Basic LSTM on regular expression languages

Create a regular expression language and run the LSTM.

This project is made in order to test LSTM capabilities.

Usage example

Generate train and dev sets.
gen_examples.py is an example on how to create a language.

The script gen_examples.py will create regular expression samples for the language:
[0-9]+a+[0-9]+b+[0-9]+c+[0-9]+d+[0-9]+

Parameters:
samples_file – the file which the samples will be writen to.
num_samples – number of samples to generate.
seq_max – maximum length of each contiguous sequence, for example a+ will be a sequence of random number of a
in range [1, seq_max].

example:
```
python gen_examples.py data/train 1000 50
```
Train the LSTM model on the language.
Run basicLSTM.py on the data you created and check the results.

Parameters:
train_samples – train samples file.
dev_samples – dev samples file.

example:
```
python basicLSTM.py data/train data/dev
```

Build With

PyTorch – the deep learning platform used

Author

Bar Katz – bar-katz on github – [email protected]

Contributing

Fork it (https://github.com/bar-katz/Basic_RNN/fork)
Create your feature branch (git checkout -b feature)
Commit your changes (git commit -am 'add feature')
Push to the branch (git push origin feature)
Create a new Pull Request

Share your results!
Found a language LSTM can not learn try to understand why and add your language description to languages directory.