Add WGANGP to comparison

[htt210]

Hi Andrew, I've just done some experiments with WGAN with Gradient Penalty (Improved Training of Wasserstein GANs, Gulrajani et al.) and found that it can converge to a reasonable solution on the synthetic dataset. Although WGANGP does not converge as fast as bayesgan, I think it would be nice if you could add WGANGP to the baselines in your experiments. Here is the output of my (very bad) implementation of WGANGP after 8000 iterations

nx = 100
nz = 10
batchSize = 64
Gconfig = [('Linear', (nz, 1000)), ('ReLU', ()), ('Linear', (1000, nx))]
Dconfig = [('Linear', (nx, 1000)), ('ReLU', ()), ('Linear', (1000, 1))]
optimizer = 'Adam'
optimParams = {'lr': 1e-4, 'betas': (0.5, 0.9)}

[ysaatchi]

This is cool -- a nice extension may be to run WGAN with GP on CIFAR or MNIST and see if you get better semi-supervised results.