Implementation of Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks using PyTorch | Paper Train:

• Modify config.py file to specify dataset, batch_size, img_size, ...
• Run python main.py

Results after 3 epochs:

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

Reference

• https://github.com/pytorch/