Pasky’s Log

So I didn’t get around to seriously (besides running a few examples) play with Keras (a powerful library for building fully-differentiable machine learning models aka neural networks) – until now. And I have been a bit surprised about how tricky it actually was for me to get a simple task running, despite (or maybe because of) all the docs available already.

The thing is, many of the “basic examples” gloss over exactly how the inputs and mainly outputs look like, and that’s important. Especially since for me, the archetypal simplest machine learning problem consists of binary classification, but in Keras the canonical task is categorical classification. Only after fumbling around for a few hours, I have realized this fundamental rift.

The examples (besides LSTM sequence classification) silently assume that you want to classify to categories (e.g. to predict words etc.), not do a binary 1/0 classification. The consequences are that if you naively copy the example MLP at first, before learning to think about it, your model will never learn anything and to add insult to injury, always show the accuracy as 1.0.

So, there are a few important things you need to do to perform binary classification:

• Pass output_dim=1 to your final Dense layer (this is the obvious one).
• Use sigmoid activation instead of softmax – obviously, softmax on single output will always normalize whatever comes in to 1.0.
• Pass class_mode='binary' to model.compile() (this fixes the accuracy display, possibly more; you want to pass show_accuracy=True to model.fit()).

Other lessons learned:

• For some projects, my approach of first cobbling up an example from existing code and then thinking harder about it works great; for others, not so much…
• In IPython, do not forget to reinitialize model = Sequential() in some of your cells – a lot of confusion ensues otherwise.
• Keras is pretty awesome and powerful. Conceptually, I think I like NNBlocks‘ usage philosophy more (regarding how you build the model), but sadly that library is still very early in its inception (I have created a bunch of gh issues).

(Edit: After a few hours, I toned down this post a bit. It wasn’t meant at all to be an attack at Keras, though it might be perceived by someone as such. Just as a word of caution to fellow Keras newbies. And it shouldn’t take much to improve the Keras docs.)

In part of my research, I have been heavily involved with building portfolios of optimization algorithms. Optimization algorithms stay at the root of many computational tasks, from designing laser mirror systems to neural network training. We want to find a minimum (or maximum) of some mathematical function, and for some functions it’s easier than for others.

For very many fairly hairy functions, the best state-of-art optimization algorithm is based on genetic algorithms and it’s called CMA-ES. It also has a very nice Python implementation by its original author, Nikolaus Hansen.

CMA-ES is still not as good as it could be on some functions with many local optima, but its performance can be much improved by establishing a restart strategy that will repeatedly restart it with varying population size and parameters. The best performing restart strategy is BIPOP-CMA-ES and unfortunately, it had no Python implementation so far. I took care of that more than a month ago, but since it’s taking some time to get my modifications upstreamed, if anyone would find that useful,

here is a patch for CMA-1.1.02 adding BIPOP restart strategy