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cascade correlation: sticking to implementation by Fahlman and not paper

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Shin
2016-05-07 23:21:40 +02:00
parent eaafc27211
commit 0221158a56
2 changed files with 73 additions and 50 deletions
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39
include/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.h
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@@ -3,7 +3,6 @@
#include "../Cascade/Network.h"
#include "../FeedForward/Network.h"
#include "../Learning/QuickPropagation.h"
#include "../ActivationFunction/Tangents.h"
#include <random>
#include <algorithm>
@@ -22,16 +21,16 @@ namespace NeuralNetwork {
std::size_t inputs = patterns[0].first.size();
std::size_t outputs = patterns[0].second.size();
Cascade::Network network(inputs, outputs, NeuralNetwork::ActivationFunction::Tangents());
Cascade::Network network(inputs, outputs, *_activFunction.get());
network.randomizeWeights();
int step = 0;
std::size_t step = 0;
float error = trainOutputs(network, patterns);
while(step++ < 15 && error > _maxError) {
while(step++ < _maxHiddenUnits && error > _maxError) {
std::vector<std::shared_ptr<Neuron>> candidates = createCandidates(network.getNeuronSize() - outputs);
std::shared_ptr<Neuron> candidate=trainCandidates(network, candidates, patterns);
std::pair<std::shared_ptr<Neuron>, std::vector<float>> candidate = trainCandidates(network, candidates, patterns);
addBestCandidate(network, candidate);
error = trainOutputs(network, patterns);
@@ -59,10 +58,20 @@ namespace NeuralNetwork {
_distribution = std::uniform_real_distribution<>(-weightRange, weightRange);
}
void setMaximumHiddenNeurons(std::size_t neurons) {
_maxHiddenUnits = neurons;
}
void setActivationFunction(const ActivationFunction::ActivationFunction &function) {
_activFunction = std::shared_ptr<ActivationFunction::ActivationFunction>(function.clone());
}
protected:
std::shared_ptr<ActivationFunction::ActivationFunction> _activFunction = std::make_shared<ActivationFunction::Sigmoid>(-4.9);
float _minimalErrorStep = 0.00005;
float _maxError;
float _weightRange;
std::size_t _maxHiddenUnits = 20;
std::size_t _numberOfCandidates;
std::mt19937 _generator;
std::uniform_real_distribution<> _distribution;
@@ -84,19 +93,23 @@ namespace NeuralNetwork {
float trainOutputs(Cascade::Network &network, const std::vector<TrainingPattern> &patterns);
std::shared_ptr<Neuron> trainCandidates(Cascade::Network &network, std::vector<std::shared_ptr<Neuron>> &candidates, const std::vector<TrainingPattern> &patterns);
std::pair<std::shared_ptr<Neuron>, std::vector<float>> trainCandidates(Cascade::Network &network, std::vector<std::shared_ptr<Neuron>> &candidates,
const std::vector<TrainingPattern> &patterns);
void addBestCandidate(Cascade::Network &network, const std::shared_ptr<Neuron> &candidate) {
void addBestCandidate(Cascade::Network &network, const std::pair<std::shared_ptr<Neuron>, std::vector<float>> &candidate) {
auto neuron = network.addNeuron();
neuron->setWeights(candidate->getWeights());
neuron->setActivationFunction(candidate->getActivationFunction());
float weightPortion = network.getNeuronSize() - network.outputs();
neuron->setWeights(candidate.first->getWeights());
neuron->setActivationFunction(candidate.first->getActivationFunction());
std::size_t outIndex = 0;
for(auto &n :network.getOutputNeurons()) {
auto weights = n->getWeights();
for(auto& weight: weights) {
weight *=0.7;
for(auto &weight: weights) {
weight *= 0.7;
}
weights[weights.size()-1] = _distribution(_generator);
weights[weights.size() - 1] = -candidate.second[outIndex] * weightPortion;//_distribution(_generator);
outIndex++;
n->setWeights(weights);
}
}
@@ -107,7 +120,7 @@ namespace NeuralNetwork {
for(std::size_t i = 0; i < _numberOfCandidates; i++) {
candidates.push_back(std::make_shared<Neuron>(id));
candidates.back()->setInputSize(id);
candidates.back()->setActivationFunction(NeuralNetwork::ActivationFunction::Tangents());
candidates.back()->setActivationFunction(*_activFunction.get());
for(std::size_t weightIndex = 0; weightIndex < id; weightIndex++) {
candidates.back()->weight(weightIndex) = _distribution(_generator);