shin/NeuralNetworkLib
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

learning: naming in bp changed and qp modified

Browse Source
This commit is contained in:
Shin
2016-05-07 20:41:52 +02:00
parent c03a13c0f8
commit 36ce3f6463
4 changed files with 82 additions and 49 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
include/NeuralNetwork/Learning/BackPropagation.h
View File

@@ -16,7 +16,7 @@ namespace Learning {
public:
inline BackPropagation(FeedForward::Network &feedForwardNetwork, CorrectionFunction::CorrectionFunction *correction = new CorrectionFunction::Linear()):
network(feedForwardNetwork), correctionFunction(correction),learningCoefficient(0.4), deltas() {
network(feedForwardNetwork), correctionFunction(correction),learningCoefficient(0.4), slopes() {
resize();
}
@@ -34,24 +34,26 @@ namespace Learning {
protected:
virtual inline void resize() {
if(deltas.size()!=network.size())
deltas.resize(network.size());
if(slopes.size()!=network.size())
slopes.resize(network.size());
for(std::size_t i=0; i < network.size(); i++) {
if(deltas[i].size()!=network[i].size())
deltas[i].resize(network[i].size());
if(slopes[i].size()!=network[i].size())
slopes[i].resize(network[i].size());
}
}
virtual void updateWeights(const std::vector<float> &input);
virtual void computeDeltas(const std::vector<float> &expectation);
FeedForward::Network &network;
CorrectionFunction::CorrectionFunction *correctionFunction;
float learningCoefficient;
std::vector<std::vector<float>> deltas;
std::vector<std::vector<float>> slopes;
};
}
}

41
include/NeuralNetwork/Learning/QuickPropagation.h
View File

@@ -16,7 +16,7 @@ namespace NeuralNetwork {
public:
inline QuickPropagation(FeedForward::Network &feedForwardNetwork, CorrectionFunction::CorrectionFunction *correction = new CorrectionFunction::Linear()):
BackPropagation(feedForwardNetwork,correction),deltasPrev() {
BackPropagation(feedForwardNetwork,correction),previousSlopes() {
resize();
}
@@ -24,32 +24,49 @@ namespace NeuralNetwork {
}
protected:
float _maxChange=1.75;
float _epsilon=0.5;
virtual inline void resize() override {
if(deltas.size()!=network.size())
deltas.resize(network.size());
if(slopes.size()!=network.size())
slopes.resize(network.size());
for(std::size_t i=0; i < network.size(); i++) {
if(deltas[i].size()!=network[i].size())
deltas[i].resize(network[i].size());
if(slopes[i].size()!=network[i].size())
slopes[i].resize(network[i].size());
}
if(deltasPrev.size()!=network.size())
deltasPrev.resize(network.size());
if(previousSlopes.size()!=network.size())
previousSlopes.resize(network.size());
for(std::size_t i=0; i < network.size(); i++) {
if(deltasPrev[i].size()!=network[i].size())
deltasPrev[i].resize(network[i].size());
if(previousSlopes[i].size()!=network[i].size())
previousSlopes[i].resize(network[i].size());
for(std::size_t j=0; j < deltasPrev[i].size(); j++) {
deltasPrev[i][j]=1.0;
for(std::size_t j=0; j < previousSlopes[i].size(); j++) {
previousSlopes[i][j]=1.0;
}
}
if(lastWeightChange.size()!=network.size())
lastWeightChange.resize(network.size());
for(std::size_t i=0; i < network.size(); i++) {
if(lastWeightChange[i].size()!=network[i].size())
lastWeightChange[i].resize(network[i].size());
for(std::size_t j=0; j < previousSlopes[i].size(); j++) {
lastWeightChange[i][j]=1.0;
}
}
weightChange= lastWeightChange;
}
virtual void updateWeights(const std::vector<float> &input) override;
std::vector<std::vector<float>> deltasPrev;
std::vector<std::vector<float>> previousSlopes ={};
std::vector<std::vector<float>> lastWeightChange ={};
std::vector<std::vector<float>> weightChange ={};
};
}
}