PyTorch implementation of Energy-Based Generative Adversarial Networks
Some code derives from pytorch official example dcgan
After every 100 training iterations, the files real_samples.png
and fake_samples.png
are written to disk
with the samples from the generative model.
After every epoch, models are saved to: netG_epoch_%d.pth
and netD_epoch_%d.pth
Repelling regularizer
-h, --help show this help message and exit
--dataroot path to dataset
--workers number of data loading workers
--batchSize input batch size
--imageSize the height / width of the input image to network
--nz size of the latent z vector
--ngf
--ndf
--margin margin of the energy loss
--niter number of epochs to train for
--lr learning rate, default=0.0002
--beta1 beta1 for adam. default=0.5
--cuda enables cuda
--netG path to netG (to continue training)
--netD path to netD (to continue training)
--outf folder to output images and model checkpoints