C# Class AForge.Neuro.Learning.ResilientBackpropagationLearning

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

Show file Open project: holisticware-admin/MonoVersal.AForgeNET Class Usage Examples

Public Methods

Method	Description
ResilientBackpropagationLearning ( ActivationNetwork network ) : System	Initializes a new instance of the ResilientBackpropagationLearning class.
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.

Private Methods

Method	Description
CalculateError ( double desiredOutput ) : double	Calculates error values for all neurons of the network.
CalculateGradient ( double input ) : void	Calculate weights updates
ResetGradient ( ) : void	Resets current weight and threshold derivatives.
ResetUpdates ( double rate ) : void	Resets the current update steps using the given learning rate.
UpdateNetwork ( ) : void	Update network's weights.

Method Details

ResilientBackpropagationLearning() public method

Initializes a new instance of the ResilientBackpropagationLearning class.

public ResilientBackpropagationLearning ( ActivationNetwork network ) : System
network	ActivationNetwork	Network to teach.
return	System

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