keras_model.py

from process_images import randomly_assign_train_test
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense


def run_model():
    # dimensions of our images.
    img_width, img_height = 130, 130

    train_data_dir = 'data/train/'
    validation_data_dir = 'data/validation/'
    nb_train_samples = 1188
    nb_validation_samples = 134
    nb_epoch = 500

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

    model.add(Convolution2D(32, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Convolution2D(64, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(64))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    model.compile(loss='binary_crossentropy',
                  optimizer='rmsprop',
                  metrics=['accuracy'])

    # this is the augmentation configuration we will use for training
    train_datagen = ImageDataGenerator(
        rescale=1. / 255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

    # this is the augmentation configuration we will use for testing:
    # only rescaling
    test_datagen = ImageDataGenerator(rescale=1. / 255)

    train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=32,
        class_mode='binary')

    validation_generator = test_datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_width, img_height),
        batch_size=32,
        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)

    model.load_weights('first_try.h5')


if __name__ == '__main__':
    randomly_assign_train_test('images/')
    # run_model()
    # randomly_assign_train_test('images/')
    # run_model()
    # randomly_assign_train_test('images')