Artificial Neural Networks - Multi-Layer Perceptrons. More...

Inheritance diagram for OpenCVForUnity.MlModule.ANN_MLP:

Public Member Functions
void	setTrainMethod (int method, double param1, double param2)

void	setTrainMethod (int method, double param1)

void	setTrainMethod (int method)

int	getTrainMethod ()

void	setActivationFunction (int type, double param1, double param2)

void	setActivationFunction (int type, double param1)

void	setActivationFunction (int type)

void	setLayerSizes (Mat _layer_sizes)

Mat	getLayerSizes ()

TermCriteria	getTermCriteria ()

void	setTermCriteria (TermCriteria val)

double	getBackpropWeightScale ()

void	setBackpropWeightScale (double val)

double	getBackpropMomentumScale ()

void	setBackpropMomentumScale (double val)

double	getRpropDW0 ()

void	setRpropDW0 (double val)

double	getRpropDWPlus ()

void	setRpropDWPlus (double val)

double	getRpropDWMinus ()

void	setRpropDWMinus (double val)

double	getRpropDWMin ()

void	setRpropDWMin (double val)

double	getRpropDWMax ()

void	setRpropDWMax (double val)

double	getAnnealInitialT ()

void	setAnnealInitialT (double val)

double	getAnnealFinalT ()

void	setAnnealFinalT (double val)

double	getAnnealCoolingRatio ()

void	setAnnealCoolingRatio (double val)

int	getAnnealItePerStep ()

void	setAnnealItePerStep (int val)

Mat	getWeights (int layerIdx)

Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
int	getVarCount ()
	Returns the number of variables in training samples. More...

override bool	empty ()
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...

bool	isTrained ()
	Returns true if the model is trained. More...

bool	isClassifier ()
	Returns true if the model is classifier. More...

bool	train (TrainData trainData, int flags)
	Trains the statistical model. More...

bool	train (TrainData trainData)
	Trains the statistical model. More...

bool	train (Mat samples, int layout, Mat responses)
	Trains the statistical model. More...

float	calcError (TrainData data, bool test, Mat resp)
	Computes error on the training or test dataset. More...

virtual float	predict (Mat samples, Mat results, int flags)
	Predicts response(s) for the provided sample(s) More...

virtual float	predict (Mat samples, Mat results)
	Predicts response(s) for the provided sample(s) More...

virtual float	predict (Mat samples)
	Predicts response(s) for the provided sample(s) More...

Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
IntPtr	getNativeObjAddr ()

virtual void	clear ()
	Clears the algorithm state. More...

void	save (string filename)

virtual string	getDefaultName ()

Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()

void	ThrowIfDisposed ()

Static Public Member Functions
static new ANN_MLP	__fromPtr__ (IntPtr addr)

static ANN_MLP	create ()
	Creates empty model. More...

static ANN_MLP	load (string filepath)
	Loads and creates a serialized ANN from a file. More...

Static Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel
static new StatModel	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Public Attributes
const int	IDENTITY = 0

const int	SIGMOID_SYM = 1

const int	GAUSSIAN = 2

const int	RELU = 3

const int	LEAKYRELU = 4

const int	UPDATE_WEIGHTS = 1

const int	NO_INPUT_SCALE = 2

const int	NO_OUTPUT_SCALE = 4

const int	BACKPROP = 0

const int	RPROP = 1

const int	ANNEAL = 2

Public Attributes inherited from OpenCVForUnity.MlModule.StatModel
const int	UPDATE_MODEL = 1

const int	RAW_OUTPUT = 1

const int	COMPRESSED_INPUT = 2

const int	PREPROCESSED_INPUT = 4

Protected Member Functions
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.MlModule.StatModel
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()

	DisposableOpenCVObject (IntPtr ptr)

	DisposableOpenCVObject (bool isEnabledDispose)

	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)

Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()

	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed `[get, protected set]`

bool	IsEnabledDispose `[get, set]`

Detailed Description

Artificial Neural Networks - Multi-Layer Perceptrons.

Unlike many other models in ML that are constructed and trained at once, in the MLP model these steps are separated. First, a network with the specified topology is created using the non-default constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is trained using a set of input and output vectors. The training procedure can be repeated more than once, that is, the weights can be adjusted based on the new training data.

Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.

See also: ml_intro_ann

Member Function Documentation

◆ fromPtr()

static new ANN_MLP OpenCVForUnity.MlModule.ANN_MLP.__fromPtr__ ( IntPtr addr )

static

◆ create()

static ANN_MLP OpenCVForUnity.MlModule.ANN_MLP.create ( )

static

Creates empty model.

Use StatModel::train to train the model, Algorithm::load<ANN_MLP>(filename) to load the pre-trained model. Note that the train method has optional flags: ANN_MLP::TrainFlags.

◆ Dispose()

override void OpenCVForUnity.MlModule.ANN_MLP.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.CoreModule.Algorithm.

◆ getAnnealCoolingRatio()

double OpenCVForUnity.MlModule.ANN_MLP.getAnnealCoolingRatio ( )

See also: setAnnealCoolingRatio

◆ getAnnealFinalT()

double OpenCVForUnity.MlModule.ANN_MLP.getAnnealFinalT ( )

See also: setAnnealFinalT

◆ getAnnealInitialT()

double OpenCVForUnity.MlModule.ANN_MLP.getAnnealInitialT ( )

See also: setAnnealInitialT

◆ getAnnealItePerStep()

int OpenCVForUnity.MlModule.ANN_MLP.getAnnealItePerStep ( )

See also: setAnnealItePerStep

◆ getBackpropMomentumScale()

double OpenCVForUnity.MlModule.ANN_MLP.getBackpropMomentumScale ( )

See also: setBackpropMomentumScale

◆ getBackpropWeightScale()

double OpenCVForUnity.MlModule.ANN_MLP.getBackpropWeightScale ( )

See also: setBackpropWeightScale

◆ getLayerSizes()

Mat OpenCVForUnity.MlModule.ANN_MLP.getLayerSizes ( )

Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer.

See also: setLayerSizes

◆ getRpropDW0()

double OpenCVForUnity.MlModule.ANN_MLP.getRpropDW0 ( )

See also: setRpropDW0

◆ getRpropDWMax()

double OpenCVForUnity.MlModule.ANN_MLP.getRpropDWMax ( )

See also: setRpropDWMax

◆ getRpropDWMin()

double OpenCVForUnity.MlModule.ANN_MLP.getRpropDWMin ( )

See also: setRpropDWMin

◆ getRpropDWMinus()

double OpenCVForUnity.MlModule.ANN_MLP.getRpropDWMinus ( )

See also: setRpropDWMinus

◆ getRpropDWPlus()

double OpenCVForUnity.MlModule.ANN_MLP.getRpropDWPlus ( )

See also: setRpropDWPlus

◆ getTermCriteria()

TermCriteria OpenCVForUnity.MlModule.ANN_MLP.getTermCriteria ( )

See also: setTermCriteria

◆ getTrainMethod()

int OpenCVForUnity.MlModule.ANN_MLP.getTrainMethod ( )

Returns current training method

◆ getWeights()

Mat OpenCVForUnity.MlModule.ANN_MLP.getWeights ( int layerIdx )

◆ load()

static ANN_MLP OpenCVForUnity.MlModule.ANN_MLP.load ( string filepath )

static

Loads and creates a serialized ANN from a file.

Use ANN::save to serialize and store an ANN to disk. Load the ANN from this file again, by calling this function with the path to the file.

Parameters

filepath path to serialized ANN

◆ setActivationFunction() [1/3]

void OpenCVForUnity.MlModule.ANN_MLP.setActivationFunction	(	int	type,
		double	param1,
		double	param2
	)

Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.

Parameters

type	The type of activation function. See ANN_MLP::ActivationFunctions.
param1	The first parameter of the activation function, \(\alpha\). Default value is 0.
param2	The second parameter of the activation function, \(\beta\). Default value is 0.

◆ setActivationFunction() [2/3]

void OpenCVForUnity.MlModule.ANN_MLP.setActivationFunction	(	int	type,
		double	param1
	)

Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.

Parameters

type	The type of activation function. See ANN_MLP::ActivationFunctions.
param1	The first parameter of the activation function, \(\alpha\). Default value is 0.
param2	The second parameter of the activation function, \(\beta\). Default value is 0.

◆ setActivationFunction() [3/3]

void OpenCVForUnity.MlModule.ANN_MLP.setActivationFunction ( int type )

Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.

Parameters

type	The type of activation function. See ANN_MLP::ActivationFunctions.
param1	The first parameter of the activation function, \(\alpha\). Default value is 0.
param2	The second parameter of the activation function, \(\beta\). Default value is 0.

◆ setAnnealCoolingRatio()

void OpenCVForUnity.MlModule.ANN_MLP.setAnnealCoolingRatio ( double val )

See also: getAnnealCoolingRatio

◆ setAnnealFinalT()

void OpenCVForUnity.MlModule.ANN_MLP.setAnnealFinalT ( double val )

See also: getAnnealFinalT

◆ setAnnealInitialT()

void OpenCVForUnity.MlModule.ANN_MLP.setAnnealInitialT ( double val )

See also: getAnnealInitialT

◆ setAnnealItePerStep()

void OpenCVForUnity.MlModule.ANN_MLP.setAnnealItePerStep ( int val )

See also: getAnnealItePerStep

◆ setBackpropMomentumScale()

void OpenCVForUnity.MlModule.ANN_MLP.setBackpropMomentumScale ( double val )

See also: getBackpropMomentumScale

◆ setBackpropWeightScale()

void OpenCVForUnity.MlModule.ANN_MLP.setBackpropWeightScale ( double val )

See also: getBackpropWeightScale

◆ setLayerSizes()

void OpenCVForUnity.MlModule.ANN_MLP.setLayerSizes ( Mat _layer_sizes )

Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer. Default value is empty Mat.

See also: getLayerSizes

◆ setRpropDW0()

void OpenCVForUnity.MlModule.ANN_MLP.setRpropDW0 ( double val )

See also: getRpropDW0

◆ setRpropDWMax()

void OpenCVForUnity.MlModule.ANN_MLP.setRpropDWMax ( double val )

See also: getRpropDWMax

◆ setRpropDWMin()

void OpenCVForUnity.MlModule.ANN_MLP.setRpropDWMin ( double val )

See also: getRpropDWMin

◆ setRpropDWMinus()

void OpenCVForUnity.MlModule.ANN_MLP.setRpropDWMinus ( double val )

See also: getRpropDWMinus

◆ setRpropDWPlus()

void OpenCVForUnity.MlModule.ANN_MLP.setRpropDWPlus ( double val )

See also: getRpropDWPlus

◆ setTermCriteria()

void OpenCVForUnity.MlModule.ANN_MLP.setTermCriteria ( TermCriteria val )

See also: getTermCriteria

◆ setTrainMethod() [1/3]

void OpenCVForUnity.MlModule.ANN_MLP.setTrainMethod	(	int	method,
		double	param1,
		double	param2
	)

Sets training method and common parameters.

Parameters

method	Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
param1	passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
param2	passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.

◆ setTrainMethod() [2/3]

void OpenCVForUnity.MlModule.ANN_MLP.setTrainMethod	(	int	method,
		double	param1
	)

Sets training method and common parameters.

Parameters

method	Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
param1	passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
param2	passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.

◆ setTrainMethod() [3/3]

void OpenCVForUnity.MlModule.ANN_MLP.setTrainMethod ( int method )

Sets training method and common parameters.

Parameters

method	Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
param1	passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
param2	passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.

Member Data Documentation

◆ ANNEAL

const int OpenCVForUnity.MlModule.ANN_MLP.ANNEAL = 2

◆ BACKPROP

const int OpenCVForUnity.MlModule.ANN_MLP.BACKPROP = 0

◆ GAUSSIAN

const int OpenCVForUnity.MlModule.ANN_MLP.GAUSSIAN = 2

◆ IDENTITY

const int OpenCVForUnity.MlModule.ANN_MLP.IDENTITY = 0

◆ LEAKYRELU

const int OpenCVForUnity.MlModule.ANN_MLP.LEAKYRELU = 4

◆ NO_INPUT_SCALE

const int OpenCVForUnity.MlModule.ANN_MLP.NO_INPUT_SCALE = 2

◆ NO_OUTPUT_SCALE

const int OpenCVForUnity.MlModule.ANN_MLP.NO_OUTPUT_SCALE = 4

◆ RELU

const int OpenCVForUnity.MlModule.ANN_MLP.RELU = 3

◆ RPROP

const int OpenCVForUnity.MlModule.ANN_MLP.RPROP = 1

◆ SIGMOID_SYM

const int OpenCVForUnity.MlModule.ANN_MLP.SIGMOID_SYM = 1

◆ UPDATE_WEIGHTS

const int OpenCVForUnity.MlModule.ANN_MLP.UPDATE_WEIGHTS = 1

The documentation for this class was generated from the following file:

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/ANN_MLP.cs

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ __fromPtr__()

◆ create()

◆ Dispose()

◆ getAnnealCoolingRatio()

◆ getAnnealFinalT()

◆ getAnnealInitialT()

◆ getAnnealItePerStep()

◆ getBackpropMomentumScale()

◆ getBackpropWeightScale()

◆ getLayerSizes()

◆ getRpropDW0()

◆ getRpropDWMax()

◆ getRpropDWMin()

◆ getRpropDWMinus()

◆ getRpropDWPlus()

◆ getTermCriteria()

◆ getTrainMethod()

◆ getWeights()

◆ load()

◆ setActivationFunction() [1/3]

◆ setActivationFunction() [2/3]

◆ setActivationFunction() [3/3]

◆ setAnnealCoolingRatio()

◆ setAnnealFinalT()

◆ setAnnealInitialT()

◆ setAnnealItePerStep()

◆ setBackpropMomentumScale()

◆ setBackpropWeightScale()

◆ setLayerSizes()

◆ setRpropDW0()

◆ setRpropDWMax()

◆ setRpropDWMin()

◆ setRpropDWMinus()

◆ setRpropDWPlus()

◆ setTermCriteria()

◆ setTrainMethod() [1/3]

◆ setTrainMethod() [2/3]

◆ setTrainMethod() [3/3]

Member Data Documentation

◆ ANNEAL

◆ BACKPROP

◆ GAUSSIAN

◆ IDENTITY

◆ LEAKYRELU

◆ NO_INPUT_SCALE

◆ NO_OUTPUT_SCALE

◆ RELU

◆ RPROP

◆ SIGMOID_SYM

◆ UPDATE_WEIGHTS

◆ fromPtr()