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block_4_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_4_expand_BN[0][0]']      \n",
      "                                                                                                  \n",
      " block_4_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_4_expand_relu[0][0]']    \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_4_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_4_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_4_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_4_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_4_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_4_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_4_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_4_project[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_4_add (Add)              (None, 28, 28, 32)   0           ['block_3_project_BN[0][0]',     \n",
      "                                                                  'block_4_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_5_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_4_add[0][0]']            \n",
      "                                                                                                  \n",
      " block_5_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_5_expand[0][0]']         \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block_5_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_5_expand_BN[0][0]']      \n",
      "                                                                                                  \n",
      " block_5_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_5_expand_relu[0][0]']    \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_5_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_5_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_5_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_5_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_5_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_5_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_5_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_5_project[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_5_add (Add)              (None, 28, 28, 32)   0           ['block_4_add[0][0]',            \n",
      "                                                                  'block_5_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_6_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_5_add[0][0]']            \n",
      "                                                                                                  \n",
      " block_6_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_6_expand[0][0]']         \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block_6_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_6_expand_BN[0][0]']      \n",
      "                                                                                                  \n",
      " block_6_pad (ZeroPadding2D)    (None, 29, 29, 192)  0           ['block_6_expand_relu[0][0]']    \n",
      "                                                                                                  \n",
      " block_6_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_6_pad[0][0]']            \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_6_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_6_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_6_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_6_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_6_project (Conv2D)       (None, 14, 14, 64)   12288       ['block_6_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_6_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_6_project[0][0]']        \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_7_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_6_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_7_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_7_expand[0][0]']         \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block_7_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_7_expand_BN[0][0]']      \n",
      "                                                                                                  \n",
      " block_7_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_7_expand_relu[0][0]']    \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_7_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_7_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_7_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_7_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_7_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_7_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_7_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_7_project[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_7_add (Add)              (None, 14, 14, 64)   0           ['block_6_project_BN[0][0]',     \n",
      "                                                                  'block_7_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_8_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_7_add[0][0]']            \n",
      "                                                                                                  \n",
      " block_8_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_8_expand[0][0]']         \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block_8_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_8_expand_BN[0][0]']      \n",
      "                                                                                                  \n",
      " block_8_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_8_expand_relu[0][0]']    \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_8_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_8_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_8_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_8_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_8_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_8_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_8_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_8_project[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_8_add (Add)              (None, 14, 14, 64)   0           ['block_7_add[0][0]',            \n",
      "                                                                  'block_8_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_9_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_8_add[0][0]']            \n",
      "                                                                                                  \n",
      " block_9_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_9_expand[0][0]']         \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block_9_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_9_expand_BN[0][0]']      \n",
      "                                                                                                  \n",
      " block_9_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_9_expand_relu[0][0]']    \n",
      " nv2D)                                                                                            \n",
      "                                                                                                  \n",
      " block_9_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_9_depthwise[0][0]']      \n",
      " malization)                                                                                      \n",
      "                                                                                                  \n",
      " block_9_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_9_depthwise_BN[0][0]']   \n",
      "                                                                                                  \n",
      " block_9_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_9_depthwise_relu[0][0]'] \n",
      "                                                                                                  \n",
      " block_9_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_9_project[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_9_add (Add)              (None, 14, 14, 64)   0           ['block_8_add[0][0]',            \n",
      "                                                                  'block_9_project_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_10_expand (Conv2D)       (None, 14, 14, 384)  24576       ['block_9_add[0][0]']            \n",
      "                                                                                                  \n",
      " block_10_expand_BN (BatchNorma  (None, 14, 14, 384)  1536       ['block_10_expand[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_10_expand_relu (ReLU)    (None, 14, 14, 384)  0           ['block_10_expand_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_10_depthwise (DepthwiseC  (None, 14, 14, 384)  3456       ['block_10_expand_relu[0][0]']   \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " block_10_depthwise_BN (BatchNo  (None, 14, 14, 384)  1536       ['block_10_depthwise[0][0]']     \n",
      " rmalization)                                                                                     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0          ['block_10_depthwise_BN[0][0]']  \n",
      "                                                                                                  \n",
      " block_10_project (Conv2D)      (None, 14, 14, 96)   36864       ['block_10_depthwise_relu[0][0]']\n",
      "                                                                                                  \n",
      " block_10_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_10_project[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block_11_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_10_project_BN[0][0]']    \n",
      "                                                                                                  \n",
      " block_11_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_11_expand[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_11_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_11_expand_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_11_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_11_expand_relu[0][0]']   \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " block_11_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_11_depthwise[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_11_depthwise_BN[0][0]']  \n",
      "                                                                                                  \n",
      " block_11_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_11_depthwise_relu[0][0]']\n",
      "                                                                                                  \n",
      " block_11_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_11_project[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block_11_add (Add)             (None, 14, 14, 96)   0           ['block_10_project_BN[0][0]',    \n",
      "                                                                  'block_11_project_BN[0][0]']    \n",
      "                                                                                                  \n",
      " block_12_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_11_add[0][0]']           \n",
      "                                                                                                  \n",
      " block_12_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_12_expand[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_12_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_12_expand_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_12_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_12_expand_relu[0][0]']   \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " block_12_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_12_depthwise[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_12_depthwise_BN[0][0]']  \n",
      "                                                                                                  \n",
      " block_12_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_12_depthwise_relu[0][0]']\n",
      "                                                                                                  \n",
      " block_12_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_12_project[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block_12_add (Add)             (None, 14, 14, 96)   0           ['block_11_add[0][0]',           \n",
      "                                                                  'block_12_project_BN[0][0]']    \n",
      "                                                                                                  \n",
      " block_13_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_12_add[0][0]']           \n",
      "                                                                                                  \n",
      " block_13_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_13_expand[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_13_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_13_expand_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_13_pad (ZeroPadding2D)   (None, 15, 15, 576)  0           ['block_13_expand_relu[0][0]']   \n",
      "                                                                                                  \n",
      " block_13_depthwise (DepthwiseC  (None, 7, 7, 576)   5184        ['block_13_pad[0][0]']           \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " block_13_depthwise_BN (BatchNo  (None, 7, 7, 576)   2304        ['block_13_depthwise[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)   0           ['block_13_depthwise_BN[0][0]']  \n",
      "                                                                                                  \n",
      " block_13_project (Conv2D)      (None, 7, 7, 160)    92160       ['block_13_depthwise_relu[0][0]']\n",
      "                                                                                                  \n",
      " block_13_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_13_project[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block_14_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_13_project_BN[0][0]']    \n",
      "                                                                                                  \n",
      " block_14_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_14_expand[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_14_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_14_expand_BN[0][0]']     \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block_14_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_14_expand_relu[0][0]']   \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " block_14_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_14_depthwise[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_14_depthwise_BN[0][0]']  \n",
      "                                                                                                  \n",
      " block_14_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_14_depthwise_relu[0][0]']\n",
      "                                                                                                  \n",
      " block_14_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_14_project[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block_14_add (Add)             (None, 7, 7, 160)    0           ['block_13_project_BN[0][0]',    \n",
      "                                                                  'block_14_project_BN[0][0]']    \n",
      "                                                                                                  \n",
      " block_15_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_14_add[0][0]']           \n",
      "                                                                                                  \n",
      " block_15_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_15_expand[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_15_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_15_expand_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_15_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_15_expand_relu[0][0]']   \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " block_15_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_15_depthwise[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_15_depthwise_BN[0][0]']  \n",
      "                                                                                                  \n",
      " block_15_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_15_depthwise_relu[0][0]']\n",
      "                                                                                                  \n",
      " block_15_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_15_project[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " block_15_add (Add)             (None, 7, 7, 160)    0           ['block_14_add[0][0]',           \n",
      "                                                                  'block_15_project_BN[0][0]']    \n",
      "                                                                                                  \n",
      " block_16_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_15_add[0][0]']           \n",
      "                                                                                                  \n",
      " block_16_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_16_expand[0][0]']        \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block_16_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_16_expand_BN[0][0]']     \n",
      "                                                                                                  \n",
      " block_16_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_16_expand_relu[0][0]']   \n",
      " onv2D)                                                                                           \n",
      "                                                                                                  \n",
      " block_16_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_16_depthwise[0][0]']     \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_16_depthwise_BN[0][0]']  \n",
      "                                                                                                  \n",
      " block_16_project (Conv2D)      (None, 7, 7, 320)    307200      ['block_16_depthwise_relu[0][0]']\n",
      "                                                                                                  \n",
      " block_16_project_BN (BatchNorm  (None, 7, 7, 320)   1280        ['block_16_project[0][0]']       \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " Conv_1 (Conv2D)                (None, 7, 7, 1280)   409600      ['block_16_project_BN[0][0]']    \n",
      "                                                                                                  \n",
      " Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)  5120        ['Conv_1[0][0]']                 \n",
      "                                                                                                  \n",
      " out_relu (ReLU)                (None, 7, 7, 1280)   0           ['Conv_1_bn[0][0]']              \n",
      "                                                                                                  \n",
      " global_average_pooling2d (Glob  (None, 1280)        0           ['out_relu[0][0]']               \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 2,257,984\n",
      "Trainable params: 0\n",
      "Non-trainable params: 2,257,984\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Add dense layers on top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_builder(hp):\n",
    "    '''\n",
    "    Args:\n",
    "    hp - Keras tuner object\n",
    "    '''\n",
    "    # Initialize the Sequential API and start stacking the layers\n",
    "    model = keras.Sequential()\n",
    "    model.add(base_model)\n",
    "    \n",
    "    # Tune the number of units in the first Dense layer\n",
    "    # Choose an optimal value between 32-160\n",
    "    hp_units = hp.Int('units', min_value=32, max_value=160, step=32)\n",
    "    model.add(keras.layers.Dense(units=hp_units, activation='relu'))\n",
    "    \n",
    "    # Add next layers\n",
    "    model.add(keras.layers.Dropout(0.5))\n",
    "    model.add(keras.layers.Dense(len(classes), activation='softmax'))\n",
    "    \n",
    "    # Tune the learning rate for the optimizer\n",
    "    # Choose an optimal value from 0.01, 0.001, or 0.0001\n",
    "    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])\n",
    "    model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),\n",
    "              loss=keras.losses.categorical_crossentropy,\n",
    "              metrics=[keras.metrics.categorical_accuracy])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Reloading Oracle from existing project ./tuning_results/oracle.json\n",
      "INFO:tensorflow:Reloading Tuner from ./tuning_results/tuner0.json\n"
     ]
    }
   ],
   "source": [
    "# Instantiate the tuner\n",
    "tuner = kt.Hyperband(model_builder, # the hypermodel\n",
    "                     objective='val_categorical_accuracy', # objective to optimize\n",
    "                     max_epochs=50,\n",
    "                     hyperband_iterations=3,\n",
    "                     project_name='tuning_results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search space summary\n",
      "Default search space size: 2\n",
      "units (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 160, 'step': 32, 'sampling': None}\n",
      "learning_rate (Choice)\n",
      "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}\n"
     ]
    }
   ],
   "source": [
    "# hypertuning settings\n",
    "tuner.search_space_summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "# Perform hypertuning\n",
    "stop_early = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n",
    "\n",
    "tf.autograph.experimental.do_not_convert\n",
    "tuner.search(xtrain, ytrain,\n",
    "             epochs=50,\n",
    "             batch_size=16,\n",
    "             callbacks=[stop_early],\n",
    "             validation_split=1/6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_model = tuner.get_best_models(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best hyperparameters\n",
    "best_hp=tuner.get_best_hyperparameters(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " mobilenetv2_1.00_224 (Funct  (None, 1280)             2257984   \n",
      " ional)                                                          \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 128)               163968    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 4)                 516       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,422,468\n",
      "Trainable params: 164,484\n",
      "Non-trainable params: 2,257,984\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build the model with the optimal hyperparameters\n",
    "h_model = tuner.hypermodel.build(best_hp)\n",
    "h_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "25/25 [==============================] - 7s 235ms/step - loss: 0.7262 - categorical_accuracy: 0.7225 - val_loss: 0.1261 - val_categorical_accuracy: 0.9625\n",
      "Epoch 2/50\n",
      "25/25 [==============================] - 5s 212ms/step - loss: 0.1103 - categorical_accuracy: 0.9625 - val_loss: 0.0518 - val_categorical_accuracy: 1.0000\n",
      "Epoch 3/50\n",
      "25/25 [==============================] - 5s 209ms/step - loss: 0.0541 - categorical_accuracy: 0.9850 - val_loss: 0.0240 - val_categorical_accuracy: 1.0000\n",
      "Epoch 4/50\n",
      "25/25 [==============================] - 5s 203ms/step - loss: 0.0416 - categorical_accuracy: 0.9900 - val_loss: 0.0213 - val_categorical_accuracy: 1.0000\n",
      "Epoch 5/50\n",
      "25/25 [==============================] - 5s 214ms/step - loss: 0.0210 - categorical_accuracy: 0.9975 - val_loss: 0.0173 - val_categorical_accuracy: 0.9875\n",
      "Epoch 6/50\n",
      "25/25 [==============================] - 5s 214ms/step - loss: 0.0187 - categorical_accuracy: 0.9950 - val_loss: 0.0218 - val_categorical_accuracy: 1.0000\n",
      "Epoch 7/50\n",
      "25/25 [==============================] - 5s 215ms/step - loss: 0.0186 - categorical_accuracy: 0.9975 - val_loss: 0.0196 - val_categorical_accuracy: 1.0000\n",
      "Epoch 8/50\n",
      "25/25 [==============================] - 5s 213ms/step - loss: 0.0100 - categorical_accuracy: 1.0000 - val_loss: 0.0149 - val_categorical_accuracy: 1.0000\n",
      "Epoch 9/50\n",
      "25/25 [==============================] - 5s 216ms/step - loss: 0.0094 - categorical_accuracy: 1.0000 - val_loss: 0.0134 - val_categorical_accuracy: 1.0000\n",
      "Epoch 10/50\n",
      "25/25 [==============================] - 5s 214ms/step - loss: 0.0067 - categorical_accuracy: 1.0000 - val_loss: 0.0156 - val_categorical_accuracy: 0.9875\n",
      "Epoch 11/50\n",
      "25/25 [==============================] - 5s 216ms/step - loss: 0.0048 - categorical_accuracy: 1.0000 - val_loss: 0.0140 - val_categorical_accuracy: 1.0000\n",
      "Epoch 12/50\n",
      "25/25 [==============================] - 5s 214ms/step - loss: 0.0048 - categorical_accuracy: 1.0000 - val_loss: 0.0095 - val_categorical_accuracy: 1.0000\n",
      "Epoch 13/50\n",
      "25/25 [==============================] - 5s 212ms/step - loss: 0.0067 - categorical_accuracy: 0.9975 - val_loss: 0.0073 - val_categorical_accuracy: 1.0000\n",
      "Epoch 14/50\n",
      "25/25 [==============================] - 5s 211ms/step - loss: 0.0037 - categorical_accuracy: 1.0000 - val_loss: 0.0068 - val_categorical_accuracy: 1.0000\n",
      "Epoch 15/50\n",
      "25/25 [==============================] - 5s 218ms/step - loss: 0.0075 - categorical_accuracy: 0.9975 - val_loss: 0.0077 - val_categorical_accuracy: 1.0000\n",
      "Epoch 16/50\n",
      "25/25 [==============================] - 5s 218ms/step - loss: 0.0055 - categorical_accuracy: 1.0000 - val_loss: 0.0186 - val_categorical_accuracy: 0.9875\n",
      "Epoch 17/50\n",
      "25/25 [==============================] - 5s 218ms/step - loss: 0.0051 - categorical_accuracy: 1.0000 - val_loss: 0.0080 - val_categorical_accuracy: 1.0000\n",
      "Epoch 18/50\n",
      "25/25 [==============================] - 5s 216ms/step - loss: 0.0026 - categorical_accuracy: 1.0000 - val_loss: 0.0041 - val_categorical_accuracy: 1.0000\n",
      "Epoch 19/50\n",
      "25/25 [==============================] - 5s 216ms/step - loss: 8.2033e-04 - categorical_accuracy: 1.0000 - val_loss: 0.0045 - val_categorical_accuracy: 1.0000\n",
      "Epoch 20/50\n",
      "25/25 [==============================] - 6s 224ms/step - loss: 0.0014 - categorical_accuracy: 1.0000 - val_loss: 0.0046 - val_categorical_accuracy: 1.0000\n",
      "Epoch 21/50\n",
      "25/25 [==============================] - 5s 220ms/step - loss: 0.0015 - categorical_accuracy: 1.0000 - val_loss: 0.0047 - val_categorical_accuracy: 1.0000\n",
      "Epoch 22/50\n",
      "25/25 [==============================] - 5s 210ms/step - loss: 0.0066 - categorical_accuracy: 0.9975 - val_loss: 0.0281 - val_categorical_accuracy: 0.9875\n",
      "Epoch 23/50\n",
      "25/25 [==============================] - 5s 202ms/step - loss: 0.0012 - categorical_accuracy: 1.0000 - val_loss: 0.0043 - val_categorical_accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fbde8125bd0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.autograph.experimental.do_not_convert\n",
    "h_model.fit(xtrain, ytrain,\n",
    "            epochs=50,\n",
    "            batch_size=16,\n",
    "            callbacks=[stop_early],\n",
    "            validation_split=1/6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(h_model.history.history) # losses in the training history\n",
    "epoch = losses.index.values+np.ones_like(losses.index.values) # epoch number\n",
    "loss = losses['loss'].values # training loss\n",
    "val_loss = losses['val_loss'].values # validation loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot losses against epoch number in log-scale\n",
    "plt.figure(figsize=(8,5),dpi=100)\n",
    "plt.semilogx(epoch, loss, linewidth=1.5, label='loss')\n",
    "plt.semilogx(epoch, val_loss, linewidth=1.5, label='val_loss')\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.ylabel('Loss Value',fontsize=12)\n",
    "plt.legend(loc=1,fontsize=12)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FBD95EDA050>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_test = keras.preprocessing.image.load_img('test_images/phone_3.png', target_size=(224,224))\n",
    "img_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_classification(image_path, model):\n",
    "    image = keras.preprocessing.image.load_img(image_path, target_size=(224,224))\n",
    "    pic_array = keras.preprocessing.image.img_to_array(image)\n",
    "    image_batch = np.expand_dims(pic_array, axis=0)\n",
    "    processed_image = keras.applications.mobilenet_v2.preprocess_input(image_batch)\n",
    "    probs = h_model.predict(processed_image)[0].tolist()\n",
    "    zipped = sorted(list(zip(classes, probs)), key=lambda x: x[1], reverse=True)\n",
    "    image_class = [zipped[i][0] for i in range(len(zipped))]\n",
    "    probability  = [zipped[i][1]*100 for i in range(len(zipped))]\n",
    "    df = pd.DataFrame(data={'image_class':image_class, 'probability(%)': probability})\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 22ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_class</th>\n",
       "      <th>probability(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>phone</td>\n",
       "      <td>99.967122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>book</td>\n",
       "      <td>0.016052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mug</td>\n",
       "      <td>0.012938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fork</td>\n",
       "      <td>0.003886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  image_class  probability(%)\n",
       "0       phone       99.967122\n",
       "1        book        0.016052\n",
       "2         mug        0.012938\n",
       "3        fork        0.003886"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.autograph.experimental.do_not_convert\n",
    "image_classification('test_images/phone_5.png', h_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}