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Deep Convolution Generative Adversarial Networks

This example implements the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

The implementation is very close to the Torch implementation dcgan.torch

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

Downloading the dataset

You can download the LSUN dataset by cloning this repo and running

python download.py -c bedroom

Usage

usage: main.py [-h] --dataset DATASET --dataroot DATAROOT [--workers WORKERS]
               [--batchSize BATCHSIZE] [--imageSize IMAGESIZE] [--nz NZ]
               [--ngf NGF] [--ndf NDF] [--niter NITER] [--lr LR]
               [--beta1 BETA1] [--cuda] [--ngpu NGPU] [--netG NETG]
               [--netD NETD]

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET     cifar10 | lsun | imagenet | folder | lfw
  --dataroot DATAROOT   path to dataset
  --workers WORKERS     number of data loading workers
  --batchSize BATCHSIZE
                        input batch size
  --imageSize IMAGESIZE
                        the height / width of the input image to network
  --nz NZ               size of the latent z vector
  --ngf NGF
  --ndf NDF
  --niter NITER         number of epochs to train for
  --lr LR               learning rate, default=0.0002
  --beta1 BETA1         beta1 for adam. default=0.5
  --cuda                enables cuda
  --ngpu NGPU           number of GPUs to use
  --netG NETG           path to netG (to continue training)
  --netD NETD           path to netD (to continue training)