Stack RNN is a project gathering the code from the paper Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets by Armand Joulin and Tomas Mikolov (pdf). In this research project, we focus on extending Recurrent Neural Networks (RNN) with a stack to allow them to learn sequences which require some form of persistent memory.
Examples are given in the script script_tasks.sh. The code is still under construction.
We are working on releasing the code for the list RNN. If you have any suggestion, please let us know (contacts below).
To run the code on a task:
> make toy
> ./train_toy -ntask 1 -nchar 2 -nhid 10 -nstack 1 -lr .1 -nmax 10 -depth 2 -bptt 50 -mod 1
To run the code on binary addition:
> make add
> ./train_add
Stack RNN works on:
- Mac OS X
- Linux
It was not tested on Windows. To compile the code a relatively recent version of g++ is required.
Run make to compile everything.
For more help about the options:
> make toy
> ./train_toy --help
Note that train_add can take the same options as train_toy.
- Paper: http://arxiv.org/abs/1503.01007
- Facebook page: https://www.facebook.com/fair
- Contact: ajoulin@fb.com
See the CONTRIBUTING file for how to help out.
Stack RNN is BSD-licensed. We also provide an additional patent grant