dosa_transfer_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.preprocessing.image import load_img, img_to_array, array_to_img, ImageDataGenerator\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Activation, Dropout, Flatten, Dense, GlobalAveragePooling2D\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.applications import ResNet50\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = load_img('food-binary/Dosa/img39.jpeg')\n",
    "x = img_to_array(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(x/255.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_generator = ImageDataGenerator(rotation_range=90.,\n",
    "                                   featurewise_center=True, \n",
    "                                   horizontal_flip=True,\n",
    "                                   fill_mode='reflect',\n",
    "                                   vertical_flip=True,\n",
    "                                   zoom_range=0.4,\n",
    "                                   featurewise_std_normalization=True,\n",
    "                                   width_shift_range=20,\n",
    "                                   height_shift_range=20,\n",
    "                                   validation_split=0.2, rescale=1./255)\n",
    "\n",
    "def get_batches(path, subset, gen=img_generator, \n",
    "                shuffle=True, batch_size=8, class_mode='categorical'): \n",
    "    return gen.flow_from_directory(path, target_size=(228,228), \n",
    "                                   class_mode=class_mode, shuffle=shuffle, batch_size=batch_size, subset=subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = load_img('food-binary/Dosa/img101.jpeg')  \n",
    "x = img_to_array(img)  \n",
    "x = x.reshape((1,) + x.shape)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create preview folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "for batch in img_generator.flow(x, batch_size=1,\n",
    "                          save_to_dir='preview', save_prefix='cat', save_format='jpeg'):\n",
    "    i += 1\n",
    "    if i > 20:\n",
    "        break  # otherwise the generator would loop indefinitely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check images in preview folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transfer Learning with Image Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications import ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_generator = get_batches('food-binary/', 'training')\n",
    "val_generator = get_batches('food-binary/', 'validation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = ResNet50(include_top=False, input_shape=(228,228,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "predictions = Dense(2, activation='softmax')(x)\n",
    "m = Model(inputs=base_model.input, outputs=predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.compile(loss='categorical_crossentropy',\n",
    "              optimizer='sgd',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.fit_generator(\n",
    "        train_generator,\n",
    "        steps_per_epoch=2000 // batch_size,\n",
    "        epochs=1,\n",
    "        validation_data=val_generator,\n",
    "        validation_steps=800 // batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}