HyperGAN
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Getting started
HyperGAN is currently in pre-release and open beta.
Everyone will have different goals when using hypergan. Here are some common uses:

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'

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!

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.

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

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).

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.

hypergan build
This builds a standard pytorch onxx file. You can then quantize and/or deploy the model.
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On this page
Training a network
Deploying a model
Building datasets
Building image datasets
Using search
Manual search
Creating a new configuration
Implementing a paper
Custom research
Using hyperGAN in an app