C# Class 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 // ... }

Inheritance: ISupervisedLearning, IDisposable

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Public Methods

Method	Description
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.

Protected Methods

Method	Description
Dispose ( bool disposing ) : void	Releases unmanaged and - optionally - managed resources

Private Methods

Method	Description
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.

Method Details

ComputeError() public method

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.
return	double

Dispose() public method

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

public Dispose ( ) : void
return	void

Dispose() protected method

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.
return	void

ParallelResilientBackpropagationLearning() public method

Initializes a new instance of the ParallelResilientBackpropagationLearning class.

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

Reset() public method

Resets the current update steps using the given learning rate.

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

Run() public method

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.
return	double

RunEpoch() public method

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.
return	double