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State-Regularized Recurrent Neural Networks
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Balanced_Parenthess
Tomita_Grammars
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README.md

SR-RNNs

The code for the ICML 2019 paper State-Regularized Recurrent Neural Networks (http://proceedings.mlr.press/v97/wang19j.html)

Usage and Examples

1. SR-GRU on Tomita grammars

a. To train a model, please run: THEANO_FLAGS=mode=FAST_RUN,device=cuda3,floatX=float32 python SR_GRU.py --tomita_grammar=n. The parameter n indicates the grammar number (n=1,2,3,4,7) (You can also run SR_GRU_temperature.py, it is a similar implementation but the temperature parameter is taken into account.)

b. To extract the DFA for a pre-trained model run: THEANO_FLAGS=mode=FAST_RUN,device=cuda3,floatX=float32 python DFA_Extractor.py --tomita_grammar=n

c. In the "DFAs" folder, you can find the extracted DFA.

We used the code from [1] to generate train and valid dataset.

2. SR-LSTM-P on Balanced Parentheses (BP)

a. Use "BP_Generator.py" to generated the train and validation datasets

b. To train a model, please run: THEANO_FLAGS=mode=FAST_RUN,device=cuda3,floatX=float32 python SR_LSTM_P.py

c. The trained model will be saved in the "models" folder

d. Run "SR_LSTM_P_inference.py" at inference stage, it calls "plot_transition.py" to plot the state transitions

[1] Weiss, Gail, Yoav Goldberg, and Eran Yahav. "Extracting automata from recurrent neural networks using queries and counterexamples." arXiv preprint arXiv:1711.09576 (2017).

Citation

Please cite in your publications if it helps your research:
@InProceedings{pmlr-v97-wang19j,
title = {State-Regularized Recurrent Neural Networks},
author = {Wang, Cheng and Niepert, Mathias},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {6596--6606},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
address = {Long Beach, California, USA},
month = {09--15 Jun},
publisher = {PMLR}
}

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