def _test_fit_image_generator(self, custom_activation=False):
from keras.preprocessing.image import ImageDataGenerator
from art.data_generators import KerasDataGenerator
x_train, y_train = self.mnist[0]
labels_test = np.argmax(self.mnist[1][1], axis=1)
classifier = KerasClassifier((0, 1),
self.model_mnist,
use_logits=False,
custom_activation=custom_activation)
acc = np.sum(
np.argmax(classifier.predict(self.mnist[1][0]), axis=1) ==
labels_test) / NB_TEST
logger.info('Accuracy: %.2f%%', (acc * 100))
keras_gen = ImageDataGenerator(width_shift_range=0.075,
height_shift_range=0.075,
rotation_range=12,
shear_range=0.075,
zoom_range=0.05,
fill_mode='constant',
cval=0)
keras_gen.fit(x_train)
data_gen = KerasDataGenerator(generator=keras_gen.flow(
x_train, y_train, batch_size=BATCH_SIZE),
size=NB_TRAIN,
batch_size=BATCH_SIZE)
classifier.fit_generator(generator=data_gen, nb_epochs=2)
acc2 = np.sum(
np.argmax(classifier.predict(self.mnist[1][0]), axis=1) ==
labels_test) / NB_TEST
logger.info('Accuracy: %.2f%%', (acc2 * 100))
self.assertTrue(acc2 >= .8 * acc)
def _test_fit_generator(self, custom_activation=False):
from art.classifiers.keras import generator_fit
from art.data_generators import KerasDataGenerator
labels = np.argmax(self.mnist[1][1], axis=1)
classifier = KerasClassifier((0, 1),
self.model_mnist,
use_logits=False,
custom_activation=custom_activation)
acc = np.sum(
np.argmax(classifier.predict(self.mnist[1][0]), axis=1) ==
labels) / NB_TEST
logger.info('Accuracy: %.2f%%', (acc * 100))
gen = generator_fit(self.mnist[0][0],
self.mnist[0][1],
batch_size=BATCH_SIZE)
data_gen = KerasDataGenerator(generator=gen,
size=NB_TRAIN,
batch_size=BATCH_SIZE)
classifier.fit_generator(generator=data_gen, nb_epochs=2)
acc2 = np.sum(
np.argmax(classifier.predict(self.mnist[1][0]), axis=1) ==
labels) / NB_TEST
logger.info('Accuracy: %.2f%%', (acc2 * 100))
self.assertTrue(acc2 >= .8 * acc)
