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Last Commit
Feb. 22, 2019
Jun. 2, 2016

Status: Archive (code is provided as-is, no updates expected)


Code for reproducing key results in the paper InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets by Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel.


This project currently requires the dev version of TensorFlow available on Github: As of the release, the latest commit is 79174a.

In addition, please pip install the following packages:

  • prettytensor
  • progressbar
  • python-dateutil

Running in Docker

$ git clone
$ docker run -v $(pwd)/InfoGAN:/InfoGAN -w /InfoGAN -it -p 8888:8888
root@X:/InfoGAN# pip install -r requirements.txt
root@X:/InfoGAN# python launchers/

Running Experiment

We provide the source code to run the MNIST example:

PYTHONPATH='.' python launchers/

You can launch TensorBoard to view the generated images:

tensorboard --logdir logs/mnist