def test_l1_l2_scale_l2Zero(self):
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = constant_op.constant(1.0, shape=shape)
loss = regularizers.l1_l2_regularizer(1.0, 0.0)(tensor)
with self.cached_session():
self.assertEquals(loss.op.name, 'l1_l2_regularizer')
self.assertAlmostEqual(loss.eval(), num_elem, 5)
def testL1L2RegularizerWithScope(self):
with self.cached_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = constant_op.constant(1.0, shape=shape)
with ops.name_scope('foo'):
loss = regularizers.l1_l2_regularizer(1.0, 1.0, scope='l1_l2')(tensor)
self.assertEquals(loss.op.name, 'foo/l1_l2')
self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5)
def test_l1_l2(self):
with self.assertRaises(ValueError):
regularizers.l1_l2_regularizer(-1., 0.5)
with self.assertRaises(ValueError):
regularizers.l1_l2_regularizer(0.5, -1.)
with self.assertRaises(ValueError):
regularizers.l1_l2_regularizer(0, 0.5)
with self.assertRaises(ValueError):
regularizers.l1_l2_regularizer(0.5, 0)
with self.cached_session():
shape = [5, 5, 5]
num_elem = 5 * 5 * 5
tensor = constant_op.constant(1.0, shape=shape)
loss = regularizers.l1_l2_regularizer(1.0, 1.0)(tensor)
self.assertEquals(loss.op.name, 'l1_l2_regularizer')
self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5)
def test_l1_l2_scales_Zero(self): shape = [5, 5, 5] tensor = constant_op.constant(1.0, shape=shape) loss = regularizers.l1_l2_regularizer(0.0, 0.0)(tensor) self.assertEquals(loss, None)