https://github.com/shaform/scratchgan-prep

https://github.com/shaform/scratchgan-prep

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• Host: GitHub
• URL: https://github.com/shaform/scratchgan-prep
• Owner: shaform
• Created: 2020-07-21T15:02:23.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-07-25T14:18:57.000Z (almost 6 years ago)
• Last Synced: 2025-01-03T08:22:36.091Z (over 1 year ago)
• Language: Python
• Size: 50.8 KB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 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:

    python3 -m scratchgan.experiment --mode="train" &

    python3 -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.