• 홈
• Encog
• Engine
• Network
• Train
• Prop
• TrainFlatNetworkProp

C# 클래스 Encog.Engine.Network.Train.Prop.TrainFlatNetworkProp

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

보호된 프로퍼티들

프로퍼티	타입	설명
currentError	double
gradients	double[]
indexable	IEngineIndexableSet
iteration	int
lastGradient	double[]
network	Encog.Engine.Network.Flat.FlatNetwork
numThreads	int
reportedException	System.Exception
totalError	double
training	IEngineDataSet
workers	IFlatGradientWorker[]

공개 메소드들

메소드	설명
CalculateGradients ( ) : void	Calculatee the gradients.
FinishTraining ( ) : void
Iteration ( ) : void
Iteration ( int count ) : void	Perform the specified number of training iterations. This is a basic implementation that just calls iteration the specified number of times. However, some training methods, particularly with the GPU, benefit greatly by calling with higher numbers than 1.
Report ( double gradients, double error, Exception ex ) : void	Called by the worker threads to report the progress at each step.
TrainFlatNetworkProp ( FlatNetwork network, IEngineDataSet training ) : Encog.Engine	Train a flat network multithreaded.
UpdateWeight ( double gradient, double lastGradient, int index ) : double	Update a weight, the means by which weights are updated vary depending on the training.

보호된 메소드들

메소드	설명
Learn ( ) : void	Apply and learn.
LearnLimited ( ) : void	Apply and learn. This is the same as learn, but it checks to see if any of the weights are below the limit threshold. In this case, these weights are zeroed out. Having two methods allows the regular learn method, which is what is usually use, to be as fast as possible.

비공개 메소드들

메소드	설명
CopyContexts ( ) : void	Copy the contexts to keep them consistent with multithreaded training.
Init ( ) : void

메소드 상세

프로퍼티 상세