def testRegularization(self, type, scale):
layer = tf.keras.layers.Dense(256)
layer.build([None, 128])
regularization = losses.regularization_penalty(
type, scale, layer.trainable_variables)
self.assertEqual(0, len(regularization.shape))
self.evaluate(regularization)
def regularize_loss(self, loss, variables=None):
"""Regularizes the loss.
Args:
loss: The loss.
variables: List of variables.
Returns:
The regularized loss.
"""
if variables is None:
variables = self.trainable_variables
regularization = self.params.get("regularization")
if regularization is not None:
loss += losses.regularization_penalty(
regularization["type"], regularization["scale"], variables)
return loss
def testRegulaizationMissingScaleValue(self):
with self.assertRaises(ValueError):
losses.regularization_penalty("l1_l2", 1e-4, [])
def testRegulaizationInvalidType(self):
with self.assertRaises(ValueError):
losses.regularization_penalty("l3", 1e-4, [])
