cv::ml::ANN_MLP Class Reference

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

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::ANN_MLP:

Public Types
enum	ActivationFunctions {   IDENTITY = 0,   SIGMOID_SYM = 1,   GAUSSIAN = 2,   RELU = 3,   LEAKYRELU = 4 }
enum	TrainFlags {   UPDATE_WEIGHTS = 1,   NO_INPUT_SCALE = 2,   NO_OUTPUT_SCALE = 4 }
enum	TrainingMethods {   BACKPROP =0,   RPROP = 1,   ANNEAL = 2 }
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	getAnnealCoolingRatio () const =0
virtual double	getAnnealFinalT () const =0
virtual double	getAnnealInitialT () const =0
virtual int	getAnnealItePerStep () const =0
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	setAnnealCoolingRatio (double val)=0
virtual void	setAnnealEnergyRNG (const RNG &rng)=0
	Set/initialize anneal RNG. More...
virtual void	setAnnealFinalT (double val)=0
virtual void	setAnnealInitialT (double val)=0
virtual void	setAnnealItePerStep (int val)=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 CV_OVERRIDE
	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...
void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...

Static Public Member Functions
static Ptr< ANN_MLP >	create ()
	Creates empty model. More...
static Ptr< ANN_MLP >	load (const String &filepath)
	Loads and creates a serialized ANN from a file. 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...

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

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

ActivationFunctions

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: \(f(x)=\beta e^{-\alpha x*x}\)
RELU	ReLU function: \(f(x)=max(0,x)\)
LEAKYRELU	Leaky ReLU function: for x>0 \(f(x)=x \) and x<=0 \(f(x)=\alpha x \)

TrainFlags

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.

TrainingMethods

enum cv::ml::ANN_MLP::TrainingMethods

Available training methods

Enumerator
BACKPROP	The back-propagation algorithm.
RPROP	The RPROP algorithm. See [181] for details.
ANNEAL	The simulated annealing algorithm. See [112] for details.

Member Function Documentation

create()

static Ptr<ANN_MLP> cv::ml::ANN_MLP::create ( )

static

Python:
	retval	=	cv.ml.ANN_MLP_create(		)

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.

getAnnealCoolingRatio()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getAnnealCoolingRatio(		)

ANNEAL: Update cooling ratio. It must be >0 and less than 1. Default value is 0.95.

See also

setAnnealCoolingRatio

getAnnealFinalT()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getAnnealFinalT(		)

ANNEAL: Update final temperature. It must be >=0 and less than initialT. Default value is 0.1.

See also

setAnnealFinalT

getAnnealInitialT()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getAnnealInitialT(		)

ANNEAL: Update initial temperature. It must be >=0. Default value is 10.

See also

setAnnealInitialT

getAnnealItePerStep()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getAnnealItePerStep(		)

ANNEAL: Update iteration per step. It must be >0 . Default value is 10.

See also

setAnnealItePerStep

getBackpropMomentumScale()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getBackpropMomentumScale(		)

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

getBackpropWeightScale()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getBackpropWeightScale(		)

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

See also

setBackpropWeightScale

getLayerSizes()

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

pure virtual

Python:
	retval	=	cv.ml_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()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getRpropDW0(		)

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

See also

setRpropDW0

getRpropDWMax()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getRpropDWMax(		)

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

See also

setRpropDWMax

getRpropDWMin()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getRpropDWMin(		)

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

See also

setRpropDWMin

getRpropDWMinus()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getRpropDWMinus(		)

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

See also

setRpropDWMinus

getRpropDWPlus()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getRpropDWPlus(		)

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

See also

setRpropDWPlus

getTermCriteria()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getTermCriteria(		)

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

getTrainMethod()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getTrainMethod(		)

Returns current training method

getWeights()

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

pure virtual

Python:
	retval	=	cv.ml_ANN_MLP.getWeights(	layerIdx	)

load()

static Ptr<ANN_MLP> cv::ml::ANN_MLP::load ( const String &  filepath )

static

Python:
	retval	=	cv.ml.ANN_MLP_load(	filepath	)

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()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setActivationFunction(	type[, param1[, 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.

setAnnealCoolingRatio()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setAnnealCoolingRatio(	val	)

See also

getAnnealCoolingRatio

setAnnealEnergyRNG()

virtual void cv::ml::ANN_MLP::setAnnealEnergyRNG ( const RNG &  rng )

pure virtual

Set/initialize anneal RNG.

setAnnealFinalT()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setAnnealFinalT(	val	)

See also

getAnnealFinalT

setAnnealInitialT()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setAnnealInitialT(	val	)

See also

getAnnealInitialT

setAnnealItePerStep()

virtual void cv::ml::ANN_MLP::setAnnealItePerStep ( int  val )

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setAnnealItePerStep(	val	)

See also

getAnnealItePerStep

setBackpropMomentumScale()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setBackpropMomentumScale(	val	)

See also

getBackpropMomentumScale

setBackpropWeightScale()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setBackpropWeightScale(	val	)

See also

getBackpropWeightScale

setLayerSizes()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setLayerSizes(	_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()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setRpropDW0(	val	)

See also

getRpropDW0

setRpropDWMax()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setRpropDWMax(	val	)

See also

getRpropDWMax

setRpropDWMin()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setRpropDWMin(	val	)

See also

getRpropDWMin

setRpropDWMinus()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setRpropDWMinus(	val	)

See also

getRpropDWMinus

setRpropDWPlus()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setRpropDWPlus(	val	)

See also

getRpropDWPlus

setTermCriteria()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setTermCriteria(	val	)

See also

getTermCriteria

setTrainMethod()

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

pure virtual

Python:
	None	=	cv.ml_ANN_MLP.setTrainMethod(	method[, param1[, 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.

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

• opencv2/ml.hpp