GANs were invented by Ian Goodfellow in 2014 and first described in the paper
Generative Adversarial Nets https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks
It was first described by Radford et. al. in the paper https://arxiv.org/pdf/1511.06434.pdf.

some of the best practies shown in ganhacks
https://github.com/soumith/ganhacks
some awesome applications about gans
https://github.com/nashory/gans-awesome-applications

we will use the Celeb-A Faces dataset which can be downloaded at the linked site,
paper: Deep Learning Face Attributes in the Wild
dataset web http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
Download link:
Baidu Drive https://pan.baidu.com/s/1eSNpdRG#list/path=%2F
Google Drive https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg

The dataset will download as a file named img_align_celeba.zip.
Once downloaded, create a directory named celeba and extract the zip file into that directory.
Then, set the dataroot input for this notebook to the celeba directory you just created.
The resulting directory structure should be:

/path/to/celeba
    -> img_align_celeba  
        -> 188242.jpg
        -> 173822.jpg
        -> 284702.jpg
        -> 537394.jpg
           ...

This is an important step because we will be using the ImageFolder dataset class,
which requires there to be subdirectories in the dataset’s root folder.
Now, we can create the dataset, create the dataloader,
set the device to run on, and finally visualize some of the training data.

how to run a test?

bash run.sh

It could work.

In priciple,the GAN optimization game is simple,the gibberish is typical
for a generator trained without proper care! gan is hard to train. such as:
Tried the resize code for 128x128 and finding that the Discriminator flatlines to 0.0000
A couple of tricks are necessary for to facilitate training.
Here is a blog: http://torch.ch/blog/2015/11/13/gan.html#balancing-the-gan-game

2 epoch output

Loss_G and Loss_D

real and fake images compare

5 epoch output

Loss_G and Loss_D

real and fake images compare

10 epoch output

Loss_G and Loss_D

real and fake images compare

20 epoch output

Loss_G and Loss_D

real and fake images compare

200 epoch output

Loss_G and Loss_D

real and fake images compare