Automated rollback of commit fc4504edb1ab419ae59b0ebb9ff8d943beb61117

PiperOrigin-RevId: 209511718

31 files changed, 32 insertions, 509 deletions

diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py
index 65171acfb6..12c82a53f6 100644
--- a/tensorflow/python/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/layers/recurrent.py
@@ -33,6 +33,7 @@ from tensorflow.python.keras.engine.base_layer import Layer
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training.checkpointable import base as checkpointable
@@ -94,27 +95,11 @@ class StackedRNNCells(Layer):
 
   @property
   def output_size(self):
-    if getattr(self.cells[-1], 'output_size', None) is not None:
+    if hasattr(self.cells[-1], 'output_size'):
       return self.cells[-1].output_size
     else:
       return self.state_size[0]
 
-  def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
-    # The init state is in reverse order of cell's initial state since the
-    # state_size is in reverse order. It is flattened into a list also because
-    # the state_size is a flattened list.
-    initial_states = []
-    for cell in self.cells[::-1]:
-      get_initial_state_fn = getattr(cell, 'get_initial_state', None)
-      if get_initial_state_fn:
-        initial_states.append(get_initial_state_fn(
-            inputs=inputs, batch_size=batch_size, dtype=dtype))
-      else:
-        initial_states.append(_generate_zero_filled_state_for_cell(
-            cell, inputs, batch_size, dtype))
-
-    return nest.flatten(initial_states)
-
   def call(self, inputs, states, constants=None, **kwargs):
     # Recover per-cell states.
     nested_states = []
@@ -276,22 +261,6 @@ class RNN(Layer):
               compatible reason, if this attribute is not available for the
               cell, the value will be inferred by the first element of the
               `state_size`.
-          - a `get_initial_state(inputs=None, batch_size=None, dtype=None)`
-              method that creates a tensor meant to be fed to `call()` as the
-              initial state, if user didn't specify any initial state via other
-              means. The returned initial state should be in shape of
-              [batch, cell.state_size]. Cell might choose to create zero filled
-              tensor, or with other values based on the cell implementations.
-              `inputs` is the input tensor to the RNN layer, which should
-              contain the batch size as its shape[0], and also dtype. Note that
-              the shape[0] might be None during the graph construction. Either
-              the `inputs` or the pair of `batch` and `dtype `are provided.
-              `batch` is a scalar tensor that represent the batch size
-              of the input. `dtype` is `tf.dtype` that represent the dtype of
-              the input.
-              For backward compatible reason, if this method is not implemented
-              by the cell, RNN layer will create a zero filled tensors with the
-              size of [batch, cell.state_size].
           In the case that `cell` is a list of RNN cell instances, the cells
           will be stacked on after the other in the RNN, implementing an
           efficient stacked RNN.
@@ -484,7 +453,7 @@ class RNN(Layer):
     else:
       state_size = [self.cell.state_size]
 
-    if getattr(self.cell, 'output_size', None) is not None:
+    if hasattr(self.cell, 'output_size'):
       output_dim = tensor_shape.as_shape(self.cell.output_size).as_list()
     else:
       # Note that state_size[0] could be a tensor_shape or int.
@@ -584,18 +553,26 @@ class RNN(Layer):
       raise validation_error
 
   def get_initial_state(self, inputs):
-    get_initial_state_fn = getattr(self.cell, 'get_initial_state', None)
-    if get_initial_state_fn:
-      init_state = get_initial_state_fn(
-          inputs=inputs, batch_size=None, dtype=None)
+    # build an all-zero tensor of shape (batch, cell.state_size)
+    initial_state = array_ops.zeros_like(inputs)
+    # shape of initial_state = (batch, timesteps, ...)
+    initial_state = math_ops.reduce_sum(
+        initial_state, axis=list(range(1, len(inputs.shape))))
+    # shape of initial_state = (batch,)
+    if _is_multiple_state(self.cell.state_size):
+      states = []
+      for dims in self.cell.state_size:
+        state = initial_state
+        flat_dims = tensor_shape.as_shape(dims).as_list()
+        # reshape the state to (batch, 1, 1, ....) and then expand each state.
+        state = array_ops.reshape(state, [-1,] + [1] * len(flat_dims))
+        states.append(K.tile(state, [1] + flat_dims))
+      return states
     else:
-      init_state = _generate_zero_filled_state(
-          array_ops.shape(inputs)[0], self.cell.state_size, inputs.dtype)
-    # Keras RNN expect the states in a list, even if it's a single state tensor.
-    if not nest.is_sequence(init_state):
-      init_state = [init_state]
-    # Force the state to be a list in case it is a namedtuple eg LSTMStateTuple.
-    return list(init_state)
+      flat_dims = tensor_shape.as_shape(self.cell.state_size).as_list()
+      initial_state = array_ops.reshape(
+          initial_state, [-1] + [1] * len(flat_dims))
+      return [K.tile(initial_state, [1] + flat_dims)]
 
   def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
     inputs, initial_state, constants = _standardize_args(inputs,
@@ -1009,9 +986,6 @@ class SimpleRNNCell(Layer):
         output._uses_learning_phase = True
     return output, [output]
 
-  def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
-    return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype)
-
   def get_config(self):
     config = {
         'units':
@@ -1543,9 +1517,6 @@ class GRUCell(Layer):
     base_config = super(GRUCell, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
-  def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
-    return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype)
-
 
 @tf_export('keras.layers.GRU')
 class GRU(RNN):
@@ -2071,9 +2042,6 @@ class LSTMCell(Layer):
     base_config = super(LSTMCell, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
-  def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
-    return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype)
-
 
 @tf_export('keras.layers.LSTM')
 class LSTM(RNN):
@@ -2386,30 +2354,3 @@ def _is_multiple_state(state_size):
   """Check whether the state_size contains multiple states."""
   return (hasattr(state_size, '__len__') and
           not isinstance(state_size, tensor_shape.TensorShape))
-
-
-def _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype):
-  if inputs is not None:
-    batch_size = array_ops.shape(inputs)[0]
-    dtype = inputs.dtype
-  return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
-
-
-def _generate_zero_filled_state(batch_size_tensor, state_size, dtype):
-  """Generate a zero filled tensor with shape [batch_size, state_size]."""
-  if None in [batch_size_tensor, dtype]:
-    raise ValueError(
-        'batch_size and dtype cannot be None while constructing initial state: '
-        'batch_size={}, dtype={}'.format(batch_size_tensor, dtype))
-  if _is_multiple_state(state_size):
-    states = []
-    for dims in state_size:
-      flat_dims = tensor_shape.as_shape(dims).as_list()
-      init_state_size = [batch_size_tensor] + flat_dims
-      init_state = array_ops.zeros(init_state_size, dtype=dtype)
-      states.append(init_state)
-    return states
-  else:
-    flat_dims = tensor_shape.as_shape(state_size).as_list()
-    init_state_size = [batch_size_tensor] + flat_dims
-    return array_ops.zeros(init_state_size, dtype=dtype)
diff --git a/tensorflow/python/keras/layers/recurrent_test.py b/tensorflow/python/keras/layers/recurrent_test.py
index f14b36e7e1..13bd070528 100644
--- a/tensorflow/python/keras/layers/recurrent_test.py
+++ b/tensorflow/python/keras/layers/recurrent_test.py
@@ -678,23 +678,6 @@ class RNNTest(test.TestCase):
           np.zeros((batch, input_size)))
       self.assertEqual(model.output_shape, (None, input_size))
 
-  def test_get_initial_state(self):
-    cell = keras.layers.SimpleRNNCell(5)
-    with self.assertRaisesRegexp(ValueError,
-                                 'batch_size and dtype cannot be None'):
-      cell.get_initial_state(None, None, None)
-
-    inputs = keras.Input((None, 2, 10))
-    initial_state = cell.get_initial_state(inputs, None, None)
-    self.assertEqual(initial_state.shape.as_list(), [None, 5])
-    self.assertEqual(initial_state.dtype, inputs.dtype)
-
-    batch = array_ops.shape(inputs)[0]
-    dtype = inputs.dtype
-    initial_state = cell.get_initial_state(None, batch, dtype)
-    self.assertEqual(initial_state.shape.as_list(), [None, 5])
-    self.assertEqual(initial_state.dtype, inputs.dtype)
-
 
 class Minimal2DRNNCell(keras.layers.Layer):
   """The minimal 2D RNN cell is a simple combination of 2 1-D RNN cell.
diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py
index c4f200a22e..c72ada11da 100644
--- a/tensorflow/python/kernel_tests/rnn_test.py
+++ b/tensorflow/python/kernel_tests/rnn_test.py
@@ -35,7 +35,6 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops as ops_lib
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util
-from tensorflow.python.keras import testing_utils
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gradients_impl
@@ -45,13 +44,11 @@ from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import tensor_array_ops
 from tensorflow.python.ops import variables as variables_lib
 import tensorflow.python.ops.data_flow_grad  # pylint: disable=unused-import
-from tensorflow.python.ops.losses import losses
 import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
 import tensorflow.python.ops.sparse_grad  # pylint: disable=unused-import
 import tensorflow.python.ops.tensor_array_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
 from tensorflow.python.training import saver
-from tensorflow.python.training import training
 
 
 class Plus1RNNCell(rnn_cell_impl.RNNCell):
@@ -253,44 +250,12 @@ class RNNTest(test.TestCase):
     self.assertAllEqual(4, state[0])
     self.assertAllEqual([[[1]], [[2]], [[3]], [[4]]], state[1])
 
-  def testCellGetInitialState(self):
-    cell = rnn_cell_impl.BasicRNNCell(5)
-    with self.assertRaisesRegexp(
-        ValueError, "batch_size and dtype cannot be None"):
-      cell.get_initial_state(None, None, None)
-
-    inputs = array_ops.placeholder(dtypes.float32, shape=(None, 4, 1))
-    with self.assertRaisesRegexp(
-        ValueError, "batch size from input tensor is different from"):
-      cell.get_initial_state(inputs=inputs, batch_size=50, dtype=None)
-
-    with self.assertRaisesRegexp(
-        ValueError, "batch size from input tensor is different from"):
-      cell.get_initial_state(
-          inputs=inputs, batch_size=constant_op.constant(50), dtype=None)
-
-    with self.assertRaisesRegexp(
-        ValueError, "dtype from input tensor is different from"):
-      cell.get_initial_state(inputs=inputs, batch_size=None, dtype=dtypes.int16)
-
-    initial_state = cell.get_initial_state(
-        inputs=inputs, batch_size=None, dtype=None)
-    self.assertEqual(initial_state.shape.as_list(), [None, 5])
-    self.assertEqual(initial_state.dtype, inputs.dtype)
-
-    batch = array_ops.shape(inputs)[0]
-    dtype = inputs.dtype
-    initial_state = cell.get_initial_state(None, batch, dtype)
-    self.assertEqual(initial_state.shape.as_list(), [None, 5])
-    self.assertEqual(initial_state.dtype, inputs.dtype)
-
   def _assert_cell_builds(self, cell_class, dtype, batch_size, in_size,
                           out_size):
     cell = cell_class(out_size, dtype=dtype)
     in_shape = tensor_shape.TensorShape((batch_size, in_size))
     cell.build(in_shape)
-    state_output = cell.get_initial_state(
-        inputs=None, batch_size=batch_size, dtype=dtype)
+    state_output = cell.zero_state(batch_size, dtype)
     cell_output, _ = cell(array_ops.zeros(in_shape, dtype), state_output)
     self.assertAllEqual([batch_size, out_size], cell_output.shape.as_list())
 
@@ -313,228 +278,12 @@ class RNNTest(test.TestCase):
     self._assert_cell_builds(contrib_rnn.IndyLSTMCell, f32, 5, 7, 3)
     self._assert_cell_builds(contrib_rnn.IndyLSTMCell, f64, 5, 7, 3)
 
-  def testRNNWithKerasSimpleRNNCell(self):
-    with self.test_session() as sess:
-      input_shape = 10
-      output_shape = 5
-      timestep = 4
-      batch = 100
-      (x_train, y_train), _ = testing_utils.get_test_data(
-          train_samples=batch,
-          test_samples=0,
-          input_shape=(timestep, input_shape),
-          num_classes=output_shape)
-      y_train = keras.utils.to_categorical(y_train)
-      cell = keras.layers.SimpleRNNCell(output_shape)
-
-      inputs = array_ops.placeholder(
-          dtypes.float32, shape=(None, timestep, input_shape))
-      predict = array_ops.placeholder(
-          dtypes.float32, shape=(None, output_shape))
-
-      outputs, state = rnn.dynamic_rnn(
-          cell, inputs, dtype=dtypes.float32)
-      self.assertEqual(outputs.shape.as_list(), [None, timestep, output_shape])
-      self.assertEqual(state.shape.as_list(), [None, output_shape])
-      loss = losses.softmax_cross_entropy(predict, state)
-      train_op = training.GradientDescentOptimizer(0.001).minimize(loss)
-
-      sess.run([variables_lib.global_variables_initializer()])
-      _, outputs, state = sess.run(
-          [train_op, outputs, state], {inputs: x_train, predict: y_train})
-
-      self.assertEqual(len(outputs), batch)
-      self.assertEqual(len(state), batch)
-
-  def testRNNWithKerasGRUCell(self):
-    with self.test_session() as sess:
-      input_shape = 10
-      output_shape = 5
-      timestep = 4
-      batch = 100
-      (x_train, y_train), _ = testing_utils.get_test_data(
-          train_samples=batch,
-          test_samples=0,
-          input_shape=(timestep, input_shape),
-          num_classes=output_shape)
-      y_train = keras.utils.to_categorical(y_train)
-      cell = keras.layers.GRUCell(output_shape)
-
-      inputs = array_ops.placeholder(
-          dtypes.float32, shape=(None, timestep, input_shape))
-      predict = array_ops.placeholder(
-          dtypes.float32, shape=(None, output_shape))
-
-      outputs, state = rnn.dynamic_rnn(
-          cell, inputs, dtype=dtypes.float32)
-      self.assertEqual(outputs.shape.as_list(), [None, timestep, output_shape])
-      self.assertEqual(state.shape.as_list(), [None, output_shape])
-      loss = losses.softmax_cross_entropy(predict, state)
-      train_op = training.GradientDescentOptimizer(0.001).minimize(loss)
-
-      sess.run([variables_lib.global_variables_initializer()])
-      _, outputs, state = sess.run(
-          [train_op, outputs, state], {inputs: x_train, predict: y_train})
-
-      self.assertEqual(len(outputs), batch)
-      self.assertEqual(len(state), batch)
-
-  def testRNNWithKerasLSTMCell(self):
-    with self.test_session() as sess:
-      input_shape = 10
-      output_shape = 5
-      timestep = 4
-      batch = 100
-      (x_train, y_train), _ = testing_utils.get_test_data(
-          train_samples=batch,
-          test_samples=0,
-          input_shape=(timestep, input_shape),
-          num_classes=output_shape)
-      y_train = keras.utils.to_categorical(y_train)
-      cell = keras.layers.LSTMCell(output_shape)
-
-      inputs = array_ops.placeholder(
-          dtypes.float32, shape=(None, timestep, input_shape))
-      predict = array_ops.placeholder(
-          dtypes.float32, shape=(None, output_shape))
-
-      outputs, state = rnn.dynamic_rnn(
-          cell, inputs, dtype=dtypes.float32)
-      self.assertEqual(outputs.shape.as_list(), [None, timestep, output_shape])
-      self.assertEqual(len(state), 2)
-      self.assertEqual(state[0].shape.as_list(), [None, output_shape])
-      self.assertEqual(state[1].shape.as_list(), [None, output_shape])
-      loss = losses.softmax_cross_entropy(predict, state[0])
-      train_op = training.GradientDescentOptimizer(0.001).minimize(loss)
-
-      sess.run([variables_lib.global_variables_initializer()])
-      _, outputs, state = sess.run(
-          [train_op, outputs, state], {inputs: x_train, predict: y_train})
-
-      self.assertEqual(len(outputs), batch)
-      self.assertEqual(len(state), 2)
-      self.assertEqual(len(state[0]), batch)
-      self.assertEqual(len(state[1]), batch)
-
-  def testRNNWithStackKerasCell(self):
-    with self.test_session() as sess:
-      input_shape = 10
-      output_shape = 5
-      timestep = 4
-      batch = 100
-      (x_train, y_train), _ = testing_utils.get_test_data(
-          train_samples=batch,
-          test_samples=0,
-          input_shape=(timestep, input_shape),
-          num_classes=output_shape)
-      y_train = keras.utils.to_categorical(y_train)
-      cell = keras.layers.StackedRNNCells(
-          [keras.layers.LSTMCell(2 * output_shape),
-           keras.layers.LSTMCell(output_shape)])
-
-      inputs = array_ops.placeholder(
-          dtypes.float32, shape=(None, timestep, input_shape))
-      predict = array_ops.placeholder(
-          dtypes.float32, shape=(None, output_shape))
-
-      outputs, state = rnn.dynamic_rnn(
-          cell, inputs, dtype=dtypes.float32)
-      self.assertEqual(outputs.shape.as_list(), [None, timestep, output_shape])
-      self.assertEqual(len(state), 4)
-      self.assertEqual(state[0].shape.as_list(), [None, output_shape])
-      self.assertEqual(state[1].shape.as_list(), [None, output_shape])
-      self.assertEqual(state[2].shape.as_list(), [None, 2 * output_shape])
-      self.assertEqual(state[3].shape.as_list(), [None, 2 * output_shape])
-      loss = losses.softmax_cross_entropy(predict, state[0])
-      train_op = training.GradientDescentOptimizer(0.001).minimize(loss)
-
-      sess.run([variables_lib.global_variables_initializer()])
-      _, outputs, state = sess.run(
-          [train_op, outputs, state], {inputs: x_train, predict: y_train})
-
-      self.assertEqual(len(outputs), batch)
-      self.assertEqual(len(state), 4)
-      for s in state:
-        self.assertEqual(len(s), batch)
-
-  def testStaticRNNWithKerasSimpleRNNCell(self):
-    with self.test_session() as sess:
-      input_shape = 10
-      output_shape = 5
-      timestep = 4
-      batch = 100
-      (x_train, y_train), _ = testing_utils.get_test_data(
-          train_samples=batch,
-          test_samples=0,
-          input_shape=(timestep, input_shape),
-          num_classes=output_shape)
-      x_train = np.transpose(x_train, (1, 0, 2))
-      y_train = keras.utils.to_categorical(y_train)
-      cell = keras.layers.SimpleRNNCell(output_shape)
-
-      inputs = [array_ops.placeholder(
-          dtypes.float32, shape=(None, input_shape))] * timestep
-      predict = array_ops.placeholder(
-          dtypes.float32, shape=(None, output_shape))
-
-      outputs, state = rnn.static_rnn(
-          cell, inputs, dtype=dtypes.float32)
-      self.assertEqual(len(outputs), timestep)
-      self.assertEqual(outputs[0].shape.as_list(), [None, output_shape])
-      self.assertEqual(state.shape.as_list(), [None, output_shape])
-      loss = losses.softmax_cross_entropy(predict, state)
-      train_op = training.GradientDescentOptimizer(0.001).minimize(loss)
-
-      sess.run([variables_lib.global_variables_initializer()])
-      feed_dict = {i: d for i, d in zip(inputs, x_train)}
-      feed_dict[predict] = y_train
-      _, outputs, state = sess.run(
-          [train_op, outputs, state], feed_dict)
-
-      self.assertEqual(len(outputs), timestep)
-      self.assertEqual(len(outputs[0]), batch)
-      self.assertEqual(len(state), batch)
-
-  def testKerasAndTFRNNLayerOutputComparison(self):
-    input_shape = 10
-    output_shape = 5
-    timestep = 4
-    batch = 20
-    (x_train, _), _ = testing_utils.get_test_data(
-        train_samples=batch,
-        test_samples=0,
-        input_shape=(timestep, input_shape),
-        num_classes=output_shape)
-    fix_weights_generator = keras.layers.SimpleRNNCell(output_shape)
-    fix_weights_generator.build((None, input_shape))
-    weights = fix_weights_generator.get_weights()
-
-    with self.test_session(graph=ops_lib.Graph()) as sess:
-      inputs = array_ops.placeholder(
-          dtypes.float32, shape=(None, timestep, input_shape))
-      cell = keras.layers.SimpleRNNCell(output_shape)
-      tf_out, tf_state = rnn.dynamic_rnn(
-          cell, inputs, dtype=dtypes.float32)
-      cell.set_weights(weights)
-      [tf_out, tf_state] = sess.run([tf_out, tf_state], {inputs: x_train})
-    with self.test_session(graph=ops_lib.Graph()) as sess:
-      k_input = keras.Input(shape=(timestep, input_shape),
-                            dtype=dtypes.float32)
-      cell = keras.layers.SimpleRNNCell(output_shape)
-      layer = keras.layers.RNN(cell, return_sequences=True, return_state=True)
-      keras_out = layer(k_input)
-      cell.set_weights(weights)
-      k_out, k_state = sess.run(keras_out, {k_input: x_train})
-    self.assertAllClose(tf_out, k_out)
-    self.assertAllClose(tf_state, k_state)
-
   def testBasicLSTMCellInterchangeWithLSTMCell(self):
     with self.test_session(graph=ops_lib.Graph()) as sess:
       basic_cell = rnn_cell_impl.BasicLSTMCell(1)
       basic_cell(array_ops.ones([1, 1]),
-                 state=basic_cell.get_initial_state(inputs=None,
-                                                    batch_size=1,
-                                                    dtype=dtypes.float32))
+                 state=basic_cell.zero_state(batch_size=1,
+                                             dtype=dtypes.float32))
       self.evaluate([v.initializer for v in basic_cell.variables])
       self.evaluate(basic_cell._bias.assign([10.] * 4))
       save = saver.Saver()
@@ -544,9 +293,8 @@ class RNNTest(test.TestCase):
     with self.test_session(graph=ops_lib.Graph()) as sess:
       lstm_cell = rnn_cell_impl.LSTMCell(1, name="basic_lstm_cell")
       lstm_cell(array_ops.ones([1, 1]),
-                state=lstm_cell.get_initial_state(inputs=None,
-                                                  batch_size=1,
-                                                  dtype=dtypes.float32))
+                state=lstm_cell.zero_state(batch_size=1,
+                                           dtype=dtypes.float32))
       self.evaluate([v.initializer for v in lstm_cell.variables])
       save = saver.Saver()
       save.restore(sess, save_path)
diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py
index 38336b64db..7b6ab20975 100644
--- a/tensorflow/python/ops/rnn.py
+++ b/tensorflow/python/ops/rnn.py
@@ -608,8 +608,7 @@ def dynamic_rnn(cell, inputs, sequence_length=None, initial_state=None,
     else:
       if not dtype:
         raise ValueError("If there is no initial_state, you must give a dtype.")
-      state = cell.get_initial_state(
-          inputs=None, batch_size=batch_size, dtype=dtype)
+      state = cell.zero_state(batch_size, dtype)
 
     def _assert_has_shape(x, shape):
       x_shape = array_ops.shape(x)
@@ -789,10 +788,6 @@ def _dynamic_rnn_loop(cell,
       input_t = tuple(ta[time.numpy()] for ta in input_ta)
 
     input_t = nest.pack_sequence_as(structure=inputs, flat_sequence=input_t)
-    # Keras RNN cells only accept state as list, even if it's a single tensor.
-    is_keras_rnn_cell = not isinstance(cell, rnn_cell_impl.RNNCell)
-    if is_keras_rnn_cell and not nest.is_sequence(state):
-      state = [state]
     call_cell = lambda: cell(input_t, state)
 
     if sequence_length is not None:
@@ -809,9 +804,6 @@ def _dynamic_rnn_loop(cell,
     else:
       (output, new_state) = call_cell()
 
-    # Keras cells always wrap state as list, even if it's a single tensor.
-    if is_keras_rnn_cell and len(new_state) == 1:
-      new_state = new_state[0]
     # Pack state if using state tuples
     output = nest.flatten(output)
 
@@ -1294,8 +1286,7 @@ def static_rnn(cell,
       if not dtype:
         raise ValueError("If no initial_state is provided, "
                          "dtype must be specified")
-      state = cell.get_initial_state(
-          inputs=None, batch_size=batch_size, dtype=dtype)
+      state = cell.zero_state(batch_size, dtype)
 
     if sequence_length is not None:  # Prepare variables
       sequence_length = ops.convert_to_tensor(
@@ -1324,10 +1315,6 @@ def static_rnn(cell,
       min_sequence_length = math_ops.reduce_min(sequence_length)
       max_sequence_length = math_ops.reduce_max(sequence_length)
 
-    # Keras RNN cells only accept state as list, even if it's a single tensor.
-    is_keras_rnn_cell = not isinstance(cell, rnn_cell_impl.RNNCell)
-    if is_keras_rnn_cell and not nest.is_sequence(state):
-      state = [state]
     for time, input_ in enumerate(inputs):
       if time > 0:
         varscope.reuse_variables()
@@ -1346,10 +1333,8 @@ def static_rnn(cell,
             state_size=cell.state_size)
       else:
         (output, state) = call_cell()
+
       outputs.append(output)
-    # Keras RNN cells only return state as list, even if it's a single tensor.
-    if is_keras_rnn_cell and len(state) == 1:
-      state = state[0]
 
     return (outputs, state)
 
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index dcc9c6a4d2..85a6a2233c 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -80,13 +80,13 @@ def assert_like_rnncell(cell_name, cell):
   conditions = [
       hasattr(cell, "output_size"),
       hasattr(cell, "state_size"),
-      hasattr(cell, "get_initial_state"),
+      hasattr(cell, "zero_state"),
       callable(cell),
   ]
   errors = [
       "'output_size' property is missing",
       "'state_size' property is missing",
-      "'get_initial_state' method is missing",
+      "'zero_state' method is missing",
       "is not callable"
   ]
 
@@ -266,36 +266,6 @@ class RNNCell(base_layer.Layer):
     # self.add_variable() inside the call() method.
     pass
 
-  def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
-    if inputs is not None:
-      # Validate the given batch_size and dtype against inputs if provided.
-      inputs = ops.convert_to_tensor(inputs, name="inputs")
-      if batch_size is not None:
-        if tensor_util.is_tensor(batch_size):
-          static_batch_size = tensor_util.constant_value(
-              batch_size, partial=True)
-        else:
-          static_batch_size = batch_size
-        if inputs.shape[0].value != static_batch_size:
-          raise ValueError(
-              "batch size from input tensor is different from the "
-              "input param. Input tensor batch: {}, batch_size: {}".format(
-                  inputs.shape[0].value, batch_size))
-
-      if dtype is not None and inputs.dtype != dtype:
-        raise ValueError(
-            "dtype from input tensor is different from the "
-            "input param. Input tensor dtype: {}, dtype: {}".format(
-                inputs.dtype, dtype))
-
-      batch_size = inputs.shape[0].value or array_ops.shape(inputs)[0]
-      dtype = inputs.dtype
-    if None in [batch_size, dtype]:
-      raise ValueError(
-          "batch_size and dtype cannot be None while constructing initial "
-          "state: batch_size={}, dtype={}".format(batch_size, dtype))
-    return self.zero_state(batch_size, dtype)
-
   def zero_state(self, batch_size, dtype):
     """Return zero-filled state tensor(s).
 
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u-cell.pbtxt
index 3b4d703aea..4ba21a25cd 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-g-r-u-cell.pbtxt
@@ -133,10 +133,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
index 9fe35571b9..511456e740 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
@@ -133,10 +133,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
index 82b6d2015b..9dfda96fc8 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
@@ -133,10 +133,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
index 671fc66db0..6718e36dc6 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
@@ -141,10 +141,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
index 88b8f37c4f..e606eab919 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
@@ -152,10 +152,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
index a4483fefa2..5deb02d569 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
@@ -152,10 +152,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
index 381c4975d7..8a63b49180 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
@@ -151,10 +151,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
index 912365a28b..db1aae2757 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
@@ -155,10 +155,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
index a4bb3219c7..32fa151a8e 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
@@ -152,10 +152,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
index 715bfd5fc7..30c6c2ce3b 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
@@ -152,10 +152,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
index b66c0f89cc..72b40cc9f7 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
@@ -151,10 +151,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
index faeb4f3513..a5c2b4aefd 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
@@ -150,10 +150,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
index caa2e60080..61d5f04b22 100644
--- a/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
@@ -151,10 +151,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-g-r-u-cell.pbtxt
index 3b4d703aea..4ba21a25cd 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-g-r-u-cell.pbtxt
@@ -133,10 +133,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
index 9fe35571b9..511456e740 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-l-s-t-m-cell.pbtxt
@@ -133,10 +133,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
index 82b6d2015b..9dfda96fc8 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-simple-r-n-n-cell.pbtxt
@@ -133,10 +133,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
index 671fc66db0..6718e36dc6 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.keras.layers.-stacked-r-n-n-cells.pbtxt
@@ -141,10 +141,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
index 88b8f37c4f..e606eab919 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-l-s-t-m-cell.pbtxt
@@ -152,10 +152,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
index a4483fefa2..5deb02d569 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-basic-r-n-n-cell.pbtxt
@@ -152,10 +152,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
index 381c4975d7..8a63b49180 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-device-wrapper.pbtxt
@@ -151,10 +151,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
index 912365a28b..db1aae2757 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-dropout-wrapper.pbtxt
@@ -155,10 +155,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
index a4bb3219c7..32fa151a8e 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-g-r-u-cell.pbtxt
@@ -152,10 +152,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
index 715bfd5fc7..30c6c2ce3b 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-l-s-t-m-cell.pbtxt
@@ -152,10 +152,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
index b66c0f89cc..72b40cc9f7 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-multi-r-n-n-cell.pbtxt
@@ -151,10 +151,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
index faeb4f3513..a5c2b4aefd 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-r-n-n-cell.pbtxt
@@ -150,10 +150,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }
diff --git a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
index caa2e60080..61d5f04b22 100644
--- a/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
+++ b/tensorflow/tools/api/golden/v2/tensorflow.nn.rnn_cell.-residual-wrapper.pbtxt
@@ -151,10 +151,6 @@ tf_class {
     argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
   }
   member_method {
-    name: "get_initial_state"
-    argspec: "args=[\'self\', \'inputs\', \'batch_size\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], "
-  }
-  member_method {
     name: "get_input_at"
     argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
   }