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parity + debug output

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Shin
2016-05-19 23:30:56 +02:00
parent b8c4d25eb2
commit 2607867a25
3 changed files with 13 additions and 10 deletions
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2
include/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.h
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@@ -52,6 +52,8 @@ namespace NeuralNetwork {
error = trainOutputs(network, patterns);
}
std::cout << error << "\n";
if(_prunningStatus && error*1.01 >= lastError) { // it is not getting bettter
network.removeLastHiddenNeuron();
error=lastError;

16
include/NeuralNetwork/ProblemSets/Parity.h
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@@ -6,15 +6,15 @@ namespace NeuralNetwork {
namespace ProblemSets {
typedef std::pair<std::vector<float>, std::vector<float>> TrainingPattern;
std::vector<TrainingPattern> Parity3() {
std::vector<TrainingPattern> Parity3(float min = 0.0) {
return {
{{0,0,0},{0}},
{{0,0,1},{1}},
{{0,1,0},{1}},
{{0,1,1},{0}},
{{1,0,0},{1}},
{{1,0,1},{0}},
{{1,1,0},{0}},
{{min,min,min},{min}},
{{min,min,1},{1}},
{{min,1,min},{1}},
{{min,1,1},{min}},
{{1,min,min},{1}},
{{1,min,1},{min}},
{{1,1,min},{min}},
{{1,1,1},{1}},
};
}

5
src/NeuralNetwork/ConstructiveAlgorithms/CascadeCorrelation.cpp
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@@ -48,7 +48,8 @@ float CascadeCorrelation::trainOutputs(Cascade::Network &network, const std::vec
}
while(iteration++ < _maxOutputLearningIterations && iterWithoutImporvement < _maxOutputLearningIterationsWithoutChange);
std::cout << "outputLearning: " << error << ", last: " << lastError << ", iters: " << iteration << "\n";
// std::cout << "outputLearning: " << error << ", last: " << lastError << ", iters: " << iteration << "\n";
for(std::size_t neuron = 0; neuron < outputs; neuron++) {
network.getOutputNeurons()[neuron]->setWeights(p[1][neuron + 1].getWeights());
}
@@ -144,7 +145,7 @@ float CascadeCorrelation::trainOutputsRandom(std::size_t step, Cascade::Network
FeedForward::Network &p = *possibleOutputs[bestNetwork];
std::cout << "network: " << bestNetwork << ", error: " << bestScore << "\n";
std::cout << "network: " << bestNetwork << "\n";
for(std::size_t neuron = 0; neuron < outputs; neuron++) {
network.getNeuron(network.getNeuronSize() - outputs + neuron)->setWeights(p[1][neuron + 1].getWeights());