C# Класс 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 // ... }

Наследование: ISupervisedLearning, IDisposable

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Открытые методы

Метод	Описание
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.

Защищенные методы

Метод	Описание
Dispose ( bool disposing ) : void	Releases unmanaged and - optionally - managed resources

Приватные методы

Метод	Описание
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.

Описание методов

ComputeError() публичный Метод

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.
Результат	double

Dispose() публичный Метод

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

public Dispose ( ) : void
Результат	void

Dispose() защищенный Метод

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.
Результат	void

ParallelResilientBackpropagationLearning() публичный Метод

Initializes a new instance of the ParallelResilientBackpropagationLearning class.

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

Reset() публичный Метод

Resets the current update steps using the given learning rate.

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

Run() публичный Метод

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.
Результат	double

RunEpoch() публичный Метод

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.
Результат	double