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

1. 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

2. 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 – barkatz138@gmail.com

Contributing

1. Fork it (https://github.com/bar-katz/Basic_RNN/fork)
2. Create your feature branch (git checkout -b feature)
3. Commit your changes (git commit -am 'add feature')
4. Push to the branch (git push origin feature)
5. 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.