Skip to content
master
Go to file
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
Jun 2, 2016
Jun 2, 2016

README.md

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

InfoGAN

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.

Dependencies

This project currently requires the dev version of TensorFlow available on Github: https://github.com/tensorflow/tensorflow. 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 git@github.com:openai/InfoGAN.git
$ docker run -v $(pwd)/InfoGAN:/InfoGAN -w /InfoGAN -it -p 8888:8888 gcr.io/tensorflow/tensorflow:r0.9rc0-devel
root@X:/InfoGAN# pip install -r requirements.txt
root@X:/InfoGAN# python launchers/run_mnist_exp.py

Running Experiment

We provide the source code to run the MNIST example:

PYTHONPATH='.' python launchers/run_mnist_exp.py

You can launch TensorBoard to view the generated images:

tensorboard --logdir logs/mnist

About

Code for reproducing key results in the paper "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"

Topics

Resources

Releases

No releases published

Packages

No packages published

Languages

You can’t perform that action at this time.