05-food-classification-transfer-learning-tensorflow-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f37776b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential\n",
    "from helper_function import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a91198c1",
   "metadata": {},
   "source": [
    "### 1. Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bcbc6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir_path = \"image_data/101_food_classes_10_percent/train/\"\n",
    "test_dir_path = \"image_data/101_food_classes_10_percent/test/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8d3f3a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "### Walk thro image data path\n",
    "\n",
    "walk_through_dir(\"image_data/101_food_classes_10_percent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6dd7ce2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 7575 files belonging to 101 classes.\n",
      "Found 25250 files belonging to 101 classes.\n"
     ]
    }
   ],
   "source": [
    "img_size = (224, 224)\n",
    "\n",
    "### Shuffle for training data to prevent overfitting\n",
    "train_data_10_percent = tf.keras.preprocessing.image_dataset_from_directory(directory = train_dir_path,\n",
    "    image_size = img_size, label_mode = \"categorical\", batch_size = 32)\n",
    "\n",
    "### Don't shuffle testing data, we do not need it on testing\n",
    "test_data = tf.keras.preprocessing.image_dataset_from_directory(directory = test_dir_path,\n",
    "    image_size = img_size, label_mode = \"categorical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef0127d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create check point\n",
    "\n",
    "### Save only the model weight\n",
    "### Save the model`s weight which score the best validation accuracy\n",
    "checkpoint_path = \"101_classes_10_percent_transfer_learning_3_checkpoint\"\n",
    "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, save_weights_only = True,\n",
    "    monitor = \"val_accuracy\", save_best_only = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63a40565",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create data augmentation\n",
    "\n",
    "data_augmentation = Sequential([\n",
    "  layers.RandomFlip(\"horizontal\"),\n",
    "  layers.RandomRotation(0.2),\n",
    "  layers.RandomZoom(0.2),\n",
    "  layers.RandomHeight(0.2),\n",
    "  layers.RandomWidth(0.2),\n",
    "], name = \"data_augmentation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d1a5e6e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " data_augmentation (Sequent  (None, None, None, 3)     0         \n",
      " ial)                                                            \n",
      "                                                                 \n",
      " efficientnetb0 (Functional  (None, None, None, 1280   4049571   \n",
      " )                           )                                   \n",
      "                                                                 \n",
      " global_average_pooling (Gl  (None, 1280)              0         \n",
      " obalAveragePooling2D)                                           \n",
      "                                                                 \n",
      " output_layer (Dense)        (None, 101)               129381    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4178952 (15.94 MB)\n",
      "Trainable params: 129381 (505.39 KB)\n",
      "Non-trainable params: 4049571 (15.45 MB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = tf.keras.applications.efficientnet.EfficientNetB0(include_top = False)\n",
    "base_model.trainable = False\n",
    "\n",
    "input_layer = layers.Input(shape = (224, 224, 3), name = \"input_layer\")\n",
    "\n",
    "x = data_augmentation(input_layer)\n",
    "x = base_model(x, training = False)\n",
    "x = layers.GlobalAveragePooling2D(name = \"global_average_pooling\")(x)\n",
    "\n",
    "output_layer = layers.Dense(len(train_data_10_percent.class_names), activation = \"softmax\", name = \"output_layer\")(x)\n",
    "\n",
    "model = tf.keras.Model(input_layer, output_layer)\n",
    "\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.Adam(),\n",
    "    metrics = [\"accuracy\"])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df1e5d5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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