SemiSuper_GAN

Implementation of semi-supervised GAN on MNIST dataset with 20% labeled data. The discriminator loss includes Supervised classification loss on labels and GAN loss of Real/Fake on Unsupervised data. Generator loss includes GAN loss along with feature matching loss as proposed in Improved Techniques for Training GANs: https://arxiv.org/pdf/1606.03498.pdf