Owl in Galapagos

TLDR: predicting rectangles with two neural networks and galapagos.


galapagos on owl 3

galapagos used to discover the best shapes for two time series neural networks

just realised that galapagos would potentially be very useful (or actually, another backprop NN might be even faster) for testing out optimal ‘window’ sizes for a time series neural network (the ‘view’ that the neural network sees when it learns to predict a number series).

When predicting with a time series neural network, one of the problems that bugged me has been that we don’t know what the best size is for prediction (called look_back in this tutorial). Too small and the NN learns that it should only go up or down, too large and it misses out on too many details.

This is where galapagos (or any other appropriate learner) comes in to help find the optimal range within which the best predictions can happen.

Galapagos was used to test 7 parameters that directly affected the neural network shape and learning rate (1 for window size, 3 for each NN : number of hidden neurons, learning rate, and steepness of the sigmoid activation function).


after 15 minutes or so, it gave me some pretty decent answers for the parameters required for learning two separate lists of parameters.

It was quite interesting to see that the learning rate varied quite a bit between the two (one was at 0.21, and another at 0.62), and alpha( used to define the steepness of the sigmoid activation function) was at 1.344 and 0.887 respectively (and then i realised that in fact learning rate is inversely proportional to alpha).

The number of hidden neurons (defining the steepness of the sigmoid activation function) stayed relatively similar at hidden = 4 neurons and 5 neurons respectively. but then, i wouldn’t have guessed if i just used a random middling number between inputs and outputs.


the resulting prediction was a prediction of a series of two parameters that define a rectangle.

Ground truth dataset in Grey, predicted dataset in Yellow.

galapagos on owl 4

the accuracy falloff after training

galapagos on owl 5 initial

before training

galapagos on owl 5 initial2

initial hand tweaking of parameters (didn’t know which ones are best to tweak)

galapagos on owl 5 learnt

so i machine learned those parameters and it got some pretty decent predictions

galapagos on owl 5 shifted

and shifted some starting rectangles and realised it predicts about up to 10 rectangles reliably enough before doing some crazy things.

plugins used: OWL, galapagos


Author: Tang Li Qun

Design Architect, Computational Designer, 3D Generalist, Machine Learning enthusiast, Tinkerer

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