deepmind-research/scratchgan

ScratchGAN

This is the example code for the following NeurIPS 2019 paper. If you use the code here please cite this paper:

@article{DBLP:journals/corr/abs-1905-09922,
  author    = {Cyprien de Masson d'Autume and
               Mihaela Rosca and
               Jack W. Rae and
               Shakir Mohamed},
  title     = {Training language GANs from Scratch},
  journal   = {CoRR},
  volume    = {abs/1905.09922},
  year      = {2019},
  url       = {http://arxiv.org/abs/1905.09922},
  archivePrefix = {arXiv},
  eprint    = {1905.09922},
  timestamp = {Wed, 29 May 2019 11:27:50 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1905-09922},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contents

The code contains:

• generators.py: implementation of the generator.
• discriminator_nets.py: implementation of the discriminator.
• eval_metrics.py: implementation of the FED metric.
• losses.py: implementation of the RL loss for the generator.
• reader.py: data reader / tokenizer.
• experiment.py: main training script.

The data contains:

• {train,valid,test}.json: the EMNLP2017 News dataset.
• glove_emnlp2017.txt: the relevant subset of GloVe embeddings.

Running

Download the data and place it in the directory specified by data_dir flag:

mkdir -p /tmp/emnlp2017
curl https://storage.googleapis.com/deepmind-scratchgan-data/train.json --output /tmp/emnlp2017/train.json
curl https://storage.googleapis.com/deepmind-scratchgan-data/valid.json --output /tmp/emnlp2017/valid.json
curl https://storage.googleapis.com/deepmind-scratchgan-data/test.json --output /tmp/emnlp2017/test.json
curl https://storage.googleapis.com/deepmind-scratchgan-data/glove_emnlp2017.txt --output /tmp/emnlp2017/glove_emnlp2017.txt

Create and activate a virtual environment if needed:

virtualenv scratchgan-venv
source scratchgan-venv/bin/activate

Install requirements:

pip install -r scratchgan/requirements.txt

Run training and evaluation jobs:

python2 -m scratchgan.experiment --mode="train" &
python2 -m scratchgan.experiment --mode="evaluate_pair" &

The evaluation code is designed to run in parallel with the training.

The training code saves checkpoints periodically, the evaluation code looks for new checkpoints and evaluate them.