1 files changed, 0 insertions, 42 deletions
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
index 16b6d145e3..909c6aba2b 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
@@ -38,9 +38,6 @@ from tensorflow.python.ops import rnn_cell_impl
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables as variables_lib
 from tensorflow.python.platform import test
-from tensorflow.python.framework import test_util
-from tensorflow.contrib.rnn.python.ops import rnn_cell as contrib_rnn_cell
-
 
 
 # pylint: enable=protected-access
@@ -361,45 +358,6 @@ class RNNCellTest(test.TestCase):
       self.assertEquals(variables[2].op.name,
                         "root/lstm_cell/projection/kernel")
 
-  def testLSTMCellLayerNorm(self):
-    with self.test_session() as sess:
-      num_units = 2
-      num_proj = 3
-      batch_size = 1
-      input_size = 4
-      with variable_scope.variable_scope(
-          "root", initializer=init_ops.constant_initializer(0.5)):
-        x = array_ops.zeros([batch_size, input_size])
-        c = array_ops.zeros([batch_size, num_units])
-        h = array_ops.zeros([batch_size, num_proj])
-        state = rnn_cell_impl.LSTMStateTuple(c, h)
-        cell = contrib_rnn_cell.LayerNormLSTMCell(
-          num_units=num_units,
-          num_proj=num_proj,
-          forget_bias=1.0,
-          layer_norm=True,
-          norm_gain=1.0,
-          norm_shift=0.0)
-        g, out_m = cell(x, state)
-        sess.run([variables_lib.global_variables_initializer()])
-        res = sess.run([g, out_m], {
-          x.name: np.ones((batch_size, input_size)),
-          c.name: 0.1 * np.ones((batch_size, num_units)),
-          h.name: 0.1 * np.ones((batch_size, num_proj))
-        })
-        self.assertEqual(len(res), 2)
-        # The numbers in results were not calculated, this is mostly just a
-        # smoke test.
-        self.assertEqual(res[0].shape, (batch_size, num_proj))
-        self.assertEqual(res[1][0].shape, (batch_size, num_units))
-        self.assertEqual(res[1][1].shape, (batch_size, num_proj))
-        # Different inputs so different outputs and states
-        for i in range(1, batch_size):
-          self.assertTrue(
-            float(np.linalg.norm((res[0][0, :] - res[0][i, :]))) < 1e-6)
-          self.assertTrue(
-            float(np.linalg.norm((res[1][0, :] - res[1][i, :]))) < 1e-6)
-
   def testOutputProjectionWrapper(self):
     with self.test_session() as sess:
       with variable_scope.variable_scope(