Make regularizers API more consistent.

PiperOrigin-RevId: 311403808
Change-Id: I2a372937bdc316f742015be6080ad945bf970377

tensorflow/python/keras/layers/serialization_test.py

@@ -53,7 +53,7 @@ class LayerSerializationTest(parameterized.TestCase, test.TestCase):
     new_layer = keras.layers.deserialize(config)
     self.assertEqual(new_layer.activation, keras.activations.relu)
     self.assertEqual(new_layer.bias_regularizer.__class__,
-                     keras.regularizers.L1L2)
+                     keras.regularizers.L2)
     if tf2.enabled():
       self.assertEqual(new_layer.kernel_initializer.__class__,
                        keras.initializers.OnesV2)
@@ -88,7 +88,7 @@ class LayerSerializationTest(parameterized.TestCase, test.TestCase):
         config, custom_objects={'SerializableInt': SerializableInt})
     self.assertEqual(new_layer.activation, keras.activations.relu)
     self.assertEqual(new_layer.bias_regularizer.__class__,
-                     keras.regularizers.L1L2)
+                     keras.regularizers.L2)
     if tf2.enabled():
       self.assertEqual(new_layer.kernel_initializer.__class__,
                        keras.initializers.OnesV2)
@@ -116,7 +116,7 @@ class LayerSerializationTest(parameterized.TestCase, test.TestCase):
       self.assertEqual(new_layer.beta_initializer.__class__,
                        keras.initializers.Zeros)
     self.assertEqual(new_layer.gamma_regularizer.__class__,
-                     keras.regularizers.L1L2)
+                     keras.regularizers.L2)
 
   @parameterized.parameters(
       [batchnorm_v1.BatchNormalization, batchnorm_v2.BatchNormalization])
@@ -135,7 +135,7 @@ class LayerSerializationTest(parameterized.TestCase, test.TestCase):
       self.assertEqual(new_layer.beta_initializer.__class__,
                        keras.initializers.Zeros)
     self.assertEqual(new_layer.gamma_regularizer.__class__,
-                     keras.regularizers.L1L2)
+                     keras.regularizers.L2)
 
   @parameterized.parameters([rnn_v1.LSTM, rnn_v2.LSTM])
   def test_serialize_deserialize_lstm(self, layer):

tensorflow/python/keras/regularizers.py

@@ -14,13 +14,14 @@
 # ==============================================================================
 """Built-in regularizers.
 """
+# pylint: disable=invalid-name
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
 import six
 
-from tensorflow.python.keras import backend as K
+from tensorflow.python.keras import backend
 from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
 from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
 from tensorflow.python.ops import math_ops
@@ -60,8 +61,8 @@ class Regularizer(object):
   >>> layer = tf.keras.layers.Dense(
   ...     5, input_dim=5,
   ...     kernel_initializer='ones',
-  ...     kernel_regularizer=tf.keras.regularizers.l1(0.01),
+  ...     kernel_regularizer=tf.keras.regularizers.L1(0.01),
-  ...     activity_regularizer=tf.keras.regularizers.l2(0.01))
+  ...     activity_regularizer=tf.keras.regularizers.L2(0.01))
   >>> tensor = tf.ones(shape=(5, 5)) * 2.0
   >>> out = layer(tensor)
 
@@ -73,9 +74,9 @@ class Regularizer(object):
   ## Available penalties
 
   ```python
-  tf.keras.regularizers.l1(0.3)  # L1 Regularization Penalty
+  tf.keras.regularizers.L1(0.3)  # L1 Regularization Penalty
-  tf.keras.regularizers.l2(0.1)  # L2 Regularization Penalty
+  tf.keras.regularizers.L2(0.1)  # L2 Regularization Penalty
-  tf.keras.regularizers.l1_l2(l1=0.01, l2=0.01)  # L1 + L2 penalties
+  tf.keras.regularizers.L1L2(l1=0.01, l2=0.01)  # L1 + L2 penalties
   ```
 
   ## Directly calling a regularizer
@@ -84,7 +85,7 @@ class Regularizer(object):
   as if it is a one-argument function.
 
   E.g.
-  >>> regularizer = tf.keras.regularizers.l2(2.)
+  >>> regularizer = tf.keras.regularizers.L2(2.)
   >>> tensor = tf.ones(shape=(5, 5))
   >>> regularizer(tensor)
   <tf.Tensor: shape=(), dtype=float32, numpy=50.0>
@@ -194,7 +195,7 @@ class Regularizer(object):
 
 @keras_export('keras.regularizers.L1L2')
 class L1L2(Regularizer):
-  r"""A regularizer that applies both L1 and L2 regularization penalties.
+  """A regularizer that applies both L1 and L2 regularization penalties.
 
   The L1 regularization penalty is computed as:
   `loss = l1 * reduce_sum(abs(x))`
@@ -202,19 +203,23 @@ class L1L2(Regularizer):
   The L2 regularization penalty is computed as
   `loss = l2 * reduce_sum(square(x))`
 
+  L1L2 may be passed to a layer as a string identifier:
+
+  >>> dense = tf.keras.layers.Dense(3, kernel_regularizer='l1_l2')
+
+  In this case, the default values used are `l1=0.01` and `l2=0.01`.
+
   Attributes:
       l1: Float; L1 regularization factor.
       l2: Float; L2 regularization factor.
   """
 
   def __init__(self, l1=0., l2=0.):  # pylint: disable=redefined-outer-name
-    self.l1 = K.cast_to_floatx(l1)
+    self.l1 = backend.cast_to_floatx(l1)
-    self.l2 = K.cast_to_floatx(l2)
+    self.l2 = backend.cast_to_floatx(l2)
 
   def __call__(self, x):
-    if not self.l1 and not self.l2:
+    regularization = backend.constant(0., dtype=x.dtype)
-      return K.constant(0.)
-    regularization = 0.
     if self.l1:
       regularization += self.l1 * math_ops.reduce_sum(math_ops.abs(x))
     if self.l2:
@@ -225,39 +230,64 @@ class L1L2(Regularizer):
     return {'l1': float(self.l1), 'l2': float(self.l2)}
 
 
-# Aliases.
+@keras_export('keras.regularizers.L1', 'keras.regularizers.l1')
-
+class L1(Regularizer):
-
+  """A regularizer that applies a L1 regularization penalty.
-@keras_export('keras.regularizers.l1')
-def l1(l=0.01):
-  r"""Create a regularizer that applies an L1 regularization penalty.
 
   The L1 regularization penalty is computed as:
-  `loss = l * reduce_sum(abs(x))`
+  `loss = l1 * reduce_sum(abs(x))`
 
-  Arguments:
+  L1 may be passed to a layer as a string identifier:
-      l: Float; L1 regularization factor.
 
-  Returns:
+  >>> dense = tf.keras.layers.Dense(3, kernel_regularizer='l1')
-    An L1 Regularizer with the given regularization factor.
+
+  In this case, the default value used is `l1=0.01`.
+
+  Attributes:
+      l1: Float; L1 regularization factor.
   """
-  return L1L2(l1=l)
+
+  def __init__(self, l1=0.01, **kwargs):  # pylint: disable=redefined-outer-name
+    l1 = kwargs.pop('l', l1)  # Backwards compatibility
+    if kwargs:
+      raise TypeError('Argument(s) not recognized: %s' % (kwargs,))
+    self.l1 = backend.cast_to_floatx(l1)
+
+  def __call__(self, x):
+    return self.l1 * math_ops.reduce_sum(math_ops.abs(x))
+
+  def get_config(self):
+    return {'l1': float(self.l1)}
 
 
-@keras_export('keras.regularizers.l2')
+@keras_export('keras.regularizers.L2', 'keras.regularizers.l2')
-def l2(l=0.01):
+class L2(Regularizer):
-  r"""Create a regularizer that applies an L2 regularization penalty.
+  """A regularizer that applies a L2 regularization penalty.
 
   The L2 regularization penalty is computed as:
-  `loss = l * reduce_sum(square(x))`
+  `loss = l2 * reduce_sum(square(x))`
 
-  Arguments:
+  L2 may be passed to a layer as a string identifier:
-      l: Float; L2 regularization factor.
 
-  Returns:
+  >>> dense = tf.keras.layers.Dense(3, kernel_regularizer='l2')
-    An L2 Regularizer with the given regularization factor.
+
+  In this case, the default value used is `l2=0.01`.
+
+  Attributes:
+      l2: Float; L2 regularization factor.
   """
-  return L1L2(l2=l)
+
+  def __init__(self, l2=0.01, **kwargs):  # pylint: disable=redefined-outer-name
+    l2 = kwargs.pop('l', l2)  # Backwards compatibility
+    if kwargs:
+      raise TypeError('Argument(s) not recognized: %s' % (kwargs,))
+    self.l2 = backend.cast_to_floatx(l2)
+
+  def __call__(self, x):
+    return self.l2 * math_ops.reduce_sum(math_ops.square(x))
+
+  def get_config(self):
+    return {'l2': float(self.l2)}
 
 
 @keras_export('keras.regularizers.l1_l2')
@@ -280,6 +310,11 @@ def l1_l2(l1=0.01, l2=0.01):  # pylint: disable=redefined-outer-name
   return L1L2(l1=l1, l2=l2)
 
 
+# Deserialization aliases.
+l1 = L1
+l2 = L2
+
+
 @keras_export('keras.regularizers.serialize')
 def serialize(regularizer):
   return serialize_keras_object(regularizer)
@@ -287,6 +322,10 @@ def serialize(regularizer):
 
 @keras_export('keras.regularizers.deserialize')
 def deserialize(config, custom_objects=None):
+  if config == 'l1_l2':
+    # Special case necessary since the defaults used for "l1_l2" (string)
+    # differ from those of the L1L2 class.
+    return L1L2(l1=0.01, l2=0.01)
   return deserialize_keras_object(
       config,
       module_objects=globals(),
@@ -296,18 +335,12 @@ def deserialize(config, custom_objects=None):
 
 @keras_export('keras.regularizers.get')
 def get(identifier):
+  """Retrieve a regularizer instance from a config or identifier."""
   if identifier is None:
     return None
   if isinstance(identifier, dict):
     return deserialize(identifier)
   elif isinstance(identifier, six.string_types):
-    identifier = str(identifier)
-    # We have to special-case functions that return classes.
-    # TODO(omalleyt): Turn these into classes or class aliases.
-    special_cases = ['l1', 'l2', 'l1_l2']
-    if identifier in special_cases:
-      # Treat like a class.
-      return deserialize({'class_name': identifier, 'config': {}})
     return deserialize(str(identifier))
   elif callable(identifier):
     return identifier

tensorflow/python/keras/tests/add_loss_correctness_test.py

@@ -288,7 +288,7 @@ class TestAddLossCorrectness(keras_parameterized.TestCase):
           model_layers, input_shape=(10,))
 
       x = np.ones((10, 10), 'float32')
-      y = np.ones((10, 1), 'float32')
+      y = np.zeros((10, 1), 'float32')
 
       optimizer = RMSPropOptimizer(learning_rate=0.001)
       model.compile(

tensorflow/python/keras/utils/generic_utils_test.py

@@ -201,7 +201,7 @@ class SerializeKerasObjectTest(test.TestCase):
         config, custom_objects={'SerializableInt': SerializableInt})
     self.assertEqual(new_layer.activation, keras.activations.relu)
     self.assertEqual(new_layer.bias_regularizer.__class__,
-                     keras.regularizers.L1L2)
+                     keras.regularizers.L2)
     self.assertEqual(new_layer.units.__class__, SerializableInt)
     self.assertEqual(new_layer.units, 3)
 
@@ -253,7 +253,7 @@ class SerializeKerasObjectTest(test.TestCase):
     self.assertEqual(new_layer.name, 'SerializableNestedInt')
     self.assertEqual(new_layer.activation, keras.activations.relu)
     self.assertEqual(new_layer.bias_regularizer.__class__,
-                     keras.regularizers.L1L2)
+                     keras.regularizers.L2)
     self.assertEqual(new_layer.units.__class__, SerializableNestedInt)
     self.assertEqual(new_layer.units, 3)
     self.assertEqual(new_layer.units.int_obj.__class__, SerializableInt)
@@ -293,7 +293,7 @@ class SerializeKerasObjectTest(test.TestCase):
             'SerializableNestedInt': SerializableNestedInt
         })
     self.assertEqual(new_layer.activation, keras.activations.relu)
-    self.assertIsInstance(new_layer.bias_regularizer, keras.regularizers.L1L2)
+    self.assertIsInstance(new_layer.bias_regularizer, keras.regularizers.L2)
     self.assertIsInstance(new_layer.units, SerializableNestedInt)
     self.assertEqual(new_layer.units, 3)
     self.assertIs(new_layer.units.fn, serializable_fn)

tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.-l1.pbtxt

@@ -0,0 +1,18 @@
+path: "tensorflow.keras.regularizers.L1"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.regularizers.L1\'>"
+  is_instance: "<class \'tensorflow.python.keras.regularizers.Regularizer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'l1\'], varargs=None, keywords=kwargs, defaults=[\'0.01\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.-l2.pbtxt

@@ -0,0 +1,18 @@
+path: "tensorflow.keras.regularizers.L2"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.regularizers.L2\'>"
+  is_instance: "<class \'tensorflow.python.keras.regularizers.Regularizer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'l2\'], varargs=None, keywords=kwargs, defaults=[\'0.01\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.l1.pbtxt

@@ -0,0 +1,18 @@
+path: "tensorflow.keras.regularizers.l1"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.regularizers.L1\'>"
+  is_instance: "<class \'tensorflow.python.keras.regularizers.Regularizer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'l1\'], varargs=None, keywords=kwargs, defaults=[\'0.01\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.l2.pbtxt

@@ -0,0 +1,18 @@
+path: "tensorflow.keras.regularizers.l2"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.regularizers.L2\'>"
+  is_instance: "<class \'tensorflow.python.keras.regularizers.Regularizer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'l2\'], varargs=None, keywords=kwargs, defaults=[\'0.01\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v1/tensorflow.keras.regularizers.pbtxt

@@ -1,13 +1,29 @@
 path: "tensorflow.keras.regularizers"
 tf_module {
+  member {
+    name: "L1"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "L1L2"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "L2"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Regularizer"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "l1"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "l2"
+    mtype: "<type \'type\'>"
+  }
   member_method {
     name: "deserialize"
     argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
@@ -16,18 +32,10 @@ tf_module {
     name: "get"
     argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "l1"
-    argspec: "args=[\'l\'], varargs=None, keywords=None, defaults=[\'0.01\'], "
-  }
   member_method {
     name: "l1_l2"
     argspec: "args=[\'l1\', \'l2\'], varargs=None, keywords=None, defaults=[\'0.01\', \'0.01\'], "
   }
-  member_method {
-    name: "l2"
-    argspec: "args=[\'l\'], varargs=None, keywords=None, defaults=[\'0.01\'], "
-  }
   member_method {
     name: "serialize"
     argspec: "args=[\'regularizer\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.-l1.pbtxt

@@ -0,0 +1,18 @@
+path: "tensorflow.keras.regularizers.L1"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.regularizers.L1\'>"
+  is_instance: "<class \'tensorflow.python.keras.regularizers.Regularizer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'l1\'], varargs=None, keywords=kwargs, defaults=[\'0.01\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.-l2.pbtxt

@@ -0,0 +1,18 @@
+path: "tensorflow.keras.regularizers.L2"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.regularizers.L2\'>"
+  is_instance: "<class \'tensorflow.python.keras.regularizers.Regularizer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'l2\'], varargs=None, keywords=kwargs, defaults=[\'0.01\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.l1.pbtxt

@@ -0,0 +1,18 @@
+path: "tensorflow.keras.regularizers.l1"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.regularizers.L1\'>"
+  is_instance: "<class \'tensorflow.python.keras.regularizers.Regularizer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'l1\'], varargs=None, keywords=kwargs, defaults=[\'0.01\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.l2.pbtxt

@@ -0,0 +1,18 @@
+path: "tensorflow.keras.regularizers.l2"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras.regularizers.L2\'>"
+  is_instance: "<class \'tensorflow.python.keras.regularizers.Regularizer\'>"
+  is_instance: "<type \'object\'>"
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'l2\'], varargs=None, keywords=kwargs, defaults=[\'0.01\'], "
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/v2/tensorflow.keras.regularizers.pbtxt

@@ -1,13 +1,29 @@
 path: "tensorflow.keras.regularizers"
 tf_module {
+  member {
+    name: "L1"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "L1L2"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "L2"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Regularizer"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "l1"
+    mtype: "<type \'type\'>"
+  }
+  member {
+    name: "l2"
+    mtype: "<type \'type\'>"
+  }
   member_method {
     name: "deserialize"
     argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
@@ -16,18 +32,10 @@ tf_module {
     name: "get"
     argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "l1"
-    argspec: "args=[\'l\'], varargs=None, keywords=None, defaults=[\'0.01\'], "
-  }
   member_method {
     name: "l1_l2"
     argspec: "args=[\'l1\', \'l2\'], varargs=None, keywords=None, defaults=[\'0.01\', \'0.01\'], "
   }
-  member_method {
-    name: "l2"
-    argspec: "args=[\'l\'], varargs=None, keywords=None, defaults=[\'0.01\'], "
-  }
   member_method {
     name: "serialize"
     argspec: "args=[\'regularizer\'], varargs=None, keywords=None, defaults=None"