diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py
index 7b46db9ad00..581a09f5b98 100644
--- a/tensorflow/python/kernel_tests/rnn_test.py
+++ b/tensorflow/python/kernel_tests/rnn_test.py
@@ -174,6 +174,45 @@ class RNNTest(tf.test.TestCase):
               1.0 * (2 + 1) * np.ones((input_size)))))
 
 
+class GRUTest(tf.test.TestCase):
+
+  def setUp(self):
+    self._seed = 23489
+    np.random.seed(self._seed)
+
+  def _testDynamic(self, use_gpu):
+    time_steps = 8
+    num_units = 3
+    input_size = 5
+    batch_size = 2
+
+    input_values = np.random.randn(time_steps, batch_size, input_size)
+
+    sequence_length = np.random.randint(0, time_steps, size=batch_size)
+
+    with self.test_session(use_gpu=use_gpu, graph=tf.Graph()) as sess:
+      concat_inputs = tf.placeholder(
+          tf.float32, shape=(time_steps, batch_size, input_size))
+
+      cell = tf.nn.rnn_cell.GRUCell(num_units=num_units, input_size=input_size)
+
+      with tf.variable_scope("dynamic_scope"):
+        outputs_dynamic, state_dynamic = tf.nn.dynamic_rnn(
+            cell, inputs=concat_inputs, sequence_length=sequence_length,
+            time_major=True, dtype=tf.float32)
+
+      feeds = {concat_inputs: input_values}
+
+      # Initialize
+      tf.initialize_all_variables().run(feed_dict=feeds)
+
+      sess.run([outputs_dynamic, state_dynamic], feed_dict=feeds)
+
+  def testDynamic(self):
+    self._testDynamic(use_gpu=False)
+    self._testDynamic(use_gpu=True)
+
+
 class LSTMTest(tf.test.TestCase):
 
   def setUp(self):
diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py
index 1d5d63dcaab..2144e37fdc7 100644
--- a/tensorflow/python/ops/rnn.py
+++ b/tensorflow/python/ops/rnn.py
@@ -465,6 +465,9 @@ def _dynamic_rnn_loop(cell, inputs, initial_state, sequence_length,
   input_shape = array_ops.shape(inputs)
   (time_steps, batch_size, unused_depth) = array_ops.unpack(input_shape, 3)
 
+  inputs_got_shape = inputs.get_shape().with_rank(3)
+  (const_time_steps, const_batch_size, const_depth) = inputs_got_shape.as_list()
+
   # Prepare dynamic conditional copying of state & output
   zero_output = array_ops.zeros(
       array_ops.pack([batch_size, cell.output_size]), inputs.dtype)
@@ -484,7 +487,20 @@ def _dynamic_rnn_loop(cell, inputs, initial_state, sequence_length,
   input_ta = input_ta.unpack(inputs)
 
   def _time_step(time, state, output_ta_t):
+    """Take a time step of the dynamic RNN.
+
+    Args:
+      time: int32 scalar Tensor.
+      state: Vector.
+      output_ta_t: `TensorArray`, the output with existing flow.
+
+    Returns:
+      The tuple (time + 1, new_state, output_ta_t with updated flow).
+    """
+
     input_t = input_ta.read(time)
+    # Restore some shape information
+    input_t.set_shape([const_batch_size, const_depth])
 
     (output, new_state) = _rnn_step(
         time, sequence_length, min_sequence_length, max_sequence_length,
@@ -501,5 +517,8 @@ def _dynamic_rnn_loop(cell, inputs, initial_state, sequence_length,
       parallel_iterations=parallel_iterations)
 
   final_outputs = output_final_ta.pack()
+  # Restore some shape information
+  final_outputs.set_shape([
+      const_time_steps, const_batch_size, cell.output_size])
 
   return (final_outputs, final_state)