Adversarially generated Julia sets

You can read this article on my own blog, if you prefer that to medium.

I thought I’d follow up my first article/tutorial about Julia, by showcasing another side of the language’s ecosystem, libraries for machine learning.

Namely, I’ll be displaying Knet and AutoGard, by implementing an image generating adversarial network. For the sake of continuity, I will use the Julia set generator we had in the last articles. We’ll feed the images to this GAN and try to get it to generate its own fractal~ish patterns.

If you don’t know what a GAN is, I suggest you just go read the original whitepaper, it’s one of the easiest and most interesting reads as far as machine learning papers go.

Assuming you don’t have the time to do that, but know some basic machine learning concepts, here’s a quick rundown.

A GAN is essentially two models, one that generates, let’s call it G, and one that discriminates (i.e classifies), D. The goal of D is to differentiate examples from a training data from “fakes”, the goal of G is to produce fakes images that fool D.

Getting started

First, we need to import and modify the code from the previous article, in order to make it generate and n dimensional matrix, with differing Julia sets as its rows. This matrix will server as our real examples for training the discriminator. Also, we need to install the dependencies for this model, namely Knet (which includes AutoGard) and Plots + GR for image generation.

Keep in mind, that there’s some element of randomness to the above code, and you can easily slip into a situation where either the G or the D loss converges to zero. Meaning that either the discriminator is unable to distinguish between images, resulting in no improvements to our generator or the discriminator gets “too good” to ever be fooled by our generator, again, resulting in no improvements to our generator.

Adjusting the various hyperparameters. This is where the hard and expensive part of machine learning would come in, where models have to be tuned through a combination of theory, intuition, luck and trial&error.

Obviously, this model doesn’t have recurrent or convolutional layers, which are a staple of most modern neural networks, especially when dealing with images.

However, if you dig a bit into Knet’s docs, I think you’ll find that adapting this model to use a CNN or a LSTMN won’t be that hard.

The beauty of Knet is that is offers a very nice balance between low level primitives and higher level abstractions, making it very useful if you need models that are somewhat unique. The other amazing thing about it, is its performance when compared to leading frameworks in this domain, such as MXnet and Tensorflow.

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