message: acknowledged changes
diff: @@ -182,22 +182,22 @@ class SparseXentTest(test.TestCase):
  np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float64),
  np.array([0, 3]).astype(label_dtype))
  - def testBfloat(self):
  + def testBfloat16(self):
  for label_dtype in np.int32, np.int64:
  np_features = np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]
  ).astype(np.float32)
  np_labels = np.array([0, 3]).astype(label_dtype)
  np_loss, np_backprop = self._npXent(np_features, np_labels)
  - bf_np_features = math_ops.cast(np_features, dtypes.bfloat16)
  - bf_np_loss = math_ops.cast(np_loss, dtypes.bfloat16)
  - bf_np_backprop = math_ops.cast(np_backprop, dtypes.bfloat16)
  + casted_features = math_ops.cast(np_features, dtypes.bfloat16)
  + casted_loss = math_ops.cast(np_loss, dtypes.bfloat16)
  + casted_backprop = math_ops.cast(np_backprop, dtypes.bfloat16)
  with self.cached_session(use_gpu=False) as sess:
  loss, backprop = gen_nn_ops.sparse_softmax_cross_entropy_with_logits(
  - bf_np_features, np_labels)
  + casted_features, np_labels)
  tf_loss, tf_backprop = self.evaluate([loss, backprop])
  - self.assertAllCloseAccordingToType(bf_np_loss, tf_loss)
  - self.assertAllCloseAccordingToType(bf_np_backprop, tf_backprop)
  + self.assertAllCloseAccordingToType(casted_loss, tf_loss)
  + self.assertAllCloseAccordingToType(casted_backprop, tf_backprop)
  def testHalf(self):
  for label_dtype in np.int32, np.int64:
  