A few months ago I saw a blog post about generating cat faces with different kinds of generative adversarial networks. I never trained a WGAN before, and I had some free time on my hands so I decided to rewrite the code and try it out myself. I skipped a lot of the parameter tuning and just used the hyper-parameters described in the blog post.
- python 3.6
- opencv-python
- numpy
- torch its probably a good idea to use 1.3.0 or higher
I used DCGAN and WGAN (with gradient penalty). I run my experiments on the cat dataset, and I also tried to generate flowers using the 102 category flower dataset. In both cases, I downsampled the images to 64x64, and since the datasets share some characteristics, I used the same hyper-parameters.
python3 train.py --dataset <DATASET> --gan_type <GAN_TYPE>
Where <DATASET> is either "cats" or "flowers" and <GAN_TYPE> is one of "dcgan" or "wgan_gp".
For both datasets and networks, the training was stable, and I ended up with more-or-less good looking, believable cats/flowers. I trained the DCGAN models for 280 epoch, and the WGAN-GP models for about 700.
