123 lines
2.8 KiB
C++
123 lines
2.8 KiB
C++
#pragma once
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#include <vector>
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#include <cmath>
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#include <NeuralNetwork/FeedForward/Network.h>
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#include "CorrectionFunction/Linear.h"
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namespace NeuralNetwork {
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namespace Learning {
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/** @class BackPropagation
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* @brief
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*/
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class BackPropagation {
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public:
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inline BackPropagation(FeedForward::Network &feedForwardNetwork, CorrectionFunction::CorrectionFunction *correction = new CorrectionFunction::Linear()):
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network(feedForwardNetwork), correctionFunction(correction),learningCoefficient(0.4), slopes() {
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resize();
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}
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virtual ~BackPropagation() {
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delete correctionFunction;
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}
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BackPropagation(const BackPropagation&)=delete;
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BackPropagation& operator=(const NeuralNetwork::Learning::BackPropagation&) = delete;
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void teach(const std::vector<float> &input, const std::vector<float> &output);
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inline virtual void setLearningCoefficient (const float& coefficient) { learningCoefficient=coefficient; }
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float getMomentumWeight() const {
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return momentumWeight;
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}
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void setMomentumWeight(const float& m) {
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momentumWeight=m;
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resize();
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}
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float getWeightDecay() const {
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return weightDecay;
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}
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void setWeightDecay(const float& wd) {
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weightDecay=wd;
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}
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std::size_t getBatchSize() const {
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return batchSize;
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}
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void setBatchSize(std::size_t size) {
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batchSize = size;
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}
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protected:
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virtual inline void resize() {
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if(slopes.size()!=network.size())
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slopes.resize(network.size());
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for(std::size_t i=0; i < network.size(); i++) {
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if(slopes[i].size()!=network[i].size())
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slopes[i].resize(network[i].size());
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}
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if(deltas.size() != network.size())
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deltas.resize(network.size());
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bool resized = false;
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for(std::size_t i = 0; i < network.size(); i++) {
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if(deltas[i].size() != network[i].size()) {
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deltas[i].resize(network[i].size());
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resized = true;
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if(i > 0) {
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for(std::size_t j = 0; j < deltas[i].size(); j++) {
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deltas[i][j].resize(network[i - 1].size());
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std::fill(deltas[i][j].begin(),deltas[i][j].end(),0.0);
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}
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}
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}
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}
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if(momentumWeight > 0.0 && (resized || lastDeltas.size() != deltas.size())) {
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lastDeltas = deltas;
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}
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}
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virtual void computeDeltas(const std::vector<float> &input);
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void updateWeights();
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virtual void computeSlopes(const std::vector<float> &expectation);
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virtual void endBatch() {
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}
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FeedForward::Network &network;
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CorrectionFunction::CorrectionFunction *correctionFunction;
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float learningCoefficient;
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float momentumWeight = 0.0;
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float weightDecay = 0.0;
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std::size_t batchSize = 1;
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std::size_t currentBatchSize = 0;
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std::vector<std::vector<float>> slopes;
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std::vector<std::vector<std::vector<float>>> deltas = {};
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std::vector<std::vector<std::vector<float>>> lastDeltas = {};
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};
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}
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} |