{"id":17069668,"url":"https://github.com/kumar-shridhar/pytorch-bayesian-dcgan","last_synced_at":"2025-03-23T10:44:19.823Z","repository":{"id":117473421,"uuid":"148300488","full_name":"kumar-shridhar/PyTorch-Bayesian-DCGAN","owner":"kumar-shridhar","description":"Bayesian Version of 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Deep Convolution Generative Adversarial Networks\n\nThis example implements the paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434)\n\nThe implementation is very close to the Torch implementation [dcgan.torch](https://github.com/soumith/dcgan.torch)\n\nAfter every 100 training iterations, the files `real_samples.png` and `fake_samples.png` are written to disk\nwith the samples from the generative model.\n\nAfter every epoch, models are saved to: `netG_epoch_%d.pth` and `netD_epoch_%d.pth`\n\n## Downloading the dataset\nYou can download the LSUN dataset by cloning [this repo](https://github.com/fyu/lsun) and running\n```\npython download.py -c bedroom\n```\n\n## Usage\n```\nusage: main.py [-h] --dataset DATASET --dataroot DATAROOT [--workers WORKERS]\n               [--batchSize BATCHSIZE] [--imageSize IMAGESIZE] [--nz NZ]\n               [--ngf NGF] [--ndf NDF] [--niter NITER] [--lr LR]\n               [--beta1 BETA1] [--cuda] [--ngpu NGPU] [--netG NETG]\n               [--netD NETD]\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --dataset DATASET     cifar10 | lsun | imagenet | folder | lfw\n  --dataroot DATAROOT   path to dataset\n  --workers WORKERS     number of data loading workers\n  --batchSize BATCHSIZE\n                        input batch size\n  --imageSize IMAGESIZE\n                        the height / width of the input image to network\n  --nz NZ               size of the latent z vector\n  --ngf NGF\n  --ndf NDF\n  --niter NITER         number of epochs to train for\n  --lr LR               learning rate, default=0.0002\n  --beta1 BETA1         beta1 for adam. default=0.5\n  --cuda                enables cuda\n  --ngpu NGPU           number of GPUs to use\n  --netG NETG           path to netG (to continue training)\n  --netD NETD           path to netD (to continue training)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkumar-shridhar%2Fpytorch-bayesian-dcgan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkumar-shridhar%2Fpytorch-bayesian-dcgan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkumar-shridhar%2Fpytorch-bayesian-dcgan/lists"}