Bit-RNN
Source code for paper: Effective Quantization Methods for Recurrent Neural Networks.
The implementation of PTB language model is modified from examples in tensorflow.
Requirments
Currently tested and run on TensorFlow 1.8 and Python 3.6. View other branches for legacy support. You may download the data from http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz.
Run
python train.py --config=config.gru --data_path=YOUR_DATA_PATH
Currently default is 2-bit weights and activations. You may edit the config file in config folder to change configuration.
Support
Submit issue for problem relate to the code itself. Send email to the author for general question about the paper.
Citation
Please cite follow if you use our code in your research:
@article{DBLP:journals/corr/HeWZWYZZ16,
author = {Qinyao He and
He Wen and
Shuchang Zhou and
Yuxin Wu and
Cong Yao and
Xinyu Zhou and
Yuheng Zou},
title = {Effective Quantization Methods for Recurrent Neural Networks},
journal = {CoRR},
volume = {abs/1611.10176},
year = {2016},
url = {http://arxiv.org/abs/1611.10176},
timestamp = {Thu, 01 Dec 2016 19:32:08 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/HeWZWYZZ16},
bibsource = {dblp computer science bibliography, http://dblp.org}
}