It implements a simple G and D network, where both G and D network consist of 3 fully connected layers. The training data is generated from gaussian distribution whose mean = 4 and variance = 1.25. Here is the training curve which indicates how generated data converge to true guassian distribution.
Wasserstein GAN with gradient penalty
