https://github.com/kumar-shridhar/pytorch-bayesian-dcgan
Bayesian Version of DCGAN
https://github.com/kumar-shridhar/pytorch-bayesian-dcgan
Last synced: about 2 months ago
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Bayesian Version of DCGAN
- Host: GitHub
- URL: https://github.com/kumar-shridhar/pytorch-bayesian-dcgan
- Owner: kumar-shridhar
- Created: 2018-09-11T10:27:39.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-12-04T07:24:55.000Z (over 6 years ago)
- Last Synced: 2025-01-28T17:17:14.543Z (4 months ago)
- Size: 25.1 MB
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deep Convolution Generative Adversarial Networks
This example implements the paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](http://arxiv.org/abs/1511.06434)
The implementation is very close to the Torch implementation [dcgan.torch](https://github.com/soumith/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](https://github.com/fyu/lsun) 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)
```