Improved-GAN

PyTorch implementation of the paper Improved Techniques for Training GANs for MNIST.

Based on openai code along with implementation of Sleepychord in PyTorch.

Usage

usage: main.py [-h] [--dataroot DATAROOT] [--savedir SAVEDIR] [--workers WORKERS]
               [--nexamples NEXAMPLES] [--batch_size BATCHSIZE] [--image_size IMAGESIZE] [--nz NZ]
               [--epochs EPOCHS] [--lr LR] [--beta1 BETA1]
               [--cuda] [--ngpu NGPU] [--manual_seed MANUALSEED] [--resume]

optional arguments:
  -h, --help                   show this help message and exit
  --dataroot DATAROOT          path to dataset, default=data
  --savedir SAVEDIR            path for saving models and logs, default=log
  --workers WORKERS            number of data loading workers, default=2
  --nexamples NEXAMPLES        number of examples per class to use as supervised data, default=10
  --batch_size BATCHSIZE       input batch size, default=64
  --image_size IMAGESIZE       the height / width of the input image to network, default=28
  --nz NZ                      size of the latent z vector, default=100
  --epochs EPOCHS              number of epochs to train for, default=10
  --lr LR                      learning rate, default=0.003
  --beta1 BETA1                beta1 for adam. default=0.5
  --cuda                       enables cuda
  --ngpu NGPU                  number of GPUs to use
  --manual_seed MANUALSEED     seed for random number generator
  --resume                     resume from last checkpoint, requires generator.pth and discriminator.pth in savedir