C# 클래스 Accord.Neuro.Learning.ParallelResilientBackpropagationLearning

Resilient Backpropagation learning algorithm.

This class implements the resilient backpropagation (RProp) learning algorithm. The RProp learning algorithm is one of the fastest learning algorithms for feed-forward learning networks which use only first-order information.

Sample usage (training network to calculate XOR function):

 // initialize input and output values double[][] input = new double[4][] { new double[] {0, 0}, new double[] {0, 1}, new double[] {1, 0}, new double[] {1, 1} }; double[][] output = new double[4][] { new double[] {0}, new double[] {1}, new double[] {1}, new double[] {0} }; // create neural network ActivationNetwork   network = new ActivationNetwork( SigmoidFunction( 2 ), 2, // two inputs in the network 2, // two neurons in the first layer 1 ); // one neuron in the second layer // create teacher ResilientBackpropagationLearning teacher = new ResilientBackpropagationLearning( network ); // loop while ( !needToStop ) { // run epoch of learning procedure double error = teacher.RunEpoch( input, output ); // check error value to see if we need to stop // ... }

상속: ISupervisedLearning, IDisposable

파일 보기 프로젝트 열기: accord-net/framework 1 사용 예제들

공개 메소드들

메소드	설명
ComputeError ( double input, double output ) : double	Compute network error for a given data set.
Dispose ( ) : void	Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.
ParallelResilientBackpropagationLearning ( ActivationNetwork network ) : System	Initializes a new instance of the ParallelResilientBackpropagationLearning class.
Reset ( double rate ) : void	Resets the current update steps using the given learning rate.
Run ( double input, double output ) : double	Runs learning iteration. Runs one learning iteration and updates neuron's weights.
RunEpoch ( double input, double output ) : double	Runs learning epoch. The method runs one learning epoch, by calling Run method for each vector provided in the input array.

보호된 메소드들

메소드	설명
Dispose ( bool disposing ) : void	Releases unmanaged and - optionally - managed resources

비공개 메소드들

메소드	설명
CalculateError ( double desiredOutput ) : double	Calculates error values for all neurons of the network.
CalculateGradient ( double input ) : void	Computes the gradient for a given input.
ResetGradient ( ) : void	Resets the gradient vector back to zero.
UpdateNetwork ( bool errIncrease = false ) : void	Update network weights.

메소드 상세

ComputeError() 공개 메소드

Compute network error for a given data set.

public ComputeError ( double input, double output ) : double
input	double	The input points.
output	double	The output points.
리턴	double

Dispose() 공개 메소드

Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.

public Dispose ( ) : void
리턴	void

Dispose() 보호된 메소드

Releases unmanaged and - optionally - managed resources

protected Dispose ( bool disposing ) : void
disposing	bool	true to release both managed /// and unmanaged resources; false to release only unmanaged /// resources.
리턴	void

ParallelResilientBackpropagationLearning() 공개 메소드

Initializes a new instance of the ParallelResilientBackpropagationLearning class.

public ParallelResilientBackpropagationLearning ( ActivationNetwork network ) : System
network	AForge.Neuro.ActivationNetwork	Network to teach.
리턴	System

Reset() 공개 메소드

Resets the current update steps using the given learning rate.

public Reset ( double rate ) : void
rate	double
리턴	void

Run() 공개 메소드

Runs learning iteration.

Runs one learning iteration and updates neuron's weights.

public Run ( double input, double output ) : double
input	double	Input vector.
output	double	Desired output vector.
리턴	double

RunEpoch() 공개 메소드

Runs learning epoch.

The method runs one learning epoch, by calling Run method for each vector provided in the input array.

public RunEpoch ( double input, double output ) : double
input	double	Array of input vectors.
output	double	Array of output vectors.
리턴	double