Neural evolution algorithms implementation (CNE, NEAT, HyperNEAT) #686
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@@ -12,9 +12,6 @@ | |
| #include <map> | ||
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| #include <mlpack/core.hpp> | ||
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| #include "link_gene.hpp" | ||
| #include "neuron_gene.hpp" | ||
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@@ -44,15 +41,13 @@ class Genome { | |
| const std::vector<LinkGene>& linkGenes, | ||
| ssize_t numInput, | ||
| ssize_t numOutput, | ||
| double fitness, | ||
| double adjustedFitness): | ||
| aId(id), | ||
| aNeuronGenes(neuronGenes), | ||
| aLinkGenes(linkGenes), | ||
| aNumInput(numInput), | ||
| aNumOutput(numOutput), | ||
| aFitness(fitness), | ||
| aAdjustedFitness(adjustedFitness) | ||
| {} | ||
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@@ -64,7 +59,6 @@ class Genome { | |
| aLinkGenes = genome.aLinkGenes; | ||
| aNumInput = genome.aNumInput; | ||
| aNumOutput = genome.aNumOutput; | ||
| aFitness = genome.aFitness; | ||
| aAdjustedFitness = genome.aAdjustedFitness; | ||
| } | ||
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@@ -80,7 +74,6 @@ class Genome { | |
| aLinkGenes = genome.aLinkGenes; | ||
| aNumInput = genome.aNumInput; | ||
| aNumOutput = genome.aNumOutput; | ||
| aFitness = genome.aFitness; | ||
| aAdjustedFitness = genome.aAdjustedFitness; | ||
| } | ||
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@@ -120,12 +113,6 @@ class Genome { | |
| // Set output length. | ||
| void NumOutput(ssize_t numOutput) { aNumOutput = numOutput; } | ||
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| // Set fitness. | ||
| void Fitness(double fitness) { aFitness = fitness; } | ||
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@@ -150,9 +137,6 @@ class Genome { | |
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| // Whether specified neuron id exist in this genome. | ||
| bool HasNeuronId(ssize_t id) const { | ||
| for (ssize_t i=0; i<NumNeuron(); ++i) { | ||
| if (aNeuronGenes[i].Id() == id) { | ||
| return true; | ||
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@@ -216,137 +200,86 @@ class Genome { | |
| return false; | ||
| } | ||
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| // Set neurons' input and output to zero. | ||
| void Flush() { | ||
| for (ssize_t i=0; i<aNeuronGenes.size(); ++i) { | ||
| aNeuronGenes[i].Activation(0); | ||
| aNeuronGenes[i].Input(0); | ||
| } | ||
| } | ||
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| // Sort neuron genes by depth. | ||
| static bool CompareNeuronGene(NeuronGene ln, NeuronGene rn) { | ||
| return (ln.Depth() < rn.Depth()); | ||
| } | ||
| void SortHiddenNeuronGenes() { | ||
| std::sort(aNeuronGenes.begin() + NumInput() + NumOutput(), aNeuronGenes.end(), CompareNeuronGene); | ||
| } | ||
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| // Sort link genes by toNeuron's depth. | ||
| void SortLinkGenes() { | ||
| struct DepthAndLink | ||
| { | ||
| double depth; | ||
| LinkGene link; | ||
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| DepthAndLink(double d, LinkGene& l) : depth(d), link(l) {} | ||
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| bool operator < (const DepthAndLink& dL) const | ||
| { | ||
| return (depth < dL.depth); | ||
| } | ||
| }; | ||
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| std::vector<double> toNeuronDepths; | ||
| for (ssize_t i=0; i<aLinkGenes.size(); ++i) { | ||
| NeuronGene toNeuron = GetNeuronById(aLinkGenes[i].ToNeuronId()); | ||
| toNeuronDepths.push_back(toNeuron.Depth()); | ||
| } | ||
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| std::vector<DepthAndLink> depthAndLinks; | ||
| ssize_t linkGenesSize = aLinkGenes.size(); | ||
| for (ssize_t i=0; i<linkGenesSize; ++i) { | ||
| depthAndLinks.push_back(DepthAndLink(toNeuronDepths[i], aLinkGenes[i])); | ||
| } | ||
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| std::sort(depthAndLinks.begin(), depthAndLinks.end()); | ||
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| for (ssize_t i=0; i<linkGenesSize; ++i) { | ||
| aLinkGenes[i] = depthAndLinks[i].link; | ||
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There was a problem hiding this comment. I think, we could speed this up, if we would just store the index of all linkGenes, an use another list, that contains all linkGenes as a reference. |
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| } | ||
| } | ||
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| // Activate genome. The last dimension of input is always 1 for bias. 0 means no bias. | ||
| void Activate(std::vector<double>& input) { | ||
| assert(input.size() == aNumInput); | ||
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There was a problem hiding this comment. If we reset the activation at the beginning, we can't use the activation at time (t - 1) from a unit that has a recurrent connection, right? I'm not sure we have to reset the activation at all, we could just overwrite the existing. There was a problem hiding this comment. @zoq Yeah, seems reasonable. I add the Flush() step to make sure it will be correct for activation. But I also think it seems not a required step. |
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| SortLinkGenes(); | ||
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| // Set all neurons' input to be 0. | ||
| for (ssize_t i=0; i<NumNeuron(); ++i) { | ||
| aNeuronGenes[i].Input(0); | ||
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There was a problem hiding this comment. If we reset the input, we also lose the input of a recurrent connection, for the next iteration. There was a problem hiding this comment. A recurrent connection's input is the neuron's activation, so I think it won't lose actually. |
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| } | ||
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| // Set input neurons. | ||
| for (ssize_t i=0; i<aNumInput; ++i) { | ||
| aNeuronGenes[i].Activation(input[i]); // assume INPUT, BIAS, OUTPUT, HIDDEN sequence | ||
| } | ||
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| // Activate hidden and output neurons. | ||
| for (ssize_t i = 0; i < NumLink(); ++i) { | ||
| if (aLinkGenes[i].Enabled()) { | ||
| ssize_t toNeuronIdx = GetNeuronIndex(aLinkGenes[i].ToNeuronId()); | ||
| ssize_t fromNeuronIdx = GetNeuronIndex(aLinkGenes[i].FromNeuronId()); | ||
| double input = aNeuronGenes[toNeuronIdx].Input() + | ||
| aNeuronGenes[fromNeuronIdx].Activation() * aLinkGenes[i].Weight(); | ||
| aNeuronGenes[toNeuronIdx].Input(input); | ||
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| if (i == NumLink() - 1) { | ||
| aNeuronGenes[toNeuronIdx].CalcActivation(); | ||
| } else if (GetNeuronIndex(aLinkGenes[i + 1].ToNeuronId()) != toNeuronIdx) { | ||
| aNeuronGenes[toNeuronIdx].CalcActivation(); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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@@ -355,7 +288,7 @@ class Genome { | |
| std::vector<double> Output() { | ||
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There was a problem hiding this comment. Let's use a reference here, also, it might be a good idea, to use an arma::vec here:
There was a problem hiding this comment. Yeah, right. I did some testing, and encounter an unexpected behaviour. This are the modifications I made: https://gist.github.com/zoq/181c036cb4b903f8e7cb2977639ee6b8 I've also, printed the activation function type in CalcActivation(), which is the RELU function, but I expected it to be SIGMOID, do we randomly choose the activation function, somewhere? The seed genome uses the SIGMOID for all neurons except for the BIAS neuron. |
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| std::vector<double> output; | ||
| for (ssize_t i=0; i<aNumOutput; ++i) { | ||
| output.push_back(aNeuronGenes[aNumInput + i].Activation()); | ||
| } | ||
| return output; | ||
| } | ||
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@@ -390,9 +323,6 @@ class Genome { | |
| // Output length. | ||
| ssize_t aNumOutput; | ||
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| // Genome fitness. | ||
| double aFitness; | ||
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Maybe it's a good idea, to add a GetDepthById function, in this case we could avoid to copy the object.