diff options
| author |  Allen Lavoie <allenl@google.com> | 2018-04-26 10:30:54 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-04-26 10:33:31 -0700 |
| commit | f63a8d6aaf251344631272d6b38327481f54fe55 (patch) | |
| tree | 34aee2c0988b63304e908703119cb03fb0f0d08c /tensorflow/contrib/cudnn_rnn | |
| parent | f495e321026683359fac213b82a20f597d4ead2a (diff) | |
Remove "everything matched" assertions from CuDNN object-based checkpointing tests
After cl/194315742 the assertions correctly point out that there are some Python objects which aren't matched (they don't have variables). Another option would be to mark these as special/optional, which we can implement if there's a need.
PiperOrigin-RevId: 194416864
Diffstat (limited to 'tensorflow/contrib/cudnn_rnn')
| -rw-r--r-- | tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py index 012b17cee8..33ddfb8dee 100644 --- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py +++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py @@ -717,7 +717,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase): inputs = 3. * array_ops.ones([num_applications, num_layers, input_size], dtype=dtypes.float32) cudnn_output, _ = cudnn_layer(inputs) - status.assert_consumed().run_restore_ops() + status.run_restore_ops() second_save_path = cudnn_checkpoint.save(checkpoint_prefix) restore_layer = compatible_cell_fn() restore_layer_checkpoint = checkpointable_utils.Checkpoint( @@ -728,7 +728,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase): restore_layer_output, current_state = restore_layer( inputs=3. * array_ops.ones([1, input_size]), state=current_state) - status.assert_consumed().run_restore_ops() + status.run_restore_ops() self.assertTrue(restore_layer.variables) for variable, expected_value in zip( restore_layer.variables, expected_variable_values): |