Skip to content

Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images.

Notifications You must be signed in to change notification settings

255BITS/cppn-gan-vae-tensorflow

 
 

Repository files navigation

cppn-gan-vae tensorflow

Train Compositional Pattern Producing Network as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images.

Morphing

Run python train.py from the command line to train from scratch and experiment with different settings.

sampler.py can be used inside IPython to interactively see results from the models being trained.

See my blog post at blog.otoro.net for more details.

I tested the implementation on TensorFlow 0.60.

Used images2gif.py written by Almar Klein, Ant1, Marius van Voorden.

License

BSD - images2gif.py

MIT - everything else

About

Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%