diff options
| author |  Alexandre Passos <apassos@google.com> | 2018-03-26 15:39:54 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-03-26 15:42:46 -0700 |
| commit | c83a54adcface7d4bb666d7c4fd3968ba980a50d (patch) | |
| tree | 5010155c5e2a46ef47bb9f9933e3bbf0b4628a7e /tensorflow/contrib/seq2seq | |
| parent | 290632966fae0619db30c1ba777634db9a43b757 (diff) | |
Makes tf.gather not silently snapshot resource variables.
PiperOrigin-RevId: 190537320
Diffstat (limited to 'tensorflow/contrib/seq2seq')
| -rw-r--r-- | tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py | 29 |
1 files changed, 17 insertions, 12 deletions
diff --git a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py index c4139dde49..07b3ad71d4 100644 --- a/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py +++ b/tensorflow/contrib/seq2seq/python/kernel_tests/attention_wrapper_test.py @@ -785,26 +785,31 @@ class AttentionWrapperTest(test.TestCase): wrapper.BahdanauAttention, wrapper.LuongAttention) expected_final_output = BasicDecoderOutput( - rnn_output=ResultSummary( - shape=(5, 3, 20), dtype=dtype('float32'), mean=0.11798714846372604), - sample_id=ResultSummary( - shape=(5, 3), dtype=dtype('int32'), mean=7.933333333333334)) + rnn_output=ResultSummary(shape=(5, 3, 20), + dtype=dtype('float32'), + mean=0.11723966), + sample_id=ResultSummary(shape=(5, 3), + dtype=dtype('int32'), + mean=9.2666666666666675)) expected_final_state = AttentionWrapperState( cell_state=LSTMStateTuple( - c=ResultSummary( - shape=(5, 9), dtype=dtype('float32'), mean=-0.0036486709), - h=ResultSummary( - shape=(5, 9), dtype=dtype('float32'), mean=-0.0018835809)), - attention=ResultSummary( - shape=(5, 20), dtype=dtype('float32'), mean=0.11798714846372604), + c=ResultSummary(shape=(5, 9), + dtype=dtype('float32'), + mean=-0.003545674), + h=ResultSummary(shape=(5, 9), + dtype=dtype('float32'), + mean=-0.0018327223)), + attention=ResultSummary(shape=(5, 20), + dtype=dtype('float32'), + mean=0.11728073), time=3, alignments=( ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125), ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125)), + alignment_history=(), attention_state=( ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125), - ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125)), - alignment_history=()) + ResultSummary(shape=(5, 8), dtype=dtype('float32'), mean=0.125))) expected_final_alignment_history = ( ResultSummary(shape=(3, 5, 8), dtype=dtype('float32'), mean=0.125), ResultSummary(shape=(3, 5, 8), dtype=dtype('float32'), mean=0.125)) |