DCGAN on MNIST

The code for this implementation is on github.

As part of the fast.ai Deep Learning For Coders part 2 course, we implemented the original GAN and DCGAN. The original GAN implementation uses a simple multi-layer perceptrons for the generator and discriminator, and it does not work very well. DCGAN uses a convolutional architecture and does better. However, the result in the course notebook did not look impressive despite many thousands of epochs of training. It’s possible to get it to a better state as I have succeeded in doing here. There is room for more improvement still. I suspect the instructor did not try too hard to show that though, because the newer WGAN, introduced a bit later, is better and easier to train. However, this post is about implementing the DCGAN.

It took me a while to get it to work well. Despite trying many things, what seemed to finally do it was switching from Adam optimizer to RMSPROP, and weight clipping. These things, together with Dropout and Batch Normalization, stabilized training. I did not do comprehensive experiments, but 5×5 convolutions seemed to perform better on this particular dataset.

Here you can see how the GAN learns to generate increasingly better novel handwritten digits.

output over epochs

The outputs look realistic, but they can be improved. Future improvements and code are on github.

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