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_save(self):
import os
path = 'tmp'
filename = 'model.h5'
classifier = ClassifierWrapper(self.model_mnist)
classifier.save(filename, path=path)
self.assertTrue(os.path.isfile(os.path.join(path, filename)))
# Remove saved file
os.remove(os.path.join(path, filename))
def test_layers(self):
(_, _), (x_test, _), _, _ = load_mnist()
x_test = x_test[:NB_TEST]
classifier = ClassifierWrapper(self.model_mnist)
self.assertEqual(len(classifier.layer_names), 3)
layer_names = classifier.layer_names
for i, name in enumerate(layer_names):
act_i = classifier.get_activations(x_test, i, batch_size=128)
act_name = classifier.get_activations(x_test, name, batch_size=128)
self.assertAlmostEqual(np.sum(act_name - act_i), 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)
def test_class_gradient(self):
(_, _), (x_test, _) = self.mnist
classifier = ClassifierWrapper(self.model_mnist)
# Test all gradients label
grads = classifier.class_gradient(x_test)
self.assertTrue(np.array(grads.shape == (NB_TEST, 10, 28, 28, 1)).all())
self.assertTrue(np.sum(grads) != 0)
# Test 1 gradient label = 5
grads = classifier.class_gradient(x_test, label=5)
self.assertTrue(np.array(grads.shape == (NB_TEST, 1, 28, 28, 1)).all())
self.assertTrue(np.sum(grads) != 0)
# Test a set of gradients label = array
label = np.random.randint(5, size=NB_TEST)
grads = classifier.class_gradient(x_test, label=label)
self.assertTrue(np.array(grads.shape == (NB_TEST, 1, 28, 28, 1)).all())
self.assertTrue(np.sum(grads) != 0)