Getting started

HyperGAN is currently in pre-release and open beta.

Everyone will have different goals when using hypergan. Here are some common uses:

Training a network

HyperGAN is currently beta. We are still searching for a default cross data-set configuration. Various papers are implemented by listing `hypergan new modelname -l'

Deploying a model

Building datasets

Building image datasets

Using search

Each of the examples support search. Automated search can help find good configurations.

If you are unsure, you can start with the 2d-distribution.py. Check out random_search.py for possibilities, you'll likely want to modify it.

Please consider sharing in a PR if you find goood configurations!

Manual search

It's faster to do manual search if you are actively looking at results. For manual search here are some tips:

  • create a baseline json configuration

  • start with a working configuration

  • run baseline every time

  • each phase make the best performer the new baseline

  • the examples are capable of (sometimes) finding a good trainer, like 2d-distribution. Mixing and matching components seems to work.

Creating a new configuration

Hyperparameter tuning, or doing something like trying softmax loss with gradient penalty, can all be done in the json configuration file.

Implementing a paper

Each paper is a combination of json file and code.

HyperGAN has tried to make it easy to add a new component. Here are some basic classification rules on where paper implementations should go:

paper type

proposed implementation

new type of encoder

new encoder class

new type of input distribution encoding

either new encoder class or projection of uniform_encoder

new type of generator

create a new generator class

feature that could possibly apply to all generators

BaseGenerator

new type of discriminator

create a new discriminator class

applies to all discriminators

add it to all discriminators with BaseDiscriminator

new type of loss

create a new loss class

new type of gradient penalty

add it to all losses with BaseLoss

new gan design

new GAN

new trainer

configuration change

new activation

add it to ops.py. Be sure to include it in the huge conditional, search for prelu and you'll see

something else

Probably easiest to hack it in as a GAN, even if you have to build the graph up yourself in #create.

When implementing a new paper, feature-gate any breaking code so that old configurations still work(and you have something to compare).

Custom research

HyperGAN is meant to support custom research as well. You can replace any part of the GAN with the json file, or just create a new GAN altogether.

Also, try to save refactoring until after it works.

Using hyperGAN in an app

hypergan build

This builds a standard pytorch onxx file. You can then quantize and/or deploy the model.