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Mixed Incremental Cross-Entropy REINFORCE ICLR 2016
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ranzato updating after change to torch.multinomial
In the most recent version of torch, torch.multinomial returns a Long tensor.
The new version of torch.multinomial also fixes also very rare out-of-boundary errors.
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README.md

MIXER - Sequence Level Training with Recurrent Neural Networks

http://arxiv.org/abs/1511.06732

This is a self contained software accompanying the paper titled: Sequence Level Training with Recurrent Neural Networks: http://arxiv.org/abs/1511.06732. The code allows you to reproduce our result on the machine translation task.

The code implements MIXER; it runs both training and evaluation.

Preparing the training data

run prepareData.sh

Examples

Here are some examples of how to use the code.

  • To run an LSTM with the default parameter setting used to generate MIXER's entry for machine translation (see table in fig.5 of http://arxiv.org/abs/1511.06732), type
th -i main.lua
  • To run an LSTM with following hyper-parameters: ** hidden units: 128 ** minibatch size: 64 ** learning rate: 0.1 ** number of time steps we unfold: 15 type
th -i main.lua -nhid 128 -bsz 64 -lr 0.1 -bptt 15

To list all the options available, you need to type

th main.lua --help

Requirements

The software is written in Lua. It requires the following packages:

  • Torch 7
  • nngraph
  • cutorch
  • cunn It runs on standard Linux box with GPU.

Installing

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

How it 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 ...

Structure of the code.

  • main.lua this is the scripts that launches training and testing. The user can pass options to set various hyper-parameters, such as learning rate, number of hidden units, etc.
  • Trainer.lua this is a simple class that loops over the dataset a certain number of epochs to train the model, that loops over the validation/test set to evaluate and that backups.
  • model_factory.lua this is a function which returns the network operating at a single time step.
  • Mixer.lua this is the class which implements the unrolled recurrent network, cloning as many steps as necessary whatever is returned by model_factory. It implements the basic the basic fprop/bprop through the recurrent model.
  • ReinforceSampler.lua class that is used to sample from a tensor storing log-probabilities.
  • ReinforceCriterion.lua criterion which is used to compute reward once the end of sequence is reached.
  • ClassNLLCriterionWeighted.lua wrapper around ClassNLLCriterion which multiplies the output of ClassNLLCriterion by a scalar to weigh the loss.
  • LinearNoBackpropInput.lua just like Linear but without computing derivatives w.r.t. input.
  • DataSource.lua data provider that takes as input a tokenized dataset in binary format and returnes mini-batches.
  • reward_factory.lua class that is used to compute BLEU and ROUGE scores (both at the sentence and corpus level).
  • util.lua auxiliary functions.

License

"MIXER"'s software is BSD-licensed. We also provide an additional patent grant.

Other Details

See the CONTRIBUTING file for how to help out.