Update tf.keras RNNs to the Keras 2.0.9 API. Does not include cuDNN layers.

Additionally, fix a bug with handling of activity_regularizer in tf.layers base Layer (and add test).

PiperOrigin-RevId: 175070161

tensorflow/python/keras/BUILD

@@ -498,6 +498,18 @@ py_test(
     ],
 )
 
+py_test(
+    name = "recurrent_test",
+    size = "small",
+    srcs = ["_impl/keras/layers/recurrent_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":keras",
+        "//tensorflow/python:client_testlib",
+        "//third_party/py/numpy",
+    ],
+)
+
 py_test(
     name = "serialization_test",
     size = "small",

tensorflow/python/keras/_impl/keras/engine/topology.py

@@ -29,6 +29,9 @@ from six.moves import zip  # pylint: disable=redefined-builtin
 from tensorflow.python.eager import context
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras._impl.keras import backend as K
+from tensorflow.python.keras._impl.keras import constraints
+from tensorflow.python.keras._impl.keras import initializers
+from tensorflow.python.keras._impl.keras import regularizers
 from tensorflow.python.keras._impl.keras.utils import conv_utils
 from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite
 from tensorflow.python.keras._impl.keras.utils.layer_utils import print_summary as print_layer_summary
@@ -209,9 +212,9 @@ class Layer(tf_base_layers.Layer):
       dtype = K.floatx()
     weight = self.add_variable(name, shape,
                                dtype=dtype,
-                               initializer=initializer,
-                               regularizer=regularizer,
-                               constraint=constraint,
+                               initializer=initializers.get(initializer),
+                               regularizer=regularizers.get(regularizer),
+                               constraint=constraints.get(constraint),
                                trainable=trainable)
     return weight
 

tensorflow/python/keras/_impl/keras/integration_test.py

@@ -93,7 +93,7 @@ class KerasIntegrationTest(test.TestCase):
       y_test = keras.utils.to_categorical(y_test)
 
       model = keras.models.Sequential()
-      model.add(keras.layers.LSTM(3, return_sequences=True,
+      model.add(keras.layers.LSTM(5, return_sequences=True,
                                   input_shape=x_train.shape[1:]))
       model.add(keras.layers.GRU(y_train.shape[-1], activation='softmax'))
       model.compile(loss='categorical_crossentropy',

tensorflow/python/keras/_impl/keras/layers/gru_test.py

@@ -156,8 +156,10 @@ class GRULayerTest(test.TestCase):
           activity_regularizer='l1')
       layer.build((None, None, 2))
       self.assertEqual(len(layer.losses), 3)
-      layer(keras.backend.variable(np.ones((2, 3, 2))))
-      self.assertEqual(len(layer.losses), 4)
+
+      x = keras.backend.variable(np.ones((2, 3, 2)))
+      layer(x)
+      self.assertEqual(len(layer.get_losses_for(x)), 1)
 
   def test_constraints_GRU(self):
     embedding_dim = 4
@@ -175,9 +177,9 @@ class GRULayerTest(test.TestCase):
           recurrent_constraint=r_constraint,
           bias_constraint=b_constraint)
       layer.build((None, None, embedding_dim))
-      self.assertEqual(layer.kernel.constraint, k_constraint)
-      self.assertEqual(layer.recurrent_kernel.constraint, r_constraint)
-      self.assertEqual(layer.bias.constraint, b_constraint)
+      self.assertEqual(layer.cell.kernel.constraint, k_constraint)
+      self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
+      self.assertEqual(layer.cell.bias.constraint, b_constraint)
 
   def test_with_masking_layer_GRU(self):
     layer_class = keras.layers.GRU

tensorflow/python/keras/_impl/keras/layers/lstm_test.py

@@ -156,8 +156,9 @@ class LSTMLayerTest(test.TestCase):
           activity_regularizer='l1')
       layer.build((None, None, 2))
       self.assertEqual(len(layer.losses), 3)
-      layer(keras.backend.variable(np.ones((2, 3, 2))))
-      self.assertEqual(len(layer.losses), 4)
+      x = keras.backend.variable(np.ones((2, 3, 2)))
+      layer(x)
+      self.assertEqual(len(layer.get_losses_for(x)), 1)
 
   def test_constraints_LSTM(self):
     embedding_dim = 4
@@ -175,9 +176,9 @@ class LSTMLayerTest(test.TestCase):
           recurrent_constraint=r_constraint,
           bias_constraint=b_constraint)
       layer.build((None, None, embedding_dim))
-      self.assertEqual(layer.kernel.constraint, k_constraint)
-      self.assertEqual(layer.recurrent_kernel.constraint, r_constraint)
-      self.assertEqual(layer.bias.constraint, b_constraint)
+      self.assertEqual(layer.cell.kernel.constraint, k_constraint)
+      self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
+      self.assertEqual(layer.cell.bias.constraint, b_constraint)
 
   def test_with_masking_layer_LSTM(self):
     layer_class = keras.layers.LSTM

tensorflow/python/keras/_impl/keras/layers/recurrent_test.py

@@ -0,0 +1,378 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for recurrent layers functionality other than GRU, LSTM, SimpleRNN.
+
+See also: lstm_test.py, gru_test.py, simplernn_test.py.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.python.keras._impl import keras
+from tensorflow.python.platform import test
+
+
+class RNNTest(test.TestCase):
+
+  def test_minimal_rnn_cell_non_layer(self):
+
+    class MinimalRNNCell(object):
+
+      def __init__(self, units, input_dim):
+        self.units = units
+        self.state_size = units
+        self.kernel = keras.backend.variable(
+            np.random.random((input_dim, units)))
+
+      def call(self, inputs, states):
+        prev_output = states[0]
+        output = keras.backend.dot(inputs, self.kernel) + prev_output
+        return output, [output]
+
+    with self.test_session():
+      # Basic test case.
+      cell = MinimalRNNCell(32, 5)
+      x = keras.Input((None, 5))
+      layer = keras.layers.RNN(cell)
+      y = layer(x)
+      model = keras.models.Model(x, y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
+
+      # Test stacking.
+      cells = [MinimalRNNCell(8, 5),
+               MinimalRNNCell(32, 8),
+               MinimalRNNCell(32, 32)]
+      layer = keras.layers.RNN(cells)
+      y = layer(x)
+      model = keras.models.Model(x, y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
+
+  def test_minimal_rnn_cell_non_layer_multiple_states(self):
+
+    class MinimalRNNCell(object):
+
+      def __init__(self, units, input_dim):
+        self.units = units
+        self.state_size = (units, units)
+        self.kernel = keras.backend.variable(
+            np.random.random((input_dim, units)))
+
+      def call(self, inputs, states):
+        prev_output_1 = states[0]
+        prev_output_2 = states[1]
+        output = keras.backend.dot(inputs, self.kernel)
+        output += prev_output_1
+        output -= prev_output_2
+        return output, [output * 2, output * 3]
+
+    with self.test_session():
+      # Basic test case.
+      cell = MinimalRNNCell(32, 5)
+      x = keras.Input((None, 5))
+      layer = keras.layers.RNN(cell)
+      y = layer(x)
+      model = keras.models.Model(x, y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
+
+      # Test stacking.
+      cells = [MinimalRNNCell(8, 5),
+               MinimalRNNCell(16, 8),
+               MinimalRNNCell(32, 16)]
+      layer = keras.layers.RNN(cells)
+      assert layer.cell.state_size == (32, 32, 16, 16, 8, 8)
+      y = layer(x)
+      model = keras.models.Model(x, y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
+
+  def test_minimal_rnn_cell_layer(self):
+
+    class MinimalRNNCell(keras.layers.Layer):
+
+      def __init__(self, units, **kwargs):
+        self.units = units
+        self.state_size = units
+        super(MinimalRNNCell, self).__init__(**kwargs)
+
+      def build(self, input_shape):
+        self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
+                                      initializer='uniform',
+                                      name='kernel')
+        self.recurrent_kernel = self.add_weight(
+            shape=(self.units, self.units),
+            initializer='uniform',
+            name='recurrent_kernel')
+        self.built = True
+
+      def call(self, inputs, states):
+        prev_output = states[0]
+        h = keras.backend.dot(inputs, self.kernel)
+        output = h + keras.backend.dot(prev_output, self.recurrent_kernel)
+        return output, [output]
+
+      def get_config(self):
+        config = {'units': self.units}
+        base_config = super(MinimalRNNCell, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    with self.test_session():
+      # Test basic case.
+      x = keras.Input((None, 5))
+      cell = MinimalRNNCell(32)
+      layer = keras.layers.RNN(cell)
+      y = layer(x)
+      model = keras.models.Model(x, y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
+
+      # Test basic case serialization.
+      x_np = np.random.random((6, 5, 5))
+      y_np = model.predict(x_np)
+      weights = model.get_weights()
+      config = layer.get_config()
+      with keras.utils.CustomObjectScope({'MinimalRNNCell': MinimalRNNCell}):
+        layer = keras.layers.RNN.from_config(config)
+      y = layer(x)
+      model = keras.models.Model(x, y)
+      model.set_weights(weights)
+      y_np_2 = model.predict(x_np)
+      self.assertAllClose(y_np, y_np_2, atol=1e-4)
+
+      # Test stacking.
+      cells = [MinimalRNNCell(8),
+               MinimalRNNCell(12),
+               MinimalRNNCell(32)]
+      layer = keras.layers.RNN(cells)
+      y = layer(x)
+      model = keras.models.Model(x, y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(np.zeros((6, 5, 5)), np.zeros((6, 32)))
+
+      # Test stacked RNN serialization.
+      x_np = np.random.random((6, 5, 5))
+      y_np = model.predict(x_np)
+      weights = model.get_weights()
+      config = layer.get_config()
+      with keras.utils.CustomObjectScope({'MinimalRNNCell': MinimalRNNCell}):
+        layer = keras.layers.RNN.from_config(config)
+      y = layer(x)
+      model = keras.models.Model(x, y)
+      model.set_weights(weights)
+      y_np_2 = model.predict(x_np)
+      self.assertAllClose(y_np, y_np_2, atol=1e-4)
+
+  def test_rnn_cell_with_constants_layer(self):
+
+    class RNNCellWithConstants(keras.layers.Layer):
+
+      def __init__(self, units, **kwargs):
+        self.units = units
+        self.state_size = units
+        super(RNNCellWithConstants, self).__init__(**kwargs)
+
+      def build(self, input_shape):
+        if not isinstance(input_shape, list):
+          raise TypeError('expects constants shape')
+        [input_shape, constant_shape] = input_shape
+        # will (and should) raise if more than one constant passed
+
+        self.input_kernel = self.add_weight(
+            shape=(input_shape[-1], self.units),
+            initializer='uniform',
+            name='kernel')
+        self.recurrent_kernel = self.add_weight(
+            shape=(self.units, self.units),
+            initializer='uniform',
+            name='recurrent_kernel')
+        self.constant_kernel = self.add_weight(
+            shape=(constant_shape[-1], self.units),
+            initializer='uniform',
+            name='constant_kernel')
+        self.built = True
+
+      def call(self, inputs, states, constants):
+        [prev_output] = states
+        [constant] = constants
+        h_input = keras.backend.dot(inputs, self.input_kernel)
+        h_state = keras.backend.dot(prev_output, self.recurrent_kernel)
+        h_const = keras.backend.dot(constant, self.constant_kernel)
+        output = h_input + h_state + h_const
+        return output, [output]
+
+      def get_config(self):
+        config = {'units': self.units}
+        base_config = super(RNNCellWithConstants, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    with self.test_session():
+      # Test basic case.
+      x = keras.Input((None, 5))
+      c = keras.Input((3,))
+      cell = RNNCellWithConstants(32)
+      layer = keras.layers.RNN(cell)
+      y = layer(x, constants=c)
+      model = keras.models.Model([x, c], y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(
+          [np.zeros((6, 5, 5)), np.zeros((6, 3))],
+          np.zeros((6, 32))
+      )
+
+    with self.test_session():
+      # Test basic case serialization.
+      x_np = np.random.random((6, 5, 5))
+      c_np = np.random.random((6, 3))
+      y_np = model.predict([x_np, c_np])
+      weights = model.get_weights()
+      config = layer.get_config()
+      custom_objects = {'RNNCellWithConstants': RNNCellWithConstants}
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.RNN.from_config(config.copy())
+      y = layer(x, constants=c)
+      model = keras.models.Model([x, c], y)
+      model.set_weights(weights)
+      y_np_2 = model.predict([x_np, c_np])
+      self.assertAllClose(y_np, y_np_2, atol=1e-4)
+
+    with self.test_session():
+      # test flat list inputs
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.RNN.from_config(config.copy())
+      y = layer([x, c])
+      model = keras.models.Model([x, c], y)
+      model.set_weights(weights)
+      y_np_3 = model.predict([x_np, c_np])
+      self.assertAllClose(y_np, y_np_3, atol=1e-4)
+
+  def test_rnn_cell_with_constants_layer_passing_initial_state(self):
+
+    class RNNCellWithConstants(keras.layers.Layer):
+
+      def __init__(self, units, **kwargs):
+        self.units = units
+        self.state_size = units
+        super(RNNCellWithConstants, self).__init__(**kwargs)
+
+      def build(self, input_shape):
+        if not isinstance(input_shape, list):
+          raise TypeError('expects constants shape')
+        [input_shape, constant_shape] = input_shape
+        # will (and should) raise if more than one constant passed
+
+        self.input_kernel = self.add_weight(
+            shape=(input_shape[-1], self.units),
+            initializer='uniform',
+            name='kernel')
+        self.recurrent_kernel = self.add_weight(
+            shape=(self.units, self.units),
+            initializer='uniform',
+            name='recurrent_kernel')
+        self.constant_kernel = self.add_weight(
+            shape=(constant_shape[-1], self.units),
+            initializer='uniform',
+            name='constant_kernel')
+        self.built = True
+
+      def call(self, inputs, states, constants):
+        [prev_output] = states
+        [constant] = constants
+        h_input = keras.backend.dot(inputs, self.input_kernel)
+        h_state = keras.backend.dot(prev_output, self.recurrent_kernel)
+        h_const = keras.backend.dot(constant, self.constant_kernel)
+        output = h_input + h_state + h_const
+        return output, [output]
+
+      def get_config(self):
+        config = {'units': self.units}
+        base_config = super(RNNCellWithConstants, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    with self.test_session():
+      # Test basic case.
+      x = keras.Input((None, 5))
+      c = keras.Input((3,))
+      s = keras.Input((32,))
+      cell = RNNCellWithConstants(32)
+      layer = keras.layers.RNN(cell)
+      y = layer(x, initial_state=s, constants=c)
+      model = keras.models.Model([x, s, c], y)
+      model.compile(optimizer='rmsprop', loss='mse')
+      model.train_on_batch(
+          [np.zeros((6, 5, 5)), np.zeros((6, 32)), np.zeros((6, 3))],
+          np.zeros((6, 32))
+      )
+
+    with self.test_session():
+      # Test basic case serialization.
+      x_np = np.random.random((6, 5, 5))
+      s_np = np.random.random((6, 32))
+      c_np = np.random.random((6, 3))
+      y_np = model.predict([x_np, s_np, c_np])
+      weights = model.get_weights()
+      config = layer.get_config()
+      custom_objects = {'RNNCellWithConstants': RNNCellWithConstants}
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.RNN.from_config(config.copy())
+      y = layer(x, initial_state=s, constants=c)
+      model = keras.models.Model([x, s, c], y)
+      model.set_weights(weights)
+      y_np_2 = model.predict([x_np, s_np, c_np])
+      self.assertAllClose(y_np, y_np_2, atol=1e-4)
+
+      # verify that state is used
+      y_np_2_different_s = model.predict([x_np, s_np + 10., c_np])
+      with self.assertRaises(AssertionError):
+        self.assertAllClose(y_np, y_np_2_different_s, atol=1e-4)
+
+    with self.test_session():
+      # test flat list inputs
+      with keras.utils.CustomObjectScope(custom_objects):
+        layer = keras.layers.RNN.from_config(config.copy())
+      y = layer([x, s, c])
+      model = keras.models.Model([x, s, c], y)
+      model.set_weights(weights)
+      y_np_3 = model.predict([x_np, s_np, c_np])
+      self.assertAllClose(y_np, y_np_3, atol=1e-4)
+
+  def test_stacked_rnn_attributes(self):
+    cells = [keras.layers.LSTMCell(3),
+             keras.layers.LSTMCell(3, kernel_regularizer='l2')]
+    layer = keras.layers.RNN(cells)
+    layer.build((None, None, 5))
+
+    # Test regularization losses
+    assert len(layer.losses) == 1
+
+    # Test weights
+    assert len(layer.trainable_weights) == 6
+    cells[0].trainable = False
+    assert len(layer.trainable_weights) == 3
+    assert len(layer.non_trainable_weights) == 3
+
+    # Test `get_losses_for`
+    x = keras.Input((None, 5))
+    y = keras.backend.sum(x)
+    cells[0].add_loss(y, inputs=x)
+    assert layer.get_losses_for(x) == [y]
+
+
+if __name__ == '__main__':
+  test.main()

tensorflow/python/keras/_impl/keras/layers/simplernn_test.py

@@ -156,8 +156,10 @@ class SimpleRNNLayerTest(test.TestCase):
           activity_regularizer='l1')
       layer.build((None, None, 2))
       self.assertEqual(len(layer.losses), 3)
-      layer(keras.backend.variable(np.ones((2, 3, 2))))
-      self.assertEqual(len(layer.losses), 4)
+
+      x = keras.backend.variable(np.ones((2, 3, 2)))
+      layer(x)
+      self.assertEqual(len(layer.get_losses_for(x)), 1)
 
   def test_constraints_SimpleRNN(self):
     embedding_dim = 4
@@ -175,9 +177,9 @@ class SimpleRNNLayerTest(test.TestCase):
           recurrent_constraint=r_constraint,
           bias_constraint=b_constraint)
       layer.build((None, None, embedding_dim))
-      self.assertEqual(layer.kernel.constraint, k_constraint)
-      self.assertEqual(layer.recurrent_kernel.constraint, r_constraint)
-      self.assertEqual(layer.bias.constraint, b_constraint)
+      self.assertEqual(layer.cell.kernel.constraint, k_constraint)
+      self.assertEqual(layer.cell.recurrent_kernel.constraint, r_constraint)
+      self.assertEqual(layer.cell.bias.constraint, b_constraint)
 
   def test_with_masking_layer_SimpleRNN(self):
     layer_class = keras.layers.SimpleRNN

tensorflow/python/keras/layers/__init__.py

@@ -134,6 +134,11 @@ from tensorflow.python.keras._impl.keras.layers.pooling import GlobalMaxPool2D
 from tensorflow.python.keras._impl.keras.layers.pooling import GlobalMaxPool3D
 
 # Recurrent layers.
+from tensorflow.python.keras._impl.keras.layers.recurrent import RNN
+from tensorflow.python.keras._impl.keras.layers.recurrent import StackedRNNCells
+from tensorflow.python.keras._impl.keras.layers.recurrent import SimpleRNNCell
+from tensorflow.python.keras._impl.keras.layers.recurrent import GRUCell
+from tensorflow.python.keras._impl.keras.layers.recurrent import LSTMCell
 from tensorflow.python.keras._impl.keras.layers.recurrent import SimpleRNN
 from tensorflow.python.keras._impl.keras.layers.recurrent import GRU
 from tensorflow.python.keras._impl.keras.layers.recurrent import LSTM

tensorflow/python/layers/base.py

@@ -642,7 +642,7 @@ class Layer(object):
             for output in output_list:
               with ops.name_scope('ActivityRegularizer'):
                 activity_regularization = self._activity_regularizer(output)
-              self.add_loss(activity_regularization)
+              self.add_loss(activity_regularization, inputs=inputs)
 
         if not in_deferred_mode:
           # TODO(fchollet): consider how masking will work with deferred mode.

tensorflow/python/layers/base_test.py

@@ -574,6 +574,13 @@ class BaseLayerTest(test.TestCase):
       self.assertEqual(3, result['label'].numpy())
       self.assertEqual(4.0, result['logits'].numpy())
 
+  def testActivityRegularizer(self):
+    regularizer = math_ops.reduce_sum
+    layer = base_layers.Layer(activity_regularizer=regularizer)
+    x = array_ops.placeholder('int32')
+    layer.apply(x)
+    self.assertEqual(len(layer.get_losses_for(x)), 1)
+
 
 class NetworkTest(test.TestCase):
 

tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u-cell.pbtxt

@@ -0,0 +1,179 @@
+path: "tensorflow.keras.layers.GRUCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.GRUCell\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\', \'implementation\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\', \'1\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'states\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  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"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/tensorflow.keras.layers.-g-r-u.pbtxt

@@ -1,14 +1,34 @@
 path: "tensorflow.keras.layers.GRU"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.GRU\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.Recurrent\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
+  member {
+    name: "activation"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "activity_regularizer"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "bias_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dropout"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "dtype"
     mtype: "<type \'property\'>"
@@ -17,6 +37,10 @@ tf_class {
     name: "graph"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "implementation"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "inbound_nodes"
     mtype: "<type \'property\'>"
@@ -33,6 +57,18 @@ tf_class {
     name: "input_shape"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "kernel_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_regularizer"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "losses"
     mtype: "<type \'property\'>"
@@ -65,10 +101,34 @@ tf_class {
     name: "output_shape"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "recurrent_activation"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_dropout"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_regularizer"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "scope_name"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "states"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "trainable_variables"
     mtype: "<type \'property\'>"
@@ -77,10 +137,18 @@ tf_class {
     name: "trainable_weights"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "units"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "updates"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "use_bias"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "variables"
     mtype: "<type \'property\'>"
@@ -91,7 +159,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\'], "
+    argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\', \'implementation\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'unroll\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\', \'1\', \'False\', \'False\', \'False\', \'False\', \'False\'], "
   }
   member_method {
     name: "add_loss"
@@ -137,10 +205,6 @@ tf_class {
     name: "get_config"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_constants"
-    argspec: "args=[\'self\', \'inputs\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "get_initial_state"
     argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
@@ -159,7 +223,7 @@ tf_class {
   }
   member_method {
     name: "get_losses_for"
-    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "get_output_at"
@@ -181,10 +245,6 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "preprocess_input"
-    argspec: "args=[\'self\', \'inputs\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "reset_states"
     argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], "
@@ -193,8 +253,4 @@ tf_class {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "step"
-    argspec: "args=[\'self\', \'inputs\', \'states\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt

@@ -0,0 +1,179 @@
+path: "tensorflow.keras.layers.LSTMCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.LSTMCell\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\', \'implementation\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\', \'1\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'states\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  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"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/tensorflow.keras.layers.-l-s-t-m.pbtxt

@@ -1,14 +1,34 @@
 path: "tensorflow.keras.layers.LSTM"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.LSTM\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.Recurrent\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
+  member {
+    name: "activation"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "activity_regularizer"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "bias_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dropout"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "dtype"
     mtype: "<type \'property\'>"
@@ -17,6 +37,10 @@ tf_class {
     name: "graph"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "implementation"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "inbound_nodes"
     mtype: "<type \'property\'>"
@@ -33,6 +57,18 @@ tf_class {
     name: "input_shape"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "kernel_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_regularizer"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "losses"
     mtype: "<type \'property\'>"
@@ -65,10 +101,34 @@ tf_class {
     name: "output_shape"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "recurrent_activation"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_dropout"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_regularizer"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "scope_name"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "states"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "trainable_variables"
     mtype: "<type \'property\'>"
@@ -77,10 +137,22 @@ tf_class {
     name: "trainable_weights"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "unit_forget_bias"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "units"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "updates"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "use_bias"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "variables"
     mtype: "<type \'property\'>"
@@ -91,7 +163,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\'], "
+    argspec: "args=[\'self\', \'units\', \'activation\', \'recurrent_activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'unit_forget_bias\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\', \'implementation\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'unroll\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'hard_sigmoid\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'True\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\', \'1\', \'False\', \'False\', \'False\', \'False\', \'False\'], "
   }
   member_method {
     name: "add_loss"
@@ -137,10 +209,6 @@ tf_class {
     name: "get_config"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_constants"
-    argspec: "args=[\'self\', \'inputs\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "get_initial_state"
     argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
@@ -159,7 +227,7 @@ tf_class {
   }
   member_method {
     name: "get_losses_for"
-    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "get_output_at"
@@ -181,10 +249,6 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "preprocess_input"
-    argspec: "args=[\'self\', \'inputs\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "reset_states"
     argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], "
@@ -193,8 +257,4 @@ tf_class {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "step"
-    argspec: "args=[\'self\', \'inputs\', \'states\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/tensorflow.keras.layers.-r-n-n.pbtxt

@@ -0,0 +1,191 @@
+path: "tensorflow.keras.layers.RNN"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "states"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'cell\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'unroll\', \'activity_regularizer\'], varargs=None, keywords=kwargs, defaults=[\'False\', \'False\', \'False\', \'False\', \'False\', \'None\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'mask\', \'training\', \'initial_state\', \'constants\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_initial_state"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "reset_states"
+    argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt

@@ -0,0 +1,179 @@
+path: "tensorflow.keras.layers.SimpleRNNCell"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.SimpleRNNCell\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'units\', \'activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\'], "
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'states\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  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"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/tensorflow.keras.layers.-simple-r-n-n.pbtxt

@@ -1,14 +1,34 @@
 path: "tensorflow.keras.layers.SimpleRNN"
 tf_class {
   is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.SimpleRNN\'>"
-  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.Recurrent\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.RNN\'>"
   is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
   is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
   is_instance: "<type \'object\'>"
+  member {
+    name: "activation"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "activity_regularizer"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "bias_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "bias_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dropout"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "dtype"
     mtype: "<type \'property\'>"
@@ -33,6 +53,18 @@ tf_class {
     name: "input_shape"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "kernel_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "kernel_regularizer"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "losses"
     mtype: "<type \'property\'>"
@@ -65,10 +97,30 @@ tf_class {
     name: "output_shape"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "recurrent_constraint"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_dropout"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_initializer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "recurrent_regularizer"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "scope_name"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "states"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "trainable_variables"
     mtype: "<type \'property\'>"
@@ -77,10 +129,18 @@ tf_class {
     name: "trainable_weights"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "units"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "updates"
     mtype: "<type \'property\'>"
   }
+  member {
+    name: "use_bias"
+    mtype: "<type \'property\'>"
+  }
   member {
     name: "variables"
     mtype: "<type \'property\'>"
@@ -91,7 +151,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'units\', \'activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\'], "
+    argspec: "args=[\'self\', \'units\', \'activation\', \'use_bias\', \'kernel_initializer\', \'recurrent_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'recurrent_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'recurrent_constraint\', \'bias_constraint\', \'dropout\', \'recurrent_dropout\', \'return_sequences\', \'return_state\', \'go_backwards\', \'stateful\', \'unroll\'], varargs=None, keywords=kwargs, defaults=[\'tanh\', \'True\', \'glorot_uniform\', \'orthogonal\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'0.0\', \'0.0\', \'False\', \'False\', \'False\', \'False\', \'False\'], "
   }
   member_method {
     name: "add_loss"
@@ -137,10 +197,6 @@ tf_class {
     name: "get_config"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "get_constants"
-    argspec: "args=[\'self\', \'inputs\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "get_initial_state"
     argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
@@ -159,7 +215,7 @@ tf_class {
   }
   member_method {
     name: "get_losses_for"
-    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
   }
   member_method {
     name: "get_output_at"
@@ -181,10 +237,6 @@ tf_class {
     name: "get_weights"
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "preprocess_input"
-    argspec: "args=[\'self\', \'inputs\', \'training\'], varargs=None, keywords=None, defaults=[\'None\'], "
-  }
   member_method {
     name: "reset_states"
     argspec: "args=[\'self\', \'states\'], varargs=None, keywords=None, defaults=[\'None\'], "
@@ -193,8 +245,4 @@ tf_class {
     name: "set_weights"
     argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
   }
-  member_method {
-    name: "step"
-    argspec: "args=[\'self\', \'inputs\', \'states\'], varargs=None, keywords=None, defaults=None"
-  }
 }

tensorflow/tools/api/golden/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt

@@ -0,0 +1,183 @@
+path: "tensorflow.keras.layers.StackedRNNCells"
+tf_class {
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.layers.recurrent.StackedRNNCells\'>"
+  is_instance: "<class \'tensorflow.python.keras._impl.keras.engine.topology.Layer\'>"
+  is_instance: "<class \'tensorflow.python.layers.base.Layer\'>"
+  is_instance: "<type \'object\'>"
+  member {
+    name: "activity_regularizer"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "dtype"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "graph"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "inbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "input_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "losses"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "non_trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "outbound_nodes"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_mask"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "output_shape"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "scope_name"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "state_size"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "trainable_weights"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "updates"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "variables"
+    mtype: "<type \'property\'>"
+  }
+  member {
+    name: "weights"
+    mtype: "<type \'property\'>"
+  }
+  member_method {
+    name: "__init__"
+    argspec: "args=[\'self\', \'cells\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "add_loss"
+    argspec: "args=[\'self\', \'losses\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_update"
+    argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "add_variable"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\', \'None\'], "
+  }
+  member_method {
+    name: "add_weight"
+    argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'True\', \'None\'], "
+  }
+  member_method {
+    name: "apply"
+    argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "build"
+    argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "call"
+    argspec: "args=[\'self\', \'inputs\', \'states\'], varargs=None, keywords=kwargs, defaults=None"
+  }
+  member_method {
+    name: "compute_mask"
+    argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "count_params"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "from_config"
+    argspec: "args=[\'cls\', \'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "get_config"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_input_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_losses_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
+  }
+  member_method {
+    name: "get_output_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_mask_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_output_shape_at"
+    argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_updates_for"
+    argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "get_weights"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
+  member_method {
+    name: "set_weights"
+    argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
+  }
+}

tensorflow/tools/api/golden/tensorflow.keras.layers.pbtxt

@@ -140,6 +140,10 @@ tf_module {
     name: "GRU"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "GRUCell"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "GaussianDropout"
     mtype: "<type \'type\'>"
@@ -208,6 +212,10 @@ tf_module {
     name: "LSTM"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "LSTMCell"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "Lambda"
     mtype: "<type \'type\'>"
@@ -272,6 +280,10 @@ tf_module {
     name: "Permute"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "RNN"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "RepeatVector"
     mtype: "<type \'type\'>"
@@ -292,6 +304,10 @@ tf_module {
     name: "SimpleRNN"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "SimpleRNNCell"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "SpatialDropout1D"
     mtype: "<type \'type\'>"
@@ -304,6 +320,10 @@ tf_module {
     name: "SpatialDropout3D"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "StackedRNNCells"
+    mtype: "<type \'type\'>"
+  }
   member {
     name: "ThresholdedReLU"
     mtype: "<type \'type\'>"

tensorflow/tools/ci_build/ci_sanity.sh

@@ -98,7 +98,8 @@ do_pylint() {
 "^tensorflow/contrib/eager/python/evaluator\.py.*\[E0202.*method-hidden "\
 "^tensorflow/contrib/eager/python/metrics_impl\.py.*\[E0202.*method-hidden "\
 "^tensorflow/python/platform/gfile\.py.*\[E0301.*non-iterator "\
-"^tensorflow/python/keras/_impl/keras/callbacks\.py.*\[E1133.*not-an-iterable"
+"^tensorflow/python/keras/_impl/keras/callbacks\.py.*\[E1133.*not-an-iterable "\
+"^tensorflow/python/keras/_impl/keras/layers/recurrent\.py.*\[E0203.*access-member-before-definition"
 
   echo "ERROR_WHITELIST=\"${ERROR_WHITELIST}\""