ResilientBackpropagationLearning Class

Resilient Backpropagation learning algorithm.

Inheritance Hierarchy

SystemObject
Accord.Neuro.LearningResilientBackpropagationLearning

Namespace: Accord.Neuro.Learning
Assembly: Accord.Neuro (in Accord.Neuro.dll) Version: 3.8.0

Syntax

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public class ResilientBackpropagationLearning : ISupervisedLearning

Public Class ResilientBackpropagationLearning
	Implements ISupervisedLearning

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The ResilientBackpropagationLearning type exposes the following members.

Constructors

	Name	Description
	ResilientBackpropagationLearning	Initializes a new instance of the ResilientBackpropagationLearning class.

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Properties

	Name	Description
	LearningRate	Learning rate.

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Methods

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Run	Runs learning iteration.
	RunEpoch	Runs learning epoch.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Remarks

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):

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// 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
    // ...
}

Reference

Accord.Neuro.Learning Namespace