Endless repeat 'no more nodes left to remove!' about neataptic HOT 4 CLOSED

@momire the MNIST dataset is quite a big (and a little more complex) dataset. Calling the evolve() function won't necessarily converge, and it's very time-expensive due to the networks being very big (~800 nodes). For me, one generation with 60 genomes takes about 20 seconds. (PS: i'm working on speeding up evolution drastically using webworkers).

However, it is definitely possible to evolve a network to work on the MNIST dataset, it just requires the use of various options. I'll be posting an example soon, but in the mean time, use the option { log: 1} and you will see the network improving.

@alancnet I think I might create an extra option that disables warnings in functions that weren't called by the user. So warnings stay enabled when the user calles mutate() but not when it happens from the evolve() function.

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I tried your advice and it worked!
Thanks for your help.
(Unfortunately, I don't know how to use Web Walker for my skill)

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I agree warnings should be disabled by default, but there is a simple way to disable them:

require('neataptic').Config.warnings = false

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So a small example of training options which seems to work quite well for me (but still takes super long):

var mnist = require('mnist');
var neataptic = require('neataptic');

var set = mnist.set(700,20);
var trainingSet = set.training;
var testset = set.test;

var network= new neataptic.Network(784,10);

for(var i = 0; i < network.connections.length; i++){
  network.connections[i].weight = Math.random() * .02 - .01;
}

var results = network.evolve(trainingSet, {
  iterations: 150, // just so it stopped after a while
  elitism: 5, // 10%
  mutationRate: 0.7, // fairly high, yes
  equal: true,
  growth: 0, // this can create very large networks, you can also remove this 
  log: 1 // see how well it's doing
});

As you might have noticed, I have a custom weight initialisation algorithm. Neataptic doesn't take into account that 784 connections cause any (sigmoid) connected neuron to activate 1 with default weight init. I already have a clean weight initialisation process in my head which i'm coding now.

If you need more help with evolving the MNIST set let me know.

PS: I think the MNIST dataset works better with a backprop + evolve technique, which I might incorperate in the future.

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