def testL1L2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l1_l2_regularizer(scope='L1L2')(tensor)
self.assertEqual(loss.op.name, 'L1L2/value')
self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5)
def testL1L2RegularizerWithScope(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
loss = losses.l1_l2_regularizer(scope='L1L2')(tensor)
self.assertEquals(loss.op.name, 'L1L2/value')
self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5)
def testL1L2RegularizerWithWeights(self):
with self.test_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = tf.constant(1.0, shape=shape)
weight_l1 = 0.01
weight_l2 = 0.05
loss = losses.l1_l2_regularizer(weight_l1, weight_l2)(tensor)
self.assertEquals(loss.op.name, 'L1L2Regularizer/value')
self.assertAlmostEqual(loss.eval(),
num_elem * weight_l1 + num_elem * weight_l2 / 2, 5)