ch5-2.py

import os
import sys

try:
    base_directory = os.path.split(sys.executable)[0]
    os.environ['PATH'] += ';' + base_directory
    import cntk
    os.environ['KERAS_BACKEND'] = 'cntk'
except ImportError:
    print('CNTK not installed')

import keras
import keras.utils
import keras.datasets
import keras.models
import keras.layers
import keras.preprocessing.image
import numpy as np
import matplotlib.pyplot as plt
import shutil

# The path to the directory where the original
# dataset was uncompressed
original_dataset_dir = 'C:/Users/anastasios/Downloads/train/train'

# The directory where we will
# store our smaller dataset
base_dir = 'C:/Users/anastasios/Desktop/cats_and_dogs'

# Directories for our training,
# validation and test splits
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')


def prepare_files():
    if os.path.isdir(base_dir):
        print('{0} exists.'.format(base_dir))
        return
    os.mkdir(base_dir)

    os.mkdir(train_dir)
    os.mkdir(validation_dir)
    os.mkdir(test_dir)

    # Directory with our training cat pictures
    train_cats_dir = os.path.join(train_dir, 'cats')
    os.mkdir(train_cats_dir)

    # Directory with our training dog pictures
    train_dogs_dir = os.path.join(train_dir, 'dogs')
    os.mkdir(train_dogs_dir)

    # Directory with our validation cat pictures
    validation_cats_dir = os.path.join(validation_dir, 'cats')
    os.mkdir(validation_cats_dir)

    # Directory with our validation dog pictures
    validation_dogs_dir = os.path.join(validation_dir, 'dogs')
    os.mkdir(validation_dogs_dir)

    # Directory with our validation cat pictures
    test_cats_dir = os.path.join(test_dir, 'cats')
    os.mkdir(test_cats_dir)

    # Directory with our validation dog pictures
    test_dogs_dir = os.path.join(test_dir, 'dogs')
    os.mkdir(test_dogs_dir)

    # Copy first 1000 cat images to train_cats_dir
    fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
    for fname in fnames:
        src = os.path.join(original_dataset_dir, fname)
        dst = os.path.join(train_cats_dir, fname)
        shutil.copyfile(src, dst)

    # Copy next 500 cat images to validation_cats_dir
    fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
    for fname in fnames:
        src = os.path.join(original_dataset_dir, fname)
        dst = os.path.join(validation_cats_dir, fname)
        shutil.copyfile(src, dst)

    # Copy next 500 cat images to test_cats_dir
    fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
    for fname in fnames:
        src = os.path.join(original_dataset_dir, fname)
        dst = os.path.join(test_cats_dir, fname)
        shutil.copyfile(src, dst)

    # Copy first 1000 dog images to train_dogs_dir
    fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
    for fname in fnames:
        src = os.path.join(original_dataset_dir, fname)
        dst = os.path.join(train_dogs_dir, fname)
        shutil.copyfile(src, dst)

    # Copy next 500 dog images to validation_dogs_dir
    fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
    for fname in fnames:
        src = os.path.join(original_dataset_dir, fname)
        dst = os.path.join(validation_dogs_dir, fname)
        shutil.copyfile(src, dst)

    # Copy next 500 dog images to test_dogs_dir
    fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
    for fname in fnames:
        src = os.path.join(original_dataset_dir, fname)
        dst = os.path.join(test_dogs_dir, fname)
        shutil.copyfile(src, dst)


def build_network():
    model = keras.models.Sequential()
    model.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Conv2D(128, (3, 3), activation='relu'))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Conv2D(128, (3, 3), activation='relu'))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(512, activation='relu'))
    model.add(keras.layers.Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.RMSprop(lr=1e-4), metrics=['acc'])
    model.summary()
    return model


def train_network_with_no_augmentation():
    model = build_network()

    # All images will be rescaled by 1./255
    train_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
    test_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)

    train_generator = train_datagen.flow_from_directory(
            # This is the target directory
            train_dir,
            # All images will be resized to 150x150
            target_size=(150, 150),
            batch_size=20,
            # Since we use binary_crossentropy loss, we need binary labels
            class_mode='binary')

    validation_generator = test_datagen.flow_from_directory(
            validation_dir,
            target_size=(150, 150),
            batch_size=20,
            class_mode='binary')

    for data_batch, labels_batch in train_generator:
        print('data batch shape:', data_batch.shape)
        print('labels batch shape:', labels_batch.shape)
        break


    history = model.fit_generator(
          train_generator,
          steps_per_epoch=100,
          epochs=30,
          validation_data=validation_generator,
          validation_steps=50)

    model.save('cats_and_dogs_small_1.h5')
    return history


def plot_history(history):
    acc = history.history['acc']
    val_acc = history.history['val_acc']
    loss = history.history['loss']
    val_loss = history.history['val_loss']

    epochs = range(len(acc))

    plt.plot(epochs, acc, 'bo', label='Training acc')
    plt.plot(epochs, val_acc, 'b', label='Validation acc')
    plt.title('Training and validation accuracy')
    plt.legend()

    plt.figure()

    plt.plot(epochs, loss, 'bo', label='Training loss')
    plt.plot(epochs, val_loss, 'b', label='Validation loss')
    plt.title('Training and validation loss')
    plt.legend()

    plt.show()


def demo_augmentation():
    datagen = keras.preprocessing.image.ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')

    train_cats_dir = os.path.join(train_dir, 'cats')
    fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]

    # We pick one image to "augment"
    img_path = fnames[3]

    # Read the image and resize it
    img = keras.preprocessing.image.load_img(img_path, target_size=(150, 150))

    # Convert it to a Numpy array with shape (150, 150, 3)
    x = keras.preprocessing.image.img_to_array(img)

    # Reshape it to (1, 150, 150, 3)
    x = x.reshape((1,) + x.shape)

    # The .flow() command below generates batches of randomly transformed images.
    # It will loop indefinitely, so we need to `break` the loop at some point!
    i = 0
    for batch in datagen.flow(x, batch_size=1):
        plt.figure(i)
        imgplot = plt.imshow(keras.preprocessing.image.array_to_img(batch[0]))
        i += 1
        if i % 4 == 0:
            break

    plt.show()


def build_network_with_dropout():
    model = keras.models.Sequential()
    model.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Conv2D(128, (3, 3), activation='relu'))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Conv2D(128, (3, 3), activation='relu'))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dropout(0.5))
    model.add(keras.layers.Dense(512, activation='relu'))
    model.add(keras.layers.Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.RMSprop(lr=1e-4), metrics=['acc'])
    return model


def train_network_with_augmentation():
    model = build_network_with_dropout()

    train_datagen = keras.preprocessing.image.ImageDataGenerator(
        rescale=1./255,
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,)


    # Note that the validation data should not be augmented!
    test_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)

    train_generator = train_datagen.flow_from_directory(
            # This is the target directory
            train_dir,
            # All images will be resized to 150x150
            target_size=(150, 150),
            batch_size=32,
            # Since we use binary_crossentropy loss, we need binary labels
            class_mode='binary')

    validation_generator = test_datagen.flow_from_directory(
            validation_dir,
            target_size=(150, 150),
            batch_size=32,
            class_mode='binary')

    model = build_network_with_dropout()

    history = model.fit_generator(
          train_generator,
          steps_per_epoch=100,
          epochs=100,
          validation_data=validation_generator,
          validation_steps=50)

    model.save('cats_and_dogs_small_2.h5')
    return history


prepare_files()


plot_history(train_network_with_augmentation())