Add support of scale_l1 == 0. or scale_l2 == 0 to l1_l2_regularizer.

Added tests.

PiperOrigin-RevId: 158179790

tensorflow/contrib/layers/python/layers/regularizers.py

@@ -124,7 +124,15 @@ def l1_l2_regularizer(scale_l1=1.0, scale_l2=1.0, scope=None):
   Raises:
     ValueError: If scale is negative or if scale is not a float.
   """
+  if isinstance(scale_l1, numbers.Integral):
+    raise ValueError('scale_l1 cannot be an integer: %s' % (scale_l1,))
+  if isinstance(scale_l2, numbers.Integral):
+    raise ValueError('scale_l2 cannot be an integer: %s' % (scale_l2,))
   scope = scope or 'l1_l2_regularizer'
+  if scale_l1 == 0.:
+    return l2_regularizer(scale_l2, scope)
+  if scale_l2 == 0.:
+    return l1_regularizer(scale_l1, scope)
   return sum_regularizer([l1_regularizer(scale_l1),
                           l2_regularizer(scale_l2)],
                          scope=scope)

tensorflow/contrib/layers/python/layers/regularizers_test.py

@@ -79,6 +79,30 @@ def test_l1_l2(self):
       self.assertEquals(loss.op.name, 'l1_l2_regularizer')
       self.assertAlmostEqual(loss.eval(), num_elem + num_elem / 2, 5)
 
+  def test_l1_l2_scale_l1Zero(self):
+    shape = [5, 5, 5]
+    num_elem = 5 * 5 * 5
+    tensor = constant_op.constant(1.0, shape=shape)
+    loss = regularizers.l1_l2_regularizer(0.0, 1.0)(tensor)
+    with self.test_session():
+      self.assertEquals(loss.op.name, 'l1_l2_regularizer')
+      self.assertAlmostEqual(loss.eval(), num_elem / 2, 5)
+
+  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.test_session():
+      self.assertEquals(loss.op.name, 'l1_l2_regularizer')
+      self.assertAlmostEqual(loss.eval(), num_elem, 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)
+
   def testL1L2RegularizerWithScope(self):
     with self.test_session():
       shape = [5, 5, 5]