diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
index bf24347c43b..d250af90370 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
@@ -2040,6 +2040,9 @@ class RawRNNTest(test.TestCase):
       inputs_ta = tensor_array_ops.TensorArray(
           dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
       inputs_ta = inputs_ta.unstack(inputs)
+      # Verify emit shapes may be unknown by feeding a placeholder that
+      # determines an emit shape.
+      unknown_dim = array_ops.placeholder(dtype=dtypes.int32)
 
       cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
 
@@ -2047,12 +2050,12 @@ class RawRNNTest(test.TestCase):
         if cell_output is None:
           emit_output = (array_ops.zeros(
               [2, 3], dtype=dtypes.int32), array_ops.zeros(
-                  [1], dtype=dtypes.int64))
+                  [unknown_dim], dtype=dtypes.int64))
           next_state = cell.zero_state(batch_size, dtypes.float32)
         else:
           emit_output = (array_ops.ones(
               [batch_size, 2, 3], dtype=dtypes.int32), array_ops.ones(
-                  [batch_size, 1], dtype=dtypes.int64))
+                  [batch_size, unknown_dim], dtype=dtypes.int64))
           next_state = cell_state
         elements_finished = array_ops.tile([time_ >= max_time], [batch_size])
         finished = math_ops.reduce_all(elements_finished)
@@ -2069,7 +2072,7 @@ class RawRNNTest(test.TestCase):
       self.assertEqual([dtypes.int32, dtypes.int64],
                        [ta.dtype for ta in output_ta])
       output = [ta.stack() for ta in output_ta]
-      output_vals = sess.run(output)
+      output_vals = sess.run(output, feed_dict={unknown_dim: 1})
       self.assertAllEqual(
           np.ones((max_time, batch_size, 2, 3), np.int32), output_vals[0])
       self.assertAllEqual(
diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py
index ca72734707e..3c3c18b1c9b 100644
--- a/tensorflow/python/ops/rnn.py
+++ b/tensorflow/python/ops/rnn.py
@@ -984,7 +984,8 @@ def raw_rnn(cell, loop_fn,
 
     if emit_structure is not None:
       flat_emit_structure = nest.flatten(emit_structure)
-      flat_emit_size = [emit.get_shape() for emit in flat_emit_structure]
+      flat_emit_size = [emit.shape if emit.shape.is_fully_defined() else
+                        array_ops.shape(emit) for emit in flat_emit_structure]
       flat_emit_dtypes = [emit.dtype for emit in flat_emit_structure]
     else:
       emit_structure = cell.output_size