C# Class Encog.Neural.Flat.Train.Prop.TrainFlatNetworkProp

Inheritance: ITrainFlatNetwork

显示文件 Open project: encog/encog-silverlight-core Class Usage Examples

Protected Properties

Property	Type	Description
CurrentError	double
Gradients	double[]

Public Methods

Method	Description
CalculateGradients ( ) : void	Calculate the gradients.
FinishTraining ( ) : void
InitOthers ( ) : void	Allow other training methods to init.
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.
UpdateWeight ( double gradients, double lastGradient, int index ) : double	Update a weight, the means by which weights are updated vary depending on the training.

Protected Methods

Method	Description
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.
TrainFlatNetworkProp ( FlatNetwork network, IMLDataSet training ) : System	Train a flat network multithreaded.

Private Methods

Method	Description
CopyContexts ( ) : void	Copy the contexts to keep them consistent with multithreaded training.
Init ( ) : void	Init the process.

Method Details

CalculateGradients() public method

Calculate the gradients.

public CalculateGradients ( ) : void
return	void

FinishTraining() public method

public FinishTraining ( ) : void
return	void

InitOthers() public abstract method

Allow other training methods to init.

public abstract InitOthers ( ) : void
return	void

Iteration() public method

public Iteration ( ) : void
return	void

Iteration() public method

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.

public Iteration ( int count ) : void
count	int	The number of training iterations.
return	void

Learn() protected method

Apply and learn.

protected Learn ( ) : void
return	void

LearnLimited() protected method

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.

protected LearnLimited ( ) : void
return	void

Report() public method

Called by the worker threads to report the progress at each step.

public Report ( double gradients, double error, Exception ex ) : void
gradients	double	The gradients from that worker.
error	double	The error for that worker.
ex	System.Exception	The exception.
return	void

TrainFlatNetworkProp() protected method

Train a flat network multithreaded.

protected TrainFlatNetworkProp ( FlatNetwork network, IMLDataSet training ) : System
network	Encog.Neural.Flat.FlatNetwork	The network to train.
training	IMLDataSet	The training data to use.
return	System

UpdateWeight() public abstract method

Update a weight, the means by which weights are updated vary depending on the training.

public abstract UpdateWeight ( double gradients, double lastGradient, int index ) : double
gradients	double	The gradients.
lastGradient	double	The last gradients.
index	int	The index.
return	double

Property Details

CurrentError protected_oe property

The current error is the average error over all of the threads.

protected double CurrentError
return	double

Gradients protected_oe property

The gradients.

protected double[] Gradients
return	double[]