This code is to accompany "Creating Virtual Universes Using Generative Adversarial Networks" manuscript arXiv:1706.02390. The architecture is an implementation of the DCGAN architecture (arXiv:1511.06434).
How to train:
git clone email@example.com:MustafaMustafa/cosmoGAN.git cd cosmoGAN/networks mkdir data wget http://portal.nersc.gov/project/dasrepo/cosmogan/cosmogan_maps_256_8k_1.npy cd ../
That will download sample data (8k maps) for testing. You can download more data from here. All of this data has been generated using our GAN and can be used to train your own. Original data is available upon request from the authors.
Load pre-trained weights:
First download the weights:
cd cosmoGAN/networks wget http://portal.nersc.gov/project/dasrepo/cosmogan/cosmoGAN_pretrained_weights.tar tar -xvf cosmoGAN_pretrained_weights.tar
Then take a look at networks/load_and_use_pretrained_weights.ipynb notebook for how to run.