img_width, img_height = 1185, 676

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 32
nb_validation_samples = 8
nb_epoch = 3

model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(3, img_width, img_height)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf"))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              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)

train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        batch_size=4,
        target_size=(img_width, img_height),
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        validation_data_dir,
        batch_size=4,
        target_size=(img_width, img_height),
        class_mode='binary')

model.fit_generator(
        train_generator,
        samples_per_epoch=nb_train_samples,
        nb_epoch=nb_epoch,
        validation_data=validation_generator,
        nb_val_samples=nb_validation_samples)