https://github.com/anibali/infogan

An unofficial Torch implementation of InfoGAN
https://github.com/anibali/infogan

gan lua neural-network torch7

Last synced: 6 months ago
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An unofficial Torch implementation of InfoGAN

• Host: GitHub
• URL: https://github.com/anibali/infogan
• Owner: anibali
• License: mit
• Created: 2016-08-21T02:33:47.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2017-09-05T02:31:35.000Z (over 8 years ago)
• Last Synced: 2025-02-01T15:40:49.716Z (12 months ago)
• Topics: gan, lua, neural-network, torch7
• Language: Lua
• Size: 129 KB
• Stars: 19
• Watchers: 4
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# InfoGAN for Torch

This is an unofficial Torch implementation of the InfoGAN neural network

architecture proposed by Chen et al in their paper ["InfoGAN: Interpretable

Representation Learning by Information Maximizing Generative Adversarial

Nets"](http://arxiv.org/abs/1606.03657). The original TensorFlow implementation

can be found at https://github.com/openai/InfoGAN.

![Generated MNIST digits varying c_1](images/output_varying_c1.png)

![Generated MNIST digits varying c_2](images/output_varying_c2.png)

## Features

* [x] Implement the InfoGAN network architecture

* [x] Use variational mutual information maximization for the error calculations

      (as per the paper)

* [x] CLI options for tweaking noise inputs and salient variables

* [x] Deterministic training

## Requirements

* A cuDNN 5 capable NVIDIA graphics card

* [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)

## Running

Begin by cloning this repository.

```sh

git clone https://github.com/anibali/infogan

cd infogan

```

**NOTE:** If you are using a Pascal architecture GPU (CUDA 8.0), you will need

to modify the Dockerfile to build from a CUDA 8.0 base image. Refer to

https://github.com/anibali/infogan/issues/5 for more details. Please make this

change before proceeding.

Build the Docker image which contains Torch and all other required

dependencies.

```sh

nvidia-docker build -t infogan-torch .

```

Download the MNIST dataset.

```sh

nvidia-docker run -it --rm --volume=$PWD:/app infogan-torch sh download_mnist.sh

```

Finally, run the training script.

```sh

nvidia-docker run -it --rm --volume=$PWD:/app infogan-torch th bin/train_infogan.lua

```

Output artifacts will appear in the `out/` directory during training, including

examples of fake images generated by the generator network and serialized copies

of the generator and discriminator networks.