CVAE-GAN_tensorlayer

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CVAE-GAN_tensorlayer

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A CVAE-GAN implementation with tensorlayer1.x

// repository documentation

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# CVAE-GAN_tensorlayer
A CVAE-GAN implementation with tensorlayer.

# Examples
## training VAE output
![0_0](https://github.com/One-sixth/CVAE-GAN_tensorlayer/blob/master/imgs/0_0.jpg)
![0_1600](https://github.com/One-sixth/CVAE-GAN_tensorlayer/blob/master/imgs/0_1600.jpg)
![1_1600](https://github.com/One-sixth/CVAE-GAN_tensorlayer/blob/master/imgs/1_1600.jpg)
![3_600](https://github.com/One-sixth/CVAE-GAN_tensorlayer/blob/master/imgs/3_600.jpg)
## testing VAE output and Recon output
![0](https://github.com/One-sixth/CVAE-GAN_tensorlayer/blob/master/test_output/0.jpg)
![1](https://github.com/One-sixth/CVAE-GAN_tensorlayer/blob/master/test_output/7.jpg)
![2](https://github.com/One-sixth/CVAE-GAN_tensorlayer/blob/master/test_output/14.jpg)
![3](https://github.com/One-sixth/CVAE-GAN_tensorlayer/blob/master/test_output/31.jpg)

# Dependent
tensorflow 
tensorlayer 
numpy 
progressbar2

My test environment is tensorflow-gpu-1.10, tensorlayer-1.91, gtx970m-3g.

# Some Problem and Attention
emmm... This implementation may have some differences with the page. 
I try WGAN-GP and LS-GAN 's loss, but the result is not good. Maybe my code is wrong. 
Then I change discriminator become autoencoder and try BEGAN 's loss, It is look good. 
 
The VAE output after training is still blurry, but I found that the reconstructed image of the discriminator can make the VAE output clearer. 
The main network structure is my simple custom resnet, which should be different to DCGAN.

# Training Log
My training process output and logs can be found in the imgs and logs folders respectively. 
The training log has been compressed and only needs to be decompressed.

# How to use
## Test
```
python3 test.py
```
I uploaded model weights in the repository. You can test with my weight right away. 
Test output can be found in the test_output folder. 
The upper three lines of each image are the VAE output, and the next three lines are the reconstructed output of the discriminator.

## Train
```
python3 train.py
```
Just simple start train.py. The train will be start. 
It will automatically reuse the previous weights if they are still there. 
*.npz files is the networks weight. If you want a new training, just delete them simply. 
If you see OOM, try to reduce batch_size or train with cpu.

# Page Link
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training 
https://arxiv.org/abs/1703.10155

CVAE-GAN_tensorlayer

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