ANN_MLP Class

ANN_MLP Class

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Neural network

Inheritance Hierarchy

Namespace: Emgu.CV.ML
Assembly: Emgu.CV.ML (in Emgu.CV.ML.dll) Version: 3.0.0.2161 (3.0.0.2161)

Syntax

public class ANN_MLP : UnmanagedObject, IStatModel, 
	IAlgorithm

Public Class ANN_MLP
	Inherits UnmanagedObject
	Implements IStatModel, IAlgorithm

public ref class ANN_MLP : public UnmanagedObject, 
	IStatModel, IAlgorithm

type ANN_MLP =  
    class
        inherit UnmanagedObject
        interface IStatModel
        interface IAlgorithm
    end

The ANN_MLP type exposes the following members.

Constructors

	Name	Description
	ANN_MLP	Create a neural network using the specific parameters

Methods

	Name	Description
	Dispose	The dispose function that implements IDisposable interface (Inherited from DisposableObject.)
	DisposeObject	Release the memory associated with this neural network (Overrides DisposableObjectDisposeObject.)
	Equals	Determines whether the specified Object is equal to the current Object. (Inherited from Object.)
	Finalize	Destructor (Inherited from DisposableObject.)
	GetHashCode	Serves as a hash function for a particular type. (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.)
	ReleaseManagedResources	Release the managed resources. This function will be called during the disposal of the current object. override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object (Inherited from DisposableObject.)
	SetActivationFunction	Initialize the activation function for each neuron.
	SetLayerSizes	Sets the layer sizes.
	SetTrainMethod	Sets training method and common parameters.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Extension Methods

	Name	Description
	Clear	Clear the statistic model (Defined by StatModelExtensions.)
	Predict	Predicts response(s) for the provided sample(s) (Defined by StatModelExtensions.)
	Read	Reads algorithm parameters from a file storage. (Defined by AlgorithmExtensions.)
	Save	Save the statistic model to file (Defined by StatModelExtensions.)
	Train(TrainData, Int32)	Overloaded. Trains the statistical model. (Defined by StatModelExtensions.)
	Train(IInputArray, DataLayoutType, IInputArray)	Overloaded. Trains the statistical model. (Defined by StatModelExtensions.)
	Write	Stores algorithm parameters in a file storage (Defined by AlgorithmExtensions.)

Fields

	Name	Description
	_ptr	A pointer to the unmanaged object (Inherited from UnmanagedObject.)

Properties

	Name	Description
	BackpropMomentumScale	BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations)
	BackpropWeightScale	BPROP: Strength of the weight gradient term
	Ptr	Pointer to the unmanaged object (Inherited from UnmanagedObject.)
	RpropDW0	RPROP: Initial value Delta_0 of update-values Delta_{ij}
	RpropDWMax	RPROP: Update-values upper limit
	RpropDWMin	RPROP: Update-values lower limit
	RpropDWMinus	RPROP: Decrease factor
	RpropDWPlus	RPROP: Increase factor
	TermCriteria	Termination criteria of the training algorithm