def test_loss_gradient(self):
(_, _), (x_test, y_test) = self.mnist
classifier = ClassifierWrapper(self.model_mnist)
# Test gradient
grads = classifier.loss_gradient(x_test, y_test)
self.assertTrue(np.array(grads.shape == (NB_TEST, 28, 28, 1)).all())
self.assertNotEqual(np.sum(grads), 0)
def test_shapes(self):
x_test, y_test = self.mnist[1]
classifier = ClassifierWrapper(self.model_mnist)
preds = classifier.predict(self.mnist[1][0])
self.assertTrue(preds.shape == y_test.shape)
self.assertTrue(classifier.nb_classes == 10)
class_grads = classifier.class_gradient(x_test[:11])
self.assertTrue(class_grads.shape == tuple([11, 10] + list(x_test[1].shape)))
loss_grads = classifier.loss_gradient(x_test[:11], y_test[:11])
self.assertTrue(loss_grads.shape == x_test[:11].shape)