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

#include "ml.hpp"

Inheritance diagram for cv::ml::ANN_MLP:

Public Types
enum	ActivationFunctions { IDENTITY = 0, SIGMOID_SYM = 1, GAUSSIAN = 2 }

enum	TrainFlags { UPDATE_WEIGHTS = 1, NO_INPUT_SCALE = 2, NO_OUTPUT_SCALE = 4 }

enum	TrainingMethods { BACKPROP =0, RPROP =1 }

Public Types inherited from cv::ml::StatModel
enum	Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 }

Public Member Functions
virtual double	getBackpropMomentumScale () const =0

virtual double	getBackpropWeightScale () const =0

virtual cv::Mat	getLayerSizes () const =0

virtual double	getRpropDW0 () const =0

virtual double	getRpropDWMax () const =0

virtual double	getRpropDWMin () const =0

virtual double	getRpropDWMinus () const =0

virtual double	getRpropDWPlus () const =0

virtual TermCriteria	getTermCriteria () const =0

virtual int	getTrainMethod () const =0

virtual Mat	getWeights (int layerIdx) const =0

virtual void	setActivationFunction (int type, double param1=0, double param2=0)=0

virtual void	setBackpropMomentumScale (double val)=0

virtual void	setBackpropWeightScale (double val)=0

virtual void	setLayerSizes (InputArray _layer_sizes)=0

virtual void	setRpropDW0 (double val)=0

virtual void	setRpropDWMax (double val)=0

virtual void	setRpropDWMin (double val)=0

virtual void	setRpropDWMinus (double val)=0

virtual void	setRpropDWPlus (double val)=0

virtual void	setTermCriteria (TermCriteria val)=0

virtual void	setTrainMethod (int method, double param1=0, double param2=0)=0

Public Member Functions inherited from cv::ml::StatModel
virtual float	calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
	Computes error on the training or test dataset. More...

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

virtual int	getVarCount () const =0
	Returns the number of variables in training samples. More...

virtual bool	isClassifier () const =0
	Returns true if the model is classifier. More...

virtual bool	isTrained () const =0
	Returns true if the model is trained. More...

virtual float	predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
	Predicts response(s) for the provided sample(s) More...

virtual bool	train (const Ptr< TrainData > &trainData, int flags=0)
	Trains the statistical model. More...

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

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

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

virtual String	getDefaultName () const

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...

Static Public Member Functions
static Ptr< ANN_MLP >	create ()
	Creates empty model. More...

Static Public Member Functions inherited from cv::ml::StatModel
template<typename _Tp >
static Ptr< _Tp >	train (const Ptr< TrainData > &data, int flags=0)
	Create and train model with default parameters. More...

Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file. More...

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

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: Neural Networks

Member Enumeration Documentation

enum cv::ml::ANN_MLP::ActivationFunctions

possible activation functions

Enumerator

IDENTITY

Identity function: \(f(x)=x\)

SIGMOID_SYM

Symmetrical sigmoid: \(f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\)

Note: If you are using the default sigmoid activation function with the default parameter values fparam1=0 and fparam2=0 then the function used is y = 1.7159*tanh(2/3 * x), so the output will range from [-1.7159, 1.7159], instead of [0,1].

GAUSSIAN

Gaussian function: \(f(x)=\beta e^{-\alpha x*x}\)

enum cv::ml::ANN_MLP::TrainFlags

Train options

Enumerator
UPDATE_WEIGHTS	Update the network weights, rather than compute them from scratch. In the latter case the weights are initialized using the Nguyen-Widrow algorithm.
NO_INPUT_SCALE	Do not normalize the input vectors. If this flag is not set, the training algorithm normalizes each input feature independently, shifting its mean value to 0 and making the standard deviation equal to 1. If the network is assumed to be updated frequently, the new training data could be much different from original one. In this case, you should take care of proper normalization.
NO_OUTPUT_SCALE	Do not normalize the output vectors. If the flag is not set, the training algorithm normalizes each output feature independently, by transforming it to the certain range depending on the used activation function.

enum cv::ml::ANN_MLP::TrainingMethods

Available training methods

Enumerator
BACKPROP	The back-propagation algorithm.
RPROP	The RPROP algorithm. See [101] for details.

Member Function Documentation

static Ptr<ANN_MLP> cv::ml::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.

virtual double cv::ml::ANN_MLP::getBackpropMomentumScale ( ) const

pure virtual

BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. Default value is 0.1.

See also: setBackpropMomentumScale

virtual double cv::ml::ANN_MLP::getBackpropWeightScale ( ) const

pure virtual

BPROP: Strength of the weight gradient term. The recommended value is about 0.1. Default value is 0.1.

See also: setBackpropWeightScale

virtual cv::Mat cv::ml::ANN_MLP::getLayerSizes ( ) const

pure virtual

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

virtual double cv::ml::ANN_MLP::getRpropDW0 ( ) const

pure virtual

RPROP: Initial value \(\Delta_0\) of update-values \(\Delta_{ij}\). Default value is 0.1.

See also: setRpropDW0

virtual double cv::ml::ANN_MLP::getRpropDWMax ( ) const

pure virtual

RPROP: Update-values upper limit \(\Delta_{max}\). It must be >1. Default value is 50.

See also: setRpropDWMax

virtual double cv::ml::ANN_MLP::getRpropDWMin ( ) const

pure virtual

RPROP: Update-values lower limit \(\Delta_{min}\). It must be positive. Default value is FLT_EPSILON.

See also: setRpropDWMin

virtual double cv::ml::ANN_MLP::getRpropDWMinus ( ) const

pure virtual

RPROP: Decrease factor \(\eta^-\). It must be <1. Default value is 0.5.

See also: setRpropDWMinus

virtual double cv::ml::ANN_MLP::getRpropDWPlus ( ) const

pure virtual

RPROP: Increase factor \(\eta^+\). It must be >1. Default value is 1.2.

See also: setRpropDWPlus

virtual TermCriteria cv::ml::ANN_MLP::getTermCriteria ( ) const

pure virtual

Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon). Default value is TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).

See also: setTermCriteria

virtual int cv::ml::ANN_MLP::getTrainMethod ( ) const

pure virtual

Returns current training method

virtual Mat cv::ml::ANN_MLP::getWeights ( int layerIdx ) const

pure virtual

virtual void cv::ml::ANN_MLP::setActivationFunction	(	int	type,
		double	param1 = `0`,
		double	param2 = `0`
	)

pure virtual

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.

virtual void cv::ml::ANN_MLP::setBackpropMomentumScale ( double val )

pure virtual

See also: getBackpropMomentumScale

virtual void cv::ml::ANN_MLP::setBackpropWeightScale ( double val )

pure virtual

See also: getBackpropWeightScale

virtual void cv::ml::ANN_MLP::setLayerSizes ( InputArray _layer_sizes )

pure virtual

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

virtual void cv::ml::ANN_MLP::setRpropDW0 ( double val )

pure virtual

See also: getRpropDW0

virtual void cv::ml::ANN_MLP::setRpropDWMax ( double val )

pure virtual

See also: getRpropDWMax

virtual void cv::ml::ANN_MLP::setRpropDWMin ( double val )

pure virtual

See also: getRpropDWMin

virtual void cv::ml::ANN_MLP::setRpropDWMinus ( double val )

pure virtual

See also: getRpropDWMinus

virtual void cv::ml::ANN_MLP::setRpropDWPlus ( double val )

pure virtual

See also: getRpropDWPlus

virtual void cv::ml::ANN_MLP::setTermCriteria ( TermCriteria val )

pure virtual

See also: getTermCriteria

virtual void cv::ml::ANN_MLP::setTrainMethod	(	int	method,
		double	param1 = `0`,
		double	param2 = `0`
	)

pure virtual

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
param2	passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP.

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

ml/include/opencv2/ml.hpp

Public Types

Public Member Functions

Static Public Member Functions

Detailed Description

Member Enumeration Documentation

Member Function Documentation