from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator

classifier = Sequential()

classifier.add(Convolution2D(32, (3, 3), padding = 'same', input_shape = (64, 64, 3), activation = 'relu'))

classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Convolution2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))


classifier.add(Flatten())


classifier.add(Dense(128, activation = 'relu'))
classifier.add(Dense(1, activation = 'sigmoid'))


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')

test_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=10,
            validation_data=test_set,
            validation_steps=2000)