Awesome

Wasserstein GAN

This repository contains implementation of WGAN and DCGAN in Julia using Knet. Here is a detailed report about WGAN.

Usage

Just run julia train.jl with the following arguments. Note that if procedure is gan, discriminator weights won't get clipped.

usage: train.jl [--gpu GPU] [--dn DN] [--type TYPE] [--data DATA]
                [--procedure PROCEDURE] [--zsize ZSIZE]
                [--epochs EPOCHS] [--report REPORT]
                [--batchsize BATCHSIZE] [--lr LR] [--clip CLIP]
                [--opt OPT] [--leak LEAK] [--out OUT] [-h]

WGAN Implementation in Knet

optional arguments:
  --gpu GPU             GPU ID if -1 don't use GPU (type: Int64,
                        default: 0)
  --dn DN               Train discriminator n times (type: Int64,
                        default: 1)
  --type TYPE           Type of model one of: [dcganbn (regular
                        DCGAN), mlpg (Generator is MLP),         mlpgd
                        (Both MLP), dcgan (Generator has no BN and has
                        constant filter size)] (default: "dcganbn")
  --data DATA           Dataset dir (lmdb) (default: "/home/cem/lsun")
  --procedure PROCEDURE
                        Training procedure. gan or wgan (default:
                        "gan")
  --zsize ZSIZE         Noise vector dimension (type: Int64, default:
                        100)
  --epochs EPOCHS       Number of training epochs (type: Int64,
                        default: 20)
  --report REPORT       Report loss in n iterations (type: Int64,
                        default: 500)
  --batchsize BATCHSIZE
                        Minibatch Size (type: Int64, default: 64)
  --lr LR               Learning rate (type: Float64, default: 0.0002)
  --clip CLIP           Clip value (type: Float64)
  --opt OPT             Optimizer, one of: [adam, rmsprop] (default:
                        "adam")
  --leak LEAK           LeakyReLU leak. (type: Float64, default: 0.2)
  --out OUT             Output directory for saving model and
                        generating images (default: "./models")
  -h, --help            show this help message and exit

Learning Curves and Outputs

Images below are the plots of the outputs of the implementation in this repo. Original outputs can be found here

Standard GAN

Wasserstein GAN