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)
def test_fit_generator(get_default_mnist_subset, default_batch_size, get_image_classifier_list):
(x_train_mnist, y_train_mnist), (x_test_mnist, y_test_mnist) = get_default_mnist_subset
gen = generator_fit(x_train_mnist, y_train_mnist, batch_size=default_batch_size)
data_gen = KerasDataGenerator(iterator=gen, size=x_train_mnist.shape[0], batch_size=default_batch_size)
classifier, _ = get_image_classifier_list(one_classifier=True)
expected_values = {"pre_fit_accuracy": ExpectedValue(0.32, 0.06), "post_fit_accuracy": ExpectedValue(0.36, 0.06)}
backend_test_fit_generator(expected_values, classifier, data_gen, get_default_mnist_subset, nb_epochs=3)
def test_fit_generator(self):
labels = np.argmax(self.y_test_mnist, axis=1)
classifier = get_image_classifier_kr_tf()
acc = np.sum(np.argmax(classifier.predict(self.x_test_mnist), axis=1) == labels) / self.n_test
logger.info("Accuracy: %.2f%%", (acc * 100))
gen = generator_fit(self.x_train_mnist, self.y_train_mnist, batch_size=self.batch_size)
data_gen = KerasDataGenerator(iterator=gen, size=self.n_train, batch_size=self.batch_size)
classifier.fit_generator(generator=data_gen, nb_epochs=2)
acc2 = np.sum(np.argmax(classifier.predict(self.x_test_mnist), axis=1) == labels) / self.n_test
logger.info("Accuracy: %.2f%%", (acc2 * 100))
self.assertEqual(acc, 0.32)
self.assertAlmostEqual(acc2, 0.70, delta=0.15)
def test_fit_generator(self):
classifier = get_classifier_kr()
labels = np.argmax(self.y_test, axis=1)
accuracy = np.sum(np.argmax(classifier.predict(self.x_test), axis=1) == labels) / NB_TEST
logger.info('Accuracy: %.2f%%', (accuracy * 100))
gen = generator_fit(self.x_train, self.y_train, batch_size=BATCH_SIZE)
data_gen = KerasDataGenerator(generator=gen, size=NB_TRAIN, batch_size=BATCH_SIZE)
classifier.fit_generator(generator=data_gen, nb_epochs=3)
accuracy_2 = np.sum(np.argmax(classifier.predict(self.x_test), axis=1) == labels) / NB_TEST
logger.info('Accuracy: %.2f%%', (accuracy_2 * 100))
self.assertEqual(accuracy, 0.32)
self.assertAlmostEqual(accuracy_2, 0.36, delta=0.02)
def test_fit_generator(self):
from art.classifiers.keras import generator_fit
from art.data_generators import KerasDataGenerator
labels = np.argmax(self.mnist[1][1], axis=1)
acc = np.sum(
np.argmax(self.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=100)
data_gen = KerasDataGenerator(generator=gen,
size=NB_TRAIN,
batch_size=100)
self.classifier.fit_generator(generator=data_gen, nb_epochs=2)
acc2 = np.sum(
np.argmax(self.classifier.predict(self.mnist[1][0]), axis=1) ==
labels) / NB_TEST
logger.info('Accuracy: %.2f%%', (acc2 * 100))
self.assertTrue(acc2 >= .8 * acc)