72 lines
1.9 KiB
C++
72 lines
1.9 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 "BackPropagation.h"
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namespace NeuralNetwork {
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namespace Learning {
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/** @class QuickPropagation
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* @brief
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*/
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class QuickPropagation : public BackPropagation {
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public:
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inline QuickPropagation(FeedForward::Network &feedForwardNetwork, CorrectionFunction::CorrectionFunction *correction = new CorrectionFunction::Linear()):
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BackPropagation(feedForwardNetwork,correction),previousSlopes() {
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resize();
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}
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virtual ~QuickPropagation() {
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}
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protected:
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float _maxChange=1.75;
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float _epsilon=0.5;
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virtual inline void resize() override {
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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(previousSlopes.size()!=network.size())
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previousSlopes.resize(network.size());
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for(std::size_t i=0; i < network.size(); i++) {
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if(previousSlopes[i].size()!=network[i].size())
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previousSlopes[i].resize(network[i].size());
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for(std::size_t j=0; j < previousSlopes[i].size(); j++) {
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previousSlopes[i][j]=1.0;
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}
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}
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if(lastWeightChange.size()!=network.size())
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lastWeightChange.resize(network.size());
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for(std::size_t i=0; i < network.size(); i++) {
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if(lastWeightChange[i].size()!=network[i].size())
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lastWeightChange[i].resize(network[i].size());
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for(std::size_t j=0; j < previousSlopes[i].size(); j++) {
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lastWeightChange[i][j]=1.0;
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}
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}
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weightChange= lastWeightChange;
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}
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virtual void updateWeights(const std::vector<float> &input) override;
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std::vector<std::vector<float>> previousSlopes ={};
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std::vector<std::vector<float>> lastWeightChange ={};
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std::vector<std::vector<float>> weightChange ={};
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};
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}
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} |