def test_fit(self):
classifier = get_classifier_bb()
self.assertRaises(
NotImplementedError, lambda: classifier.fit(
self.x_train, self.y_train, batch_size=BATCH_SIZE, nb_epochs=2)
)
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)
print(repr_)
self.assertIn('BlackBoxClassifier', repr_)
self.assertIn('clip_values=(0, 255)', repr_)
self.assertIn('defences=None', repr_)
self.assertIn('preprocessing=(0, 1)', 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.defences), 3)
predictions_classifier = classifier.predict(self.x_test)
# Apply the same defences by hand
x_test_defense = self.x_test
x_test_defense, _ = fs(x_test_defense, self.y_test)
x_test_defense, _ = jpeg(x_test_defense, self.y_test)
x_test_defense, _ = smooth(x_test_defense, self.y_test)
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)
self.assertEqual(predictions.shape, self.y_test.shape)
self.assertEqual(classifier.nb_classes(), 10)
self.assertEqual(predictions.shape, self.y_test.shape)