Loose the check for regularizer and convert None to default value.

PiperOrigin-RevId: 319092652
Change-Id: If75a7d3474290f510d92fd6ebcfaf371f5fe6fcb

tensorflow/python/keras/regularizers.py

@@ -31,7 +31,7 @@ from tensorflow.python.util.tf_export import keras_export
 
 
 def _check_penalty_number(x):
-  """check penalty number availability, raise ValueError if failed"""
+  """check penalty number availability, raise ValueError if failed."""
   if not isinstance(x, (float, int)):
     raise ValueError(('Value: {} is not a valid regularization penalty number, '
                       'expected an int or float value').format(x))
@@ -43,6 +43,10 @@ def _check_penalty_number(x):
         ).format(x))
 
 
+def _none_to_default(inputs, default):
+  return default if inputs is None else default
+
+
 @keras_export('keras.regularizers.Regularizer')
 class Regularizer(object):
   """Regularizer base class.
@@ -230,6 +234,11 @@ class L1L2(Regularizer):
   """
 
   def __init__(self, l1=0., l2=0.):  # pylint: disable=redefined-outer-name
+    # The default value for l1 and l2 are different from the value in l1_l2
+    # for backward compatiblity reason. Eg, L1L2(l2=0.1) will only have l2
+    # and no l1 penalty.
+    l1 = 0. if l1 is None else l1
+    l2 = 0. if l2 is None else l2
     _check_penalty_number(l1)
     _check_penalty_number(l2)
 
@@ -270,6 +279,7 @@ class L1(Regularizer):
     if kwargs:
       raise TypeError('Argument(s) not recognized: %s' % (kwargs,))
 
+    l1 = 0.01 if l1 is None else l1
     _check_penalty_number(l1)
 
     self.l1 = backend.cast_to_floatx(l1)
@@ -303,6 +313,7 @@ class L2(Regularizer):
     if kwargs:
       raise TypeError('Argument(s) not recognized: %s' % (kwargs,))
 
+    l2 = 0.01 if l2 is None else l2
     _check_penalty_number(l2)
 
     self.l2 = backend.cast_to_floatx(l2)

tensorflow/python/keras/regularizers_test.py

@@ -203,6 +203,18 @@ class KerasRegularizersTest(keras_parameterized.TestCase,
     with self.assertRaisesRegex(ValueError, 'Could not interpret regularizer'):
       keras.regularizers.get(0)
 
+  @parameterized.named_parameters([
+      ('l1', regularizers.l1(l1=None), 0.01),
+      ('l2', regularizers.l2(l2=None), 0.01),
+      ('l1_l2', regularizers.l1_l2(l1=None, l2=None), 0.),
+  ])
+  def test_default_value_when_init_with_none(self, regularizer, expected_value):
+    expected_value = np.asarray(expected_value)
+    if hasattr(regularizer, 'l1'):
+      self.assertAllClose(regularizer.l1, expected_value)
+    if hasattr(regularizer, 'l2'):
+      self.assertAllClose(regularizer.l2, expected_value)
+
 
 if __name__ == '__main__':
   test.main()