def test_clever_l2_no_target(self):
batch_size, nb_train, nb_test = 100, 500, 10
(x_train, y_train), (x_test, y_test), _, _ = load_mnist()
# Get the classifier
krc = self._create_krclassifier()
krc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2)
scores = clever(krc,
x_test[0],
5,
5,
3,
2,
target=None,
c_init=1,
pool_factor=10)
print("Clever Scores for n-1 classes", scores, scores.shape)
self.assertTrue(scores.shape == (krc.nb_classes - 1, ))
def test_clever_l2_same_target(self):
batch_size = 100
(x_train, y_train), (x_test, _), _, _ = load_mnist()
# Get the classifier
krc = self._create_krclassifier()
krc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2)
scores = clever(krc,
x_test[0],
5,
5,
3,
2,
target=np.argmax(krc.predict(x_test[:1])),
c_init=1,
pool_factor=10)
self.assertIsNone(
scores[0],
msg='Clever scores for the predicted class should be `None`.')
def test_clever_l2_no_target(self):
batch_size = 100
(x_train, y_train), (x_test, _), _, _ = load_mnist()
# Get the classifier
krc = self._create_krclassifier()
krc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2)
scores = clever(krc,
x_test[0],
5,
5,
3,
2,
target=None,
c_init=1,
pool_factor=10)
logger.info("Clever scores for n-1 classes: %s %s", str(scores),
str(scores.shape))
self.assertTrue(scores.shape == (krc.nb_classes - 1, ))
def test_clever_l2_no_target_sorted(self):
batch_size, nb_train, nb_test = 100, 500, 10
(x_train, y_train), (x_test, y_test), _, _ = load_mnist()
# Get the classifier
krc = self._create_krclassifier()
krc.fit(x_train, y_train, batch_size=batch_size, nb_epochs=2)
scores = clever(krc,
x_test[0],
5,
5,
3,
2,
target=None,
target_sort=True,
c_init=1,
pool_factor=10)
logger.info("Clever scores for n-1 classes: %s %s", str(scores),
str(scores.shape))
# Should approx. be in decreasing value
self.assertTrue(scores.shape == (krc.nb_classes - 1, ))
