Xception_Transfer_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d145d835",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 11/18/2021-1\n",
    "from __future__ import absolute_import, division\n",
    "from IPython.display import clear_output\n",
    "import os\n",
    "from os.path import join\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "contentPath = os.getcwd()\n",
    "dataPath = join(contentPath, 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c0e3e5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "data_augmentation = keras.Sequential(\n",
    "    [layers.RandomFlip(\"horizontal\"), layers.RandomRotation(0.1),]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9221e584",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = keras.applications.Xception(\n",
    "    weights=\"imagenet\",  # Load weights pre-trained on ImageNet.\n",
    "    input_shape=(150, 150, 3),\n",
    "    include_top=False,\n",
    ")  # Do not include the ImageNet classifier at the top.\n",
    "\n",
    "# Freeze the base_model\n",
    "base_model.trainable = False\n",
    "\n",
    "# Create new model on top\n",
    "inputs = keras.Input(shape=(150, 150, 3))\n",
    "x = data_augmentation(inputs)  # Apply random data augmentation\n",
    "\n",
    "# Pre-trained Xception weights requires that input be scaled\n",
    "# from (0, 255) to a range of (-1., +1.), the rescaling layer\n",
    "# outputs: `(inputs * scale) + offset`\n",
    "scale_layer = keras.layers.Rescaling(scale=1 / 127.5, offset=-1)\n",
    "x = scale_layer(x)\n",
    "\n",
    "# The base model contains batchnorm layers. We want to keep them in inference mode\n",
    "# when we unfreeze the base model for fine-tuning, so we make sure that the\n",
    "# base_model is running in inference mode here.\n",
    "x = base_model(x, training=False)\n",
    "x = keras.layers.GlobalAveragePooling2D()(x)\n",
    "x = keras.layers.Dropout(0.2)(x)  # Regularize with dropout\n",
    "outputs = keras.layers.Dense(1)(x)\n",
    "model = keras.Model(inputs, outputs)\n",
    "\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49de101c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2Path=join(contentPath, 'CV2Images')\n",
    "\n",
    "from tensorflow.keras.utils import image_dataset_from_directory\n",
    "train_ds = image_dataset_from_directory(\n",
    "    cv2Path,\n",
    "    color_mode='rgb',\n",
    "    validation_split=0.2,\n",
    "    subset=\"training\",\n",
    "    seed=456,\n",
    "    image_size=(150, 150),\n",
    "    batch_size=4)\n",
    "print()\n",
    "validation_ds = image_dataset_from_directory(\n",
    "    cv2Path,\n",
    "    color_mode='rgb',\n",
    "    validation_split=0.2,\n",
    "    subset=\"validation\",\n",
    "    seed=456,\n",
    "    image_size=(150, 150),\n",
    "    batch_size=4)\n",
    "print()\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fe04d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(),\n",
    "    loss=keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    metrics=[keras.metrics.BinaryAccuracy()],\n",
    ")\n",
    "\n",
    "epochs = 4\n",
    "model.fit(train_ds, epochs=epochs, validation_data=validation_ds)\n",
    "clear_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11c7b40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unfreeze the base_model. Note that it keeps running in inference mode\n",
    "# since we passed `training=False` when calling it. This means that\n",
    "# the batchnorm layers will not update their batch statistics.\n",
    "# This prevents the batchnorm layers from undoing all the training\n",
    "# we've done so far.\n",
    "base_model.trainable = True\n",
    "model.summary()\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(1e-5),  # Low learning rate\n",
    "    loss=keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    metrics=[keras.metrics.BinaryAccuracy()],\n",
    ")\n",
    "\n",
    "epochs = 2\n",
    "history=model.fit(train_ds,\n",
    "                  epochs=epochs,\n",
    "                  validation_data=validation_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c883bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from CV2_Utils import *\n",
    "import cv2\n",
    "testPath= join(contentPath, 'friendly_validation_13.png')\n",
    "img=cv2.imread(testPath)\n",
    "img=cv2.resize(img, dsize=(150, 150))\n",
    "cvu.plotShowSingleImage(img, title1=img.shape)\n",
    "result=model.predict(x=img, shape=(None,150,150,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a85af6f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "modelName = 'retrained_' base_model.name + '.h5'\n",
    "savePath = join(contentPath, modelName)\n",
    "# model.save()"
   ]
  }
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