def testRegularization(self, type, scale): layer = tf.keras.layers.Dense(256) layer.build([None, 128]) regularization = optim.regularization_penalty( type, scale, weights_list=layer.trainable_variables) self.assertEqual(0, len(regularization.shape.as_list())) if not compat.is_tf2(): with self.test_session() as sess: sess.run(tf.global_variables_initializer()) self.evaluate(regularization)
def _testRegularization(self, type, scale): tf.reset_default_graph() x = tf.placeholder_with_default( np.random.randn(64, 128).astype(np.float32), shape=(None, 128)) x = tf.layers.dense(x, 256) x = tf.layers.dense(x, 128) regularization = optim.regularization_penalty(type, scale) self.assertEqual(0, len(regularization.shape.as_list())) with self.test_session(tf.get_default_graph()) as sess: sess.run(tf.global_variables_initializer()) sess.run(regularization)
def testRegulaizationMissingScaleValue(self): with self.assertRaises(ValueError): optim.regularization_penalty("l1_l2", 1e-4, weights_list=[])
def testRegulaizationInvalidType(self): with self.assertRaises(ValueError): optim.regularization_penalty("l3", 1e-4, weights_list=[])