C# 클래스 Encog.Neural.Flat.Train.Prop.TrainFlatNetworkResilient

상속: TrainFlatNetworkProp

파일 보기 프로젝트 열기: encog/encog-silverlight-core 1 사용 예제들

공개 메소드들

메소드	설명
InitOthers ( ) : void	Not needed for this training type.
TrainFlatNetworkResilient ( FlatNetwork flat, IMLDataSet trainingSet ) : System	Tran a network using RPROP.
TrainFlatNetworkResilient ( FlatNetwork network, IMLDataSet training, double zeroTolerance, double initialUpdate, double maxStep ) : System	Construct a resilient trainer for flat networks.
UpdateWeight ( double gradients, double lastGradient, int index ) : double	Calculate the amount to change the weight by.
UpdateWeightMinus ( double gradients, double lastGradient, int index ) : double
UpdateWeightPlus ( double gradients, double lastGradient, int index ) : double
UpdateiWeightMinus ( double gradients, double lastGradient, int index ) : double
UpdateiWeightPlus ( double gradients, double lastGradient, int index ) : double

비공개 메소드들

메소드	설명
Sign ( double v ) : int	Determine the sign of the value.

메소드 상세

InitOthers() 공개 메소드

Not needed for this training type.

public InitOthers ( ) : void
리턴	void

TrainFlatNetworkResilient() 공개 메소드

Tran a network using RPROP.

public TrainFlatNetworkResilient ( FlatNetwork flat, IMLDataSet trainingSet ) : System
flat	Encog.Neural.Flat.FlatNetwork	The network to train.
trainingSet	IMLDataSet	The training data to use.
리턴	System

TrainFlatNetworkResilient() 공개 메소드

Construct a resilient trainer for flat networks.

public TrainFlatNetworkResilient ( FlatNetwork network, IMLDataSet training, double zeroTolerance, double initialUpdate, double maxStep ) : System
network	Encog.Neural.Flat.FlatNetwork	The network to train.
training	IMLDataSet	The training data to use.
zeroTolerance	double	How close a number should be to zero to be counted as zero.
initialUpdate	double	The initial update value.
maxStep	double	The maximum step value.
리턴	System

UpdateWeight() 공개 메소드

Calculate the amount to change the weight by.

public UpdateWeight ( double gradients, double lastGradient, int index ) : double
gradients	double	The gradients.
lastGradient	double	The last gradients.
index	int	The index to update.
리턴	double

UpdateWeightMinus() 공개 메소드

public UpdateWeightMinus ( double gradients, double lastGradient, int index ) : double
gradients	double
lastGradient	double
index	int
리턴	double

UpdateWeightPlus() 공개 메소드

public UpdateWeightPlus ( double gradients, double lastGradient, int index ) : double
gradients	double
lastGradient	double
index	int
리턴	double

UpdateiWeightMinus() 공개 메소드

public UpdateiWeightMinus ( double gradients, double lastGradient, int index ) : double
gradients	double
lastGradient	double
index	int
리턴	double

UpdateiWeightPlus() 공개 메소드

public UpdateiWeightPlus ( double gradients, double lastGradient, int index ) : double
gradients	double
lastGradient	double
index	int
리턴	double