from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator

classifier = Sequential()

# Convolution 
classifier.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))

# Max pooling
classifier.add(MaxPooling2D(pool_size=(2, 2)))

# Flattening
classifier.add(Flatten())

# Full connection
classifier.add(Dense(units=128, activation='relu'))
classifier.add(Dense(units=1, activation='sigmoid'))

# Compiling CNN
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory(
        'dataset/training_set',
        target_size=(64, 64),
        batch_size=32,
        class_mode='binary')

testing_set = test_datagen.flow_from_directory(
        'dataset/test_set',
        target_size=(64, 64),
        batch_size=32,
        class_mode='binary')

classifier.fit_generator(
        training_set,
        steps_per_epoch=8000,
        epochs=25,
        validation_data=testing_set,
        validation_steps=2000)