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path: root/tensorflow/contrib/cudnn_rnn/__init__.py
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* Fix build breakageGravatar James Qin2017-11-12
| | | | | | | | | Now the layout of tensorflow/contrib/cudnn_rnn is similar to tensorflow/contrib/layers Delete repeated tests in cudnn_rnn_ops_test (new tests in cudnn_rnn_test). PiperOrigin-RevId: 175487631
* Switch tf.contrib.cudnn_rnn.CudnnXXX to point to layer APIs instead of op ↵Gravatar James Qin2017-11-03
| | | | | | wrappers PiperOrigin-RevId: 174523358
* Automated g4 rollback of changelist 173494053Gravatar Gunhan Gulsoy2017-10-26
| | | | PiperOrigin-RevId: 173560463
* Switch tf.contrib.cudnn_rnn.CudnnXXX to point to layer APIs instead of op ↵Gravatar James Qin2017-10-25
| | | | | | wrappers PiperOrigin-RevId: 173494053
* Exposing CudnnCompatibleRNN classes.Gravatar James Qin2017-08-30
| | | | | | Also add reuse arg to LSTMBlockCell. PiperOrigin-RevId: 167030950
* Revamp CudnnRNN SaveablesGravatar James Qin2017-08-24
| | | | | | | | | | | | | | | | 1. Use a lossy way to save/restore cudnn biases during checkpointing. Cudnn uses 2 biases each gate for all RNNs while tf uses one. To allow cudnn checkpoints to be compatible with both Cudnn and platform-independent impls, previously both individual bias and summed biases each gate were stored. The new way only stores the bias sum for each gate, and split it half-half when restoring from a cudnn graph. Doing this does not cause problems since RNNs do not use weight-decay to regularize. 2. Use inheritance instead of branching * Split RNNParamsSaveable to 1 base class and 4 subclasses. * Extract common routines and only overwrite rnn-type-specific pieces in subclasses. PiperOrigin-RevId: 166413989
* Expose models in contrib.cudnn_rnnGravatar Skye Wanderman-Milne2017-03-24
| | | | Change: 151185292
* Seal contrib interfaces (as much a feasible). If you were using a symbol ↵Gravatar Martin Wicke2017-01-29
| | | | | | which is now hidden, it should be added to the _allowed_symbols list in the appropriate __init__.py file. Change: 145943844
* Add checkpoint support for cuDNN RNN.Gravatar Yao Zhang2016-12-06
| | | | | | | | | | | TESTED: - opensource_build passed: https://ci.tensorflow.org/job/tensorflow-cl-presubmit-multijob/8309/ https://ci.tensorflow.org/job/tensorflow-cl-presubmit-multijob/8641/ https://ci.tensorflow.org/job/tensorflow-cl-presubmit-multijob/9331/ https://ci.tensorflow.org/job/tensorflow-cl-presubmit-multijob/9406/ - passed unit tests Change: 141227871
* Adding Cudnn RNN support.Gravatar Xiaoqiang Zheng2016-08-26
It is about 2-3x faster compared to rnn_cell.LSTMCell and lstm_ops.LSTMBlockCell. Cudnn LSTM speedup Cudnn LSTM speedup over rnn.LSTMCell over rnn.LSTMBlockCell large 200.00% 192.27% medium 247.75% 228.38% small 500.00% 438.10% The step-time per second for each model size. Cudnn LSTM rnn_cell.LSTMCell lstm_ops.LSTMBlockCell large 0.0854 0.2562 0.2496 medium 0.0222 0.0772 0.0729 small 0.0042 0.0252 0.0226 TESTED: - opensource_build https://ci.tensorflow.org/job/tensorflow-cl-presubmit-multijob/4568/ - passed unit tests Change: 131472315
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