diff --git a/tensorflow/python/keras/layers/recurrent.py b/tensorflow/python/keras/layers/recurrent.py
index 66c68e20854..12c82a53f6e 100644
--- a/tensorflow/python/keras/layers/recurrent.py
+++ b/tensorflow/python/keras/layers/recurrent.py
@@ -670,6 +670,8 @@ class RNN(Layer):
     if generic_utils.has_arg(self.cell.call, 'training'):
       kwargs['training'] = training
 
+    # TF RNN cells expect single tensor as state instead of list wrapped tensor.
+    is_tf_rnn_cell = getattr(self.cell, '_is_tf_rnn_cell', None) is not None
     if constants:
       if not generic_utils.has_arg(self.cell.call, 'constants'):
         raise ValueError('RNN cell does not support constants')
@@ -677,11 +679,21 @@ class RNN(Layer):
       def step(inputs, states):
         constants = states[-self._num_constants:]  # pylint: disable=invalid-unary-operand-type
         states = states[:-self._num_constants]  # pylint: disable=invalid-unary-operand-type
-        return self.cell.call(inputs, states, constants=constants, **kwargs)
+
+        states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
+        output, new_states = self.cell.call(
+            inputs, states, constants=constants, **kwargs)
+        if not nest.is_sequence(new_states):
+          new_states = [new_states]
+        return output, new_states
     else:
 
       def step(inputs, states):
-        return self.cell.call(inputs, states, **kwargs)
+        states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
+        output, new_states = self.cell.call(inputs, states, **kwargs)
+        if not nest.is_sequence(new_states):
+          new_states = [new_states]
+        return output, new_states
 
     last_output, outputs, states = K.rnn(
         step,
diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py
index e32d7c4e672..c72ada11dad 100644
--- a/tensorflow/python/kernel_tests/rnn_test.py
+++ b/tensorflow/python/kernel_tests/rnn_test.py
@@ -301,14 +301,12 @@ class RNNTest(test.TestCase):
       self.assertAllEqual([10.] * 4, self.evaluate(lstm_cell._bias))
 
   def testRNNCellSerialization(self):
-    for cell in  [
+    for cell in [
         rnn_cell_impl.LSTMCell(32, use_peepholes=True, cell_clip=True),
         rnn_cell_impl.BasicLSTMCell(32, dtype=dtypes.float32),
-        # TODO(scottzhu): GRU and BasicRNN cell are not compatible with Keras.
-        # rnn_cell_impl.BasicRNNCell(
-        #     32, activation="relu", dtype=dtypes.float32),
-        # rnn_cell_impl.GRUCell(
-        #     32, kernel_initializer="ones", dtype=dtypes.float32)
+        rnn_cell_impl.BasicRNNCell(32, activation="relu", dtype=dtypes.float32),
+        rnn_cell_impl.GRUCell(
+            32, kernel_initializer="ones", dtype=dtypes.float32)
     ]:
       with self.test_session():
         x = keras.Input((None, 5))
@@ -326,11 +324,13 @@ class RNNTest(test.TestCase):
         # not visible as a Keras layer, and also has a name conflict with
         # keras.LSTMCell and GRUCell.
         layer = keras.layers.RNN.from_config(
-            config, custom_objects={
-                # "BasicRNNCell": rnn_cell_impl.BasicRNNCell,
-                # "GRUCell": rnn_cell_impl.GRUCell,
+            config,
+            custom_objects={
+                "BasicRNNCell": rnn_cell_impl.BasicRNNCell,
+                "GRUCell": rnn_cell_impl.GRUCell,
                 "LSTMCell": rnn_cell_impl.LSTMCell,
-                "BasicLSTMCell": rnn_cell_impl.BasicLSTMCell})
+                "BasicLSTMCell": rnn_cell_impl.BasicLSTMCell
+            })
         y = layer(x)
         model = keras.models.Model(x, y)
         model.set_weights(weights)
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index f481726d545..85a6a2233cc 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -193,6 +193,13 @@ class RNNCell(base_layer.Layer):
   for each `s` in `self.batch_size`.
   """
 
+  def __init__(self, trainable=True, name=None, dtype=None, **kwargs):
+    super(RNNCell, self).__init__(
+        trainable=trainable, name=name, dtype=dtype, **kwargs)
+    # Attribute that indicates whether the cell is a TF RNN cell, due the slight
+    # difference between TF and Keras RNN cell.
+    self._is_tf_rnn_cell = True
+
   def __call__(self, inputs, state, scope=None):
     """Run this RNN cell on inputs, starting from the given state.
 
@@ -524,8 +531,8 @@ class GRUCell(LayerRNNCell):
   def get_config(self):
     config = {
         "num_units": self._num_units,
-        "initializer": initializers.serialize(self._initializer),
         "kernel_initializer": initializers.serialize(self._kernel_initializer),
+        "bias_initializer": initializers.serialize(self._bias_initializer),
         "activation": activations.serialize(self._activation),
         "reuse": self._reuse,
     }