def test_save(self):
path = "tmp"
filename = "model.h5"
classifier = get_classifier_bb()
self.assertRaises(NotImplementedError, lambda: classifier.save(filename, path=path))
def test_repr(self):
classifier = get_classifier_bb()
repr_ = repr(classifier)
self.assertIn("BlackBoxClassifier", repr_)
self.assertIn("clip_values=[ 0. 255.]", repr_)
self.assertIn("defences=None", repr_)
self.assertIn("preprocessing=(0, 1)", repr_)
def test_fit(self):
classifier = get_classifier_bb()
self.assertRaises(
NotImplementedError,
lambda: classifier.fit(self.x_train_mnist,
self.y_train_mnist,
batch_size=self.batch_size,
nb_epochs=2),
)
def test_repr(self):
classifier = get_classifier_bb()
repr_ = repr(classifier)
self.assertIn("BlackBoxClassifier", repr_)
self.assertIn("clip_values=[ 0. 255.]", repr_)
self.assertIn("defences=None", repr_)
self.assertIn(
"preprocessing=StandardisationMeanStd(mean=0, std=1, apply_fit=True, apply_predict=True)",
repr_)
def test_defences_predict(self):
clip_values = (0, 1)
fs = FeatureSqueezing(clip_values=clip_values, bit_depth=2)
jpeg = JpegCompression(clip_values=clip_values, apply_predict=True)
smooth = SpatialSmoothing()
classifier = get_classifier_bb(defences=[fs, jpeg, smooth])
self.assertEqual(len(classifier.preprocessing_defences), 3)
predictions_classifier = classifier.predict(self.x_test_mnist)
# Apply the same defences by hand
x_test_defense = self.x_test_mnist
x_test_defense, _ = fs(x_test_defense, self.y_test_mnist)
x_test_defense, _ = jpeg(x_test_defense, self.y_test_mnist)
x_test_defense, _ = smooth(x_test_defense, self.y_test_mnist)
classifier = get_classifier_bb()
predictions_check = classifier.predict(x_test_defense)
# Check that the prediction results match
np.testing.assert_array_almost_equal(predictions_classifier, predictions_check, decimal=4)
def test_shapes(self):
classifier = get_classifier_bb()
predictions = classifier.predict(self.x_test_mnist)
self.assertEqual(predictions.shape, self.y_test_mnist.shape)
self.assertEqual(classifier.nb_classes, 10)
self.assertEqual(predictions.shape, self.y_test_mnist.shape)
