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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
public class ResilientBackpropagationLearning : ISupervisedLearning
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The ResilientBackpropagationLearning type exposes the following members.

Constructors
  NameDescription
Public methodResilientBackpropagationLearning
Initializes a new instance of the ResilientBackpropagationLearning class.
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Properties
  NameDescription
Public propertyLearningRate
Learning rate.
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Methods
  NameDescription
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodRun
Runs learning iteration.
Public methodRunEpoch
Runs learning epoch.
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(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.)
Public Extension MethodToTOverloaded.
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):

// 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
    // ...
}
See Also