diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py
index f3b3f6a5157..566aa75f2d9 100644
--- a/tensorflow/python/keras/backend.py
+++ b/tensorflow/python/keras/backend.py
@@ -4105,9 +4105,9 @@ def rnn(step_function,
   # That's what the tile call does, it just repeats the mask along its
   # second dimension n times.
   def _expand_mask(mask_t, input_t, fixed_dim=1):
-    if nest.is_sequence(mask_t):
+    if nest.is_nested(mask_t):
       raise ValueError('mask_t is expected to be tensor, but got %s' % mask_t)
-    if nest.is_sequence(input_t):
+    if nest.is_nested(input_t):
       raise ValueError('input_t is expected to be tensor, but got %s' % input_t)
     rank_diff = len(input_t.shape) - len(mask_t.shape)
     for _ in range(rank_diff):
@@ -4133,7 +4133,7 @@ def rnn(step_function,
         input_t.reverse()
       return input_t
 
-    if nest.is_sequence(inputs):
+    if nest.is_nested(inputs):
       processed_input = nest.map_structure(_process_single_input_t, inputs)
     else:
       processed_input = (_process_single_input_t(inputs),)
diff --git a/tensorflow/python/keras/engine/compile_utils.py b/tensorflow/python/keras/engine/compile_utils.py
index 2e4e40826e5..5b8f1492fd0 100644
--- a/tensorflow/python/keras/engine/compile_utils.py
+++ b/tensorflow/python/keras/engine/compile_utils.py
@@ -62,7 +62,7 @@ class Container(object):
     struct = map_to_output_names(outputs, self._output_names, struct)
     struct = map_missing_dict_keys(outputs, struct)
     # Allow passing one object that applies to all outputs.
-    if not nest.is_sequence(struct) and nest.is_sequence(outputs):
+    if not nest.is_nested(struct) and nest.is_nested(outputs):
       struct = nest.map_structure(lambda _: struct, outputs)
     return struct
 
@@ -267,7 +267,7 @@ class LossesContainer(Container):
     return loss
 
   def _should_broadcast(self, obj):
-    return not nest.is_sequence(obj)
+    return not nest.is_nested(obj)
 
   def _copy_object(self, obj):
     return obj  # Losses don't need to be copied.
@@ -478,11 +478,11 @@ class MetricsContainer(Container):
 
   def _should_broadcast(self, obj):
     # e.g. 'mse'.
-    if not nest.is_sequence(obj):
+    if not nest.is_nested(obj):
       return True
     # e.g. ['mse'] or ['mse', 'mae'].
     return (isinstance(obj, (list, tuple)) and
-            not any(nest.is_sequence(o) for o in obj))
+            not any(nest.is_nested(o) for o in obj))
 
   def _copy_object(self, obj):
     if isinstance(obj, metrics_mod.Metric):
@@ -572,10 +572,10 @@ def map_to_output_names(y_pred, output_names, struct):
   Returns:
     `struct` mapped to a list in same order as `output_names`.
   """
-  single_output = not nest.is_sequence(y_pred)
+  single_output = not nest.is_nested(y_pred)
   outputs_are_flat_list = (not single_output and
                            isinstance(y_pred, (list, tuple)) and
-                           not any(nest.is_sequence(y_p) for y_p in y_pred))
+                           not any(nest.is_nested(y_p) for y_p in y_pred))
 
   if (single_output or outputs_are_flat_list) and isinstance(struct, dict):
     output_names = output_names or create_pseudo_output_names(y_pred)
diff --git a/tensorflow/python/keras/engine/data_adapter.py b/tensorflow/python/keras/engine/data_adapter.py
index 8184ec7a0c1..3672ef64da3 100644
--- a/tensorflow/python/keras/engine/data_adapter.py
+++ b/tensorflow/python/keras/engine/data_adapter.py
@@ -1300,7 +1300,7 @@ def _make_class_weight_map_fn(class_weight):
     """Convert `class_weight` to `sample_weight`."""
     x, y, sw = unpack_x_y_sample_weight(data)
 
-    if nest.is_sequence(y):
+    if nest.is_nested(y):
       raise ValueError(
           "`class_weight` is only supported for Models with a single output.")
 
@@ -1496,7 +1496,7 @@ def pack_x_y_sample_weight(x, y=None, sample_weight=None):
     # there is no ambiguity. This also makes NumPy and Dataset
     # consistent in that the user does not have to wrap their Dataset
     # data in an unecessary tuple
-    if not nest.is_sequence(x):
+    if not nest.is_nested(x):
       return x
     else:
       return (x,)
diff --git a/tensorflow/python/keras/engine/functional.py b/tensorflow/python/keras/engine/functional.py
index 6c725d0d795..2b991cc187c 100644
--- a/tensorflow/python/keras/engine/functional.py
+++ b/tensorflow/python/keras/engine/functional.py
@@ -130,9 +130,9 @@ class Functional(training_lib.Model):
     # be called with a dict, where the keys of the dict are the names
     # of the `Input` objects. Extra keys are ignored with warning.
     self._enable_dict_to_input_mapping = (
-        not nest.is_sequence(self._nested_inputs) or
+        not nest.is_nested(self._nested_inputs) or
         (isinstance(self._nested_inputs, (list, tuple, dict)) and
-         not any(nest.is_sequence(t) for t in self._nested_inputs)))
+         not any(nest.is_nested(t) for t in self._nested_inputs)))
 
     if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
       base_layer_utils.create_keras_history(self._nested_outputs)
@@ -519,7 +519,7 @@ class Functional(training_lib.Model):
     """Maps `tensors` to their respective `keras.Input`."""
     if self._enable_dict_to_input_mapping and isinstance(tensors, dict):
       ref_inputs = self._nested_inputs
-      if not nest.is_sequence(ref_inputs):
+      if not nest.is_nested(ref_inputs):
         ref_inputs = [self._nested_inputs]
       if isinstance(ref_inputs, dict):
         # In the case that the graph is constructed with dict input tensors,
@@ -1289,7 +1289,7 @@ def get_network_config(network, serialize_layer_fn=None):
         tf_utils.ListWrapper([layer.name, new_node_index, tensor_index]))
   model_inputs = nest.pack_sequence_as(network._nested_inputs, model_inputs)
   # Preserve external Keras compat for Models with single input.
-  if not nest.is_sequence(model_inputs):
+  if not nest.is_nested(model_inputs):
     model_inputs = [model_inputs]
   model_inputs = tf_utils.convert_inner_node_data(model_inputs)
   config['input_layers'] = model_inputs
@@ -1305,7 +1305,7 @@ def get_network_config(network, serialize_layer_fn=None):
         tf_utils.ListWrapper([layer.name, new_node_index, tensor_index]))
   model_outputs = nest.pack_sequence_as(network._nested_outputs, model_outputs)
   # Preserve external Keras compat for Models with single output.
-  if not nest.is_sequence(model_outputs):
+  if not nest.is_nested(model_outputs):
     model_outputs = [model_outputs]
   model_outputs = tf_utils.convert_inner_node_data(model_outputs)
   config['output_layers'] = model_outputs
diff --git a/tensorflow/python/keras/engine/node.py b/tensorflow/python/keras/engine/node.py
index f629648baf0..30771a181f1 100644
--- a/tensorflow/python/keras/engine/node.py
+++ b/tensorflow/python/keras/engine/node.py
@@ -198,7 +198,7 @@ class Node(object):
       return tf_utils.ListWrapper(data)
 
     data = nest.map_structure(serialize_first_arg_tensor, inputs)
-    if not nest.is_sequence(data):
+    if not nest.is_nested(data):
       data = [data]
     data = tf_utils.convert_inner_node_data(data)
     return data
diff --git a/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py b/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py
index de69cdac611..422e7afb31e 100644
--- a/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py
+++ b/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py
@@ -1235,7 +1235,7 @@ class MultiRNNCell(RNNCell):
     super(MultiRNNCell, self).__init__()
     if not cells:
       raise ValueError("Must specify at least one cell for MultiRNNCell.")
-    if not nest.is_sequence(cells):
+    if not nest.is_nested(cells):
       raise TypeError("cells must be a list or tuple, but saw: %s." % cells)
 
     if len(set(id(cell) for cell in cells)) < len(cells):
@@ -1252,7 +1252,7 @@ class MultiRNNCell(RNNCell):
         self._track_trackable(cell, name="cell-%d" % (cell_number,))
     self._state_is_tuple = state_is_tuple
     if not state_is_tuple:
-      if any(nest.is_sequence(c.state_size) for c in self._cells):
+      if any(nest.is_nested(c.state_size) for c in self._cells):
         raise ValueError("Some cells return tuples of states, but the flag "
                          "state_is_tuple is not set.  State sizes are: %s" %
                          str([c.state_size for c in self._cells]))
@@ -1309,7 +1309,7 @@ class MultiRNNCell(RNNCell):
     for i, cell in enumerate(self._cells):
       with vs.variable_scope("cell_%d" % i):
         if self._state_is_tuple:
-          if not nest.is_sequence(state):
+          if not nest.is_nested(state):
             raise ValueError(
                 "Expected state to be a tuple of length %d, but received: %s" %
                 (len(self.state_size), state))
diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py
index 9d10ad73749..72962eb8aef 100644
--- a/tensorflow/python/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/layers/recurrent.py
@@ -139,7 +139,7 @@ class StackedRNNCells(Layer):
     # Call the cells in order and store the returned states.
     new_nested_states = []
     for cell, states in zip(self.cells, nested_states):
-      states = states if nest.is_sequence(states) else [states]
+      states = states if nest.is_nested(states) else [states]
       # TF cell does not wrap the state into list when there is only one state.
       is_tf_rnn_cell = getattr(cell, '_is_tf_rnn_cell', None) is not None
       states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
@@ -448,7 +448,7 @@ class RNN(Layer):
   def states(self):
     if self._states is None:
       state = nest.map_structure(lambda _: None, self.cell.state_size)
-      return state if nest.is_sequence(self.cell.state_size) else [state]
+      return state if nest.is_nested(self.cell.state_size) else [state]
     return self._states
 
   @states.setter
@@ -559,7 +559,7 @@ class RNN(Layer):
       # A nested tensor input
       pass
 
-    if not nest.is_sequence(input_shape):
+    if not nest.is_nested(input_shape):
       # This indicates the there is only one input.
       if self.input_spec is not None:
         self.input_spec[0] = get_input_spec(input_shape)
@@ -632,7 +632,7 @@ class RNN(Layer):
   def get_initial_state(self, inputs):
     get_initial_state_fn = getattr(self.cell, 'get_initial_state', None)
 
-    if nest.is_sequence(inputs):
+    if nest.is_nested(inputs):
       # The input are nested sequences. Use the first element in the seq to get
       # batch size and dtype.
       inputs = nest.flatten(inputs)[0]
@@ -647,7 +647,7 @@ class RNN(Layer):
       init_state = _generate_zero_filled_state(batch_size, self.cell.state_size,
                                                dtype)
     # Keras RNN expect the states in a list, even if it's a single state tensor.
-    if not nest.is_sequence(init_state):
+    if not nest.is_nested(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)
@@ -743,7 +743,7 @@ class RNN(Layer):
       # TODO(scottzhu): Should we accept multiple different masks?
       mask = nest.flatten(mask)[0]
 
-    if nest.is_sequence(inputs):
+    if nest.is_nested(inputs):
       # In the case of nested input, use the first element for shape check.
       input_shape = K.int_shape(nest.flatten(inputs)[0])
     else:
@@ -782,7 +782,7 @@ class RNN(Layer):
         states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
         output, new_states = cell_call_fn(
             inputs, states, constants=constants, **kwargs)
-        if not nest.is_sequence(new_states):
+        if not nest.is_nested(new_states):
           new_states = [new_states]
         return output, new_states
     else:
@@ -790,7 +790,7 @@ class RNN(Layer):
       def step(inputs, states):
         states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
         output, new_states = cell_call_fn(inputs, states, **kwargs)
-        if not nest.is_sequence(new_states):
+        if not nest.is_nested(new_states):
           new_states = [new_states]
         return output, new_states
     last_output, outputs, states = K.rnn(
@@ -929,7 +929,7 @@ class RNN(Layer):
         return K.zeros([batch_size] + tensor_shape.TensorShape(state).as_list())
       self.states = nest.map_structure(
           create_state_variable, self.cell.state_size)
-      if not nest.is_sequence(self.states):
+      if not nest.is_nested(self.states):
         self.states = [self.states]
     elif states is None:
       for state, size in zip(nest.flatten(self.states),
@@ -1359,7 +1359,7 @@ class SimpleRNNCell(DropoutRNNCellMixin, Layer):
     self.built = True
 
   def call(self, inputs, states, training=None):
-    prev_output = states[0] if nest.is_sequence(states) else states
+    prev_output = states[0] if nest.is_nested(states) else states
     dp_mask = self.get_dropout_mask_for_cell(inputs, training)
     rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
         prev_output, training)
@@ -1377,7 +1377,7 @@ class SimpleRNNCell(DropoutRNNCellMixin, Layer):
     if self.activation is not None:
       output = self.activation(output)
 
-    new_state = [output] if nest.is_sequence(states) else output
+    new_state = [output] if nest.is_nested(states) else output
     return output, new_state
 
   def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
@@ -1819,7 +1819,7 @@ class GRUCell(DropoutRNNCellMixin, Layer):
     self.built = True
 
   def call(self, inputs, states, training=None):
-    h_tm1 = states[0] if nest.is_sequence(states) else states  # previous memory
+    h_tm1 = states[0] if nest.is_nested(states) else states  # previous memory
 
     dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=3)
     rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
@@ -1917,7 +1917,7 @@ class GRUCell(DropoutRNNCellMixin, Layer):
       hh = self.activation(x_h + recurrent_h)
     # previous and candidate state mixed by update gate
     h = z * h_tm1 + (1 - z) * hh
-    new_state = [h] if nest.is_sequence(states) else h
+    new_state = [h] if nest.is_nested(states) else h
     return h, new_state
 
   def get_config(self):
@@ -3020,7 +3020,7 @@ def _generate_zero_filled_state(batch_size_tensor, state_size, dtype):
     init_state_size = [batch_size_tensor] + flat_dims
     return array_ops.zeros(init_state_size, dtype=dtype)
 
-  if nest.is_sequence(state_size):
+  if nest.is_nested(state_size):
     return nest.map_structure(create_zeros, state_size)
   else:
     return create_zeros(state_size)
diff --git a/tensorflow/python/keras/utils/tf_utils.py b/tensorflow/python/keras/utils/tf_utils.py
index 01f3c2a8de2..c9ad96cd37b 100644
--- a/tensorflow/python/keras/utils/tf_utils.py
+++ b/tensorflow/python/keras/utils/tf_utils.py
@@ -174,7 +174,7 @@ def map_structure_with_atomic(is_atomic_fn, map_fn, nested):
     return map_fn(nested)
 
   # Recursively convert.
-  if not nest.is_sequence(nested):
+  if not nest.is_nested(nested):
     raise ValueError(
         'Received non-atomic and non-sequence element: {}'.format(nested))
   if nest._is_mapping(nested):
@@ -284,7 +284,7 @@ def convert_inner_node_data(nested, wrap=False):
       return True
     if _is_serialized_node_data(nested):
       return True
-    return not nest.is_sequence(nested)
+    return not nest.is_nested(nested)
 
   def _convert_object_or_list(nested):
     """Convert b/t `ListWrapper` object and list representations."""