This is a self contained software accompanying the paper titled: Learning Longer Memory in Recurrent Neural Networks: http://arxiv.org/abs/1412.7753.
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CONTRIBUTING.md
LICENSE
PATENTS
README.md
datasource.lua
datatools.lua
lstm.lua
main.lua
mfactory.lua
options.lua
rnn.lua
scrnn.lua
tokenizer.lua
train.lua
util.lua

README.md

Structurally Constrained Recurrent Neural Network

This is a self contained software accompanying the paper titled: Learning Longer Memory in Recurrent Neural Networks: http://arxiv.org/abs/1412.7753. The code allows you to reproduce our results on two language modeling datasets:

  • PenntreeBank
  • Text8

The code implements three recurrent models:

  • Standard Recurrent Neural Networks
  • Long Short Term Memory Recurrent Neural Networks
  • Structurally Constrained Recurrent Neural Networks

It also allows you to play around with various hyper-parameters.

Examples

Here are some of the examples of how to use the code.

  • To run a standard RNN model on PenntreeBank with following hyper-parameters:
    • hidden units: 100
    • minibatch size: 32
    • learning rate: 0.05

you type

th -i main.lua -dset ptb -name srnn_sm -nhid 100 -batchsz 32 -eta 0.05
  • To run a LSTM RNN model on Text8 with following hyper-parameters:
    • hidden units: 100
    • minibatch size: 32
    • learning rate: 0.05
    • unfolding depth: 20
    • backprop frequency: 5

you type

th -i main.lua -dset text8 -name lstm_sm -nhid 100 -batchsz 32 -eta 0.05 -blen 20 -bfreq 5
  • To run a Structurally Constrained RNN model on PenntreeBank with following hyper-parameters:
    • hidden units: 100
    • number of constrained units: 20
    • minibatch size: 32
    • learning rate: 0.05
    • unfolding depth: 30
    • backprop frequency: 5

you type

th -i main.lua -dset text8 -name scrnn_sm -nhid 100 -nslow 20 -batchsz 32 -eta 0.05 -blen 30 -bfreq 5

To list all the options available, you need to type

th main.lua --help

Requirements

The software requires you to have the following two packages already installed on your systems:

Installing

Download the files in an appropriate directory and run the code from there. See below.

How Structurally Constrained Recurrent Neural Network Software works

The top level file is called main.lua. In order to run the code you need to run the file using torch. For example:

th -i main.lua -<option1_name> option1_val -<option2_name> option2_val ...

In order to check what all options are available, type

th -i main.lua --help

License

Structurally Constrained Recurrent Neural Network is BSD-licensed. We also provide an additional patent grant.

Other Details

See the CONTRIBUTING file for how to help out.