Awesome
Conditional Generative Adversarial Networks
This example implements a conditional generative adversarial network, as illustrated in Conditional Generative Adversarial Nets
This implementation is very close to the dcgan implementation.
After every 100 training iterations, the files real_samples.png
and fake_samples_%3d.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 MNIST dataset here
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
[--batchSize BATCHSIZE] [--imageSize IMAGESIZE] [--channels CHANNELS]
[--latentdim LATENDIM] [--n_classes N_CLASSES] [--epoch EPOCH] [--lrte LRATE]
[--beta1 BETA1] [--cuda] [--ngpu NGPU] [--netG NETG]
[--netD NETD]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET cifar10 | lsun | mnist
--dataroot DATAROOT path to dataset
--batchSize BATCHSIZE
input batch size
--imageSize IMAGESIZE
the height / width of the input image to network
--channels CHANNELS the channels of the input image to network
--latentdim LATENTDIM the size of the latent vector
--n_classes N_CLASSES the number of classes/labels in the dataset
--epoch EPOCH number of epochs to train for
--lrate LRATE learning rate, default=0.0002
--beta BETA beta for adam. default=0.5
--beta1 BETA1 beta1 for adam. default=0.999
--output folder to output images. defualt=.
--randomseed