#include <otbNeuralNetworkMachineLearningModel.h>

Inheritance diagram for otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >:

Collaboration diagram for otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >:

Public Types
typedef Superclass::ConfidenceValueType	ConfidenceValueType

typedef itk::SmartPointer< const Self >	ConstPointer

typedef Superclass::InputListSampleType	InputListSampleType

typedef Superclass::InputSampleType	InputSampleType

typedef Superclass::InputValueType	InputValueType

typedef std::map< TargetValueType, unsigned int >	MapOfLabelsType

typedef itk::SmartPointer< Self >	Pointer

typedef Superclass::ProbaSampleType	ProbaSampleType

typedef NeuralNetworkMachineLearningModel	Self

typedef MachineLearningModel< TInputValue, TTargetValue >	Superclass

typedef Superclass::TargetListSampleType	TargetListSampleType

typedef Superclass::TargetSampleType	TargetSampleType

typedef Superclass::TargetValueType	TargetValueType

Public Types inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
typedef MachineLearningModel	Self

typedef itk::Object	Superclass

typedef itk::SmartPointer< Self >	Pointer

typedef itk::SmartPointer< const Self >	ConstPointer

typedef MLMSampleTraits< TInputValue >::ValueType	InputValueType

typedef MLMSampleTraits< TInputValue >::SampleType	InputSampleType

typedef itk::Statistics::ListSample< InputSampleType >	InputListSampleType

typedef MLMTargetTraits< TTargetValue >::ValueType	TargetValueType

typedef MLMTargetTraits< TTargetValue >::SampleType	TargetSampleType

typedef itk::Statistics::ListSample< TargetSampleType >	TargetListSampleType

typedef MLMTargetTraits< double >::ValueType	ConfidenceValueType

typedef MLMTargetTraits< double >::SampleType	ConfidenceSampleType

typedef itk::Statistics::ListSample< ConfidenceSampleType >	ConfidenceListSampleType

typedef itk::VariableLengthVector< double >	ProbaSampleType

typedef itk::Statistics::ListSample< ProbaSampleType >	ProbaListSampleType

static Pointer	New ()

virtual ::itk::LightObject::Pointer	CreateAnother (void) const

virtual const char *	GetNameOfClass () const

virtual int	GetTrainMethod ()

virtual void	SetTrainMethod (int _arg)

void	SetLayerSizes (const std::vector< unsigned int > layers)

virtual int	GetActivateFunction ()

virtual void	SetActivateFunction (int _arg)

virtual double	GetAlpha ()

virtual void	SetAlpha (double _arg)

virtual double	GetBeta ()

virtual void	SetBeta (double _arg)

virtual double	GetBackPropDWScale ()

virtual void	SetBackPropDWScale (double _arg)

virtual double	GetBackPropMomentScale ()

virtual void	SetBackPropMomentScale (double _arg)

virtual double	GetRegPropDW0 ()

virtual void	SetRegPropDW0 (double _arg)

virtual double	GetRegPropDWMin ()

virtual void	SetRegPropDWMin (double _arg)

virtual int	GetTermCriteriaType ()

virtual void	SetTermCriteriaType (int _arg)

virtual int	GetMaxIter ()

virtual void	SetMaxIter (int _arg)

virtual double	GetEpsilon ()

virtual void	SetEpsilon (double _arg)

void	Train () override

void	Save (const std::string &filename, const std::string &name="") override

void	Load (const std::string &filename, const std::string &name="") override

Classification model file compatibility tests
cv::Ptr< cv::ml::ANN_MLP >	m_ANNModel

int	m_TrainMethod

int	m_ActivateFunction

std::vector< unsigned int >	m_LayerSizes

double	m_Alpha

double	m_Beta

double	m_BackPropDWScale

double	m_BackPropMomentScale

double	m_RegPropDW0

double	m_RegPropDWMin

int	m_TermCriteriaType

int	m_MaxIter

double	m_Epsilon

cv::Mat	m_MatrixOfLabels

MapOfLabelsType	m_MapOfLabels

bool	CanReadFile (const std::string &) override

bool	CanWriteFile (const std::string &) override

	NeuralNetworkMachineLearningModel ()

	~NeuralNetworkMachineLearningModel () override=default

TargetSampleType	DoPredict (const InputSampleType &input, ConfidenceValueType quality=nullptr, ProbaSampleType proba=nullptr) const override

void	LabelsToMat (const TargetListSampleType *listSample, cv::Mat &output)

void	PrintSelf (std::ostream &os, itk::Indent indent) const override

	NeuralNetworkMachineLearningModel (const Self &)=delete

void	operator= (const Self &)=delete

void	CreateNetwork ()

void	SetupNetworkAndTrain (cv::Mat &labels)

Additional Inherited Members
Public Member Functions inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
virtual const char *	GetNameOfClass () const

TargetSampleType	Predict (const InputSampleType &input, ConfidenceValueType quality=nullptr, ProbaSampleType proba=nullptr) const

virtual void	SetDimension (unsigned int _arg)

virtual unsigned int	GetDimension ()

TargetListSampleType::Pointer	PredictBatch (const InputListSampleType input, ConfidenceListSampleType quality=nullptr, ProbaListSampleType *proba=nullptr) const

bool	HasConfidenceIndex () const

bool	HasProbaIndex () const

virtual void	SetInputListSample (InputListSampleType *_arg)

	itkGetObjectMacro (InputListSample, InputListSampleType)

virtual const InputListSampleType *	GetInputListSample () const

	itkGetObjectMacro (TargetListSample, TargetListSampleType)

	itkGetObjectMacro (ConfidenceListSample, ConfidenceListSampleType)

virtual void	SetTargetListSample (TargetListSampleType *_arg)

virtual bool	GetRegressionMode ()

void	SetRegressionMode (bool flag)

Protected Member Functions inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
	MachineLearningModel ()

	~MachineLearningModel () override=default

void	PrintSelf (std::ostream &os, itk::Indent indent) const override

Protected Attributes inherited from otb::MachineLearningModel< TInputValue, TTargetValue >
InputListSampleType::Pointer	m_InputListSample

InputListSampleType::Pointer	m_ValidationListSample

TargetListSampleType::Pointer	m_TargetListSample

ConfidenceListSampleType::Pointer	m_ConfidenceListSample

bool	m_RegressionMode

bool	m_IsRegressionSupported

bool	m_ConfidenceIndex

bool	m_ProbaIndex

bool	m_IsDoPredictBatchMultiThreaded

unsigned int	m_Dimension

Detailed Description

template<class TInputValue, class TTargetValue>
class otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >

Definition at line 34 of file otbNeuralNetworkMachineLearningModel.h.

Member Typedef Documentation

◆ ConfidenceValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::ConfidenceValueType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::ConfidenceValueType

Definition at line 49 of file otbNeuralNetworkMachineLearningModel.h.

◆ ConstPointer

template<class TInputValue , class TTargetValue >

typedef itk::SmartPointer<const Self> otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::ConstPointer

Definition at line 41 of file otbNeuralNetworkMachineLearningModel.h.

◆ InputListSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputListSampleType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::InputListSampleType

Definition at line 45 of file otbNeuralNetworkMachineLearningModel.h.

◆ InputSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputSampleType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::InputSampleType

Definition at line 44 of file otbNeuralNetworkMachineLearningModel.h.

◆ InputValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::InputValueType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::InputValueType

Definition at line 43 of file otbNeuralNetworkMachineLearningModel.h.

◆ MapOfLabelsType

template<class TInputValue , class TTargetValue >

typedef std::map<TargetValueType, unsigned int> otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::MapOfLabelsType

Definition at line 51 of file otbNeuralNetworkMachineLearningModel.h.

◆ Pointer

template<class TInputValue , class TTargetValue >

typedef itk::SmartPointer<Self> otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::Pointer

Definition at line 40 of file otbNeuralNetworkMachineLearningModel.h.

◆ ProbaSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::ProbaSampleType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::ProbaSampleType

Definition at line 50 of file otbNeuralNetworkMachineLearningModel.h.

◆ Self

template<class TInputValue , class TTargetValue >

typedef NeuralNetworkMachineLearningModel otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::Self

Standard class typedefs.

Definition at line 38 of file otbNeuralNetworkMachineLearningModel.h.

◆ Superclass

template<class TInputValue , class TTargetValue >

typedef MachineLearningModel<TInputValue, TTargetValue> otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::Superclass

Definition at line 39 of file otbNeuralNetworkMachineLearningModel.h.

◆ TargetListSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetListSampleType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::TargetListSampleType

Definition at line 48 of file otbNeuralNetworkMachineLearningModel.h.

◆ TargetSampleType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetSampleType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::TargetSampleType

Definition at line 47 of file otbNeuralNetworkMachineLearningModel.h.

◆ TargetValueType

template<class TInputValue , class TTargetValue >

typedef Superclass::TargetValueType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::TargetValueType

Definition at line 46 of file otbNeuralNetworkMachineLearningModel.h.

Constructor & Destructor Documentation

◆ NeuralNetworkMachineLearningModel() [1/2]

template<class TInputValue , class TOutputValue >

otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::NeuralNetworkMachineLearningModel

protected

Constructor

Definition at line 32 of file otbNeuralNetworkMachineLearningModel.hxx.

References otb::MachineLearningModel< TInputValue, TTargetValue >::m_ConfidenceIndex, and otb::MachineLearningModel< TInputValue, TTargetValue >::m_IsRegressionSupported.

◆ ~NeuralNetworkMachineLearningModel()

template<class TInputValue , class TTargetValue >

otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::~NeuralNetworkMachineLearningModel ( )

overrideprotecteddefault

Destructor

◆ NeuralNetworkMachineLearningModel() [2/2]

template<class TInputValue , class TTargetValue >

otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::NeuralNetworkMachineLearningModel ( const Self & )

privatedelete

Is the input model file readable and compatible with the corresponding classifier ?

Member Function Documentation

◆ CanReadFile()

template<class TInputValue , class TOutputValue >

bool otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::CanReadFile ( const std::string & file )

overridevirtual

Is the input model file readable and compatible with the corresponding classifier ?

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 269 of file otbNeuralNetworkMachineLearningModel.hxx.

References CV_TYPE_NAME_ML_ANN_MLP.

◆ CanWriteFile()

template<class TInputValue , class TOutputValue >

bool otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::CanWriteFile ( const std::string & )

overridevirtual

Is the input model file writable and compatible with the corresponding classifier ?

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 295 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ CreateAnother()

template<class TInputValue , class TTargetValue >

virtual::itk::LightObject::Pointer otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::CreateAnother ( void ) const

Run-time type information (and related methods).

◆ CreateNetwork()

template<class TInputValue , class TOutputValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::CreateNetwork

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 125 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ DoPredict()

template<class TInputValue , class TOutputValue >

NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::TargetSampleType otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::DoPredict	(	const InputSampleType &	input,
		ConfidenceValueType *	quality = `nullptr`,
		ProbaSampleType *	proba = `nullptr`
	)		const

overrideprotected

Predict values using the model

Definition at line 186 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ GetActivateFunction()

template<class TInputValue , class TTargetValue >

virtual int otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetActivateFunction ( )

virtual

Setters/Getters to the neuron activation function 3 methods are available:

CvANN_MLP::IDENTITY
CvANN_MLP::SIGMOID_SYM
CvANN_MLP::GAUSSIAN Default is CvANN_MLP::SIGMOID_SYM
See also
http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetAlpha()

template<class TInputValue , class TTargetValue >

virtual double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetAlpha ( )

virtual

Setters/Getters to the alpha parameter of the activation function Default is 0.

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetBackPropDWScale()

template<class TInputValue , class TTargetValue >

virtual double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetBackPropDWScale ( )

virtual

Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1 Default is 0.1

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetBackPropMomentScale()

template<class TInputValue , class TTargetValue >

virtual double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetBackPropMomentScale ( )

virtual

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 is 0.1

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetBeta()

template<class TInputValue , class TTargetValue >

virtual double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetBeta ( )

virtual

Setters/Getters to the beta parameter of the activation function Default is 0.

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetEpsilon()

template<class TInputValue , class TTargetValue >

virtual double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetEpsilon ( )

virtual

Epsilon value used in the Termination criteria. default is 0.01

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetMaxIter()

template<class TInputValue , class TTargetValue >

virtual int otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetMaxIter ( )

virtual

Maximum number of iteration used in the Termination criteria. default is 1000

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetNameOfClass()

template<class TInputValue , class TTargetValue >

virtual const char* otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetNameOfClass ( ) const

virtual

Run-time type information (and related methods).

◆ GetRegPropDW0()

template<class TInputValue , class TTargetValue >

virtual double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetRegPropDW0 ( )

virtual

Initial value $\Delta_0$ of update-values $\Delta_{ij}$ in RPROP method. Default is 0.1

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetRegPropDWMin()

template<class TInputValue , class TTargetValue >

virtual double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetRegPropDWMin ( )

virtual

Update-values lower limit $\Delta_{min}$ in RPROP method. It must be positive. Default is FLT_EPSILON

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetTermCriteriaType()

template<class TInputValue , class TTargetValue >

virtual int otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetTermCriteriaType ( )

virtual

Termination criteria. It can be CV_TERMCRIT_ITER or CV_TERMCRIT_EPS or CV_TERMCRIT_ITER+CV_TERMCRIT_EPS default is CV_TERMCRIT_ITER+CV_TERMCRIT_EPS.

See also: http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ GetTrainMethod()

template<class TInputValue , class TTargetValue >

virtual int otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::GetTrainMethod ( )

virtual

Setters/Getters to the train method 2 methods are available:

CvANN_MLP_TrainParams::BACKPROP The back-propagation algorithm.
CvANN_MLP_TrainParams::RPROP The RPROP algorithm. Default is CvANN_MLP_TrainParams::RPROP.
See also
http://docs.opencv.org/modules/ml/doc/neural_networks.html

◆ LabelsToMat()

template<class TInputValue , class TOutputValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::LabelsToMat	(	const TargetListSampleType *	labels,
		cv::Mat &	output
	)

protected

Converts a ListSample of VariableLengthVector to a CvMat. The user is responsible for freeing the output pointer with the cvReleaseMat function. A null pointer is resturned in case the conversion failed.

Definition at line 68 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ Load()

template<class TInputValue , class TOutputValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::Load	(	const std::string &	filename,
		const std::string &	name = `""`
	)

overridevirtual

Load the model from file

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 259 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ New()

template<class TInputValue , class TTargetValue >

static Pointer otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::New ( )

static

Run-time type information (and related methods).

◆ operator=()

template<class TInputValue , class TTargetValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::operator= ( const Self & )

privatedelete

Is the input model file readable and compatible with the corresponding classifier ?

◆ PrintSelf()

template<class TInputValue , class TOutputValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::PrintSelf	(	std::ostream &	os,
		itk::Indent	indent
	)		const

overrideprotected

PrintSelf method

Definition at line 301 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ Save()

template<class TInputValue , class TOutputValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::Save	(	const std::string &	filename,
		const std::string &	name = `""`
	)

overridevirtual

Save the model to file

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 244 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ SetActivateFunction()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetActivateFunction ( int _arg )

virtual

Run-time type information (and related methods).

◆ SetAlpha()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetAlpha ( double _arg )

virtual

Run-time type information (and related methods).

◆ SetBackPropDWScale()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetBackPropDWScale ( double _arg )

virtual

Run-time type information (and related methods).

◆ SetBackPropMomentScale()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetBackPropMomentScale ( double _arg )

virtual

Run-time type information (and related methods).

◆ SetBeta()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetBeta ( double _arg )

virtual

Run-time type information (and related methods).

◆ SetEpsilon()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetEpsilon ( double _arg )

virtual

Run-time type information (and related methods).

◆ SetLayerSizes()

template<class TInputValue , class TOutputValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::SetLayerSizes ( const std::vector< unsigned int > layers )

Set the number of neurons in each layer (including input and output layers). The number of neuron in the first layer (input layer) must be equal to the number of samples in the InputListSample

Sets the topology of the NN

Definition at line 53 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ SetMaxIter()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetMaxIter ( int _arg )

virtual

Run-time type information (and related methods).

◆ SetRegPropDW0()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetRegPropDW0 ( double _arg )

virtual

Run-time type information (and related methods).

◆ SetRegPropDWMin()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetRegPropDWMin ( double _arg )

virtual

Run-time type information (and related methods).

◆ SetTermCriteriaType()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetTermCriteriaType ( int _arg )

virtual

Run-time type information (and related methods).

◆ SetTrainMethod()

template<class TInputValue , class TTargetValue >

virtual void otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::SetTrainMethod ( int _arg )

virtual

Run-time type information (and related methods).

◆ SetupNetworkAndTrain()

template<class TInputValue , class TOutputValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::SetupNetworkAndTrain ( cv::Mat & labels )

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 145 of file otbNeuralNetworkMachineLearningModel.hxx.

◆ Train()

template<class TInputValue , class TOutputValue >

void otb::NeuralNetworkMachineLearningModel< TInputValue, TOutputValue >::Train

overridevirtual

Train the machine learning model

Train the machine learning model for classification

Implements otb::MachineLearningModel< TInputValue, TTargetValue >.

Definition at line 166 of file otbNeuralNetworkMachineLearningModel.hxx.

Member Data Documentation

◆ m_ActivateFunction

template<class TInputValue , class TTargetValue >

int otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_ActivateFunction

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 208 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_Alpha

template<class TInputValue , class TTargetValue >

double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_Alpha

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 211 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_ANNModel

template<class TInputValue , class TTargetValue >

cv::Ptr<cv::ml::ANN_MLP> otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_ANNModel

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 206 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_BackPropDWScale

template<class TInputValue , class TTargetValue >

double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_BackPropDWScale

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 213 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_BackPropMomentScale

template<class TInputValue , class TTargetValue >

double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_BackPropMomentScale

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 214 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_Beta

template<class TInputValue , class TTargetValue >

double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_Beta

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 212 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_Epsilon

template<class TInputValue , class TTargetValue >

double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_Epsilon

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 219 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_LayerSizes

template<class TInputValue , class TTargetValue >

std::vector<unsigned int> otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_LayerSizes

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 209 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_MapOfLabels

template<class TInputValue , class TTargetValue >

MapOfLabelsType otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_MapOfLabels

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 222 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_MatrixOfLabels

template<class TInputValue , class TTargetValue >

cv::Mat otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_MatrixOfLabels

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 221 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_MaxIter

template<class TInputValue , class TTargetValue >

int otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_MaxIter

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 218 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_RegPropDW0

template<class TInputValue , class TTargetValue >

double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_RegPropDW0

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 215 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_RegPropDWMin

template<class TInputValue , class TTargetValue >

double otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_RegPropDWMin

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 216 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_TermCriteriaType

template<class TInputValue , class TTargetValue >

int otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_TermCriteriaType

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 217 of file otbNeuralNetworkMachineLearningModel.h.

◆ m_TrainMethod

template<class TInputValue , class TTargetValue >

int otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >::m_TrainMethod

private

Is the input model file readable and compatible with the corresponding classifier ?

Definition at line 207 of file otbNeuralNetworkMachineLearningModel.h.

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

Public Types

Classification model file compatibility tests

Additional Inherited Members

Detailed Description

template<class TInputValue, class TTargetValue> class otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >

Member Typedef Documentation

◆ ConfidenceValueType

◆ ConstPointer

◆ InputListSampleType

◆ InputSampleType

◆ InputValueType

◆ MapOfLabelsType

◆ Pointer

◆ ProbaSampleType

◆ Self

◆ Superclass

◆ TargetListSampleType

◆ TargetSampleType

◆ TargetValueType

Constructor & Destructor Documentation

◆ NeuralNetworkMachineLearningModel() [1/2]

◆ ~NeuralNetworkMachineLearningModel()

◆ NeuralNetworkMachineLearningModel() [2/2]

Member Function Documentation

◆ CanReadFile()

◆ CanWriteFile()

◆ CreateAnother()

◆ CreateNetwork()

◆ DoPredict()

◆ GetActivateFunction()

◆ GetAlpha()

◆ GetBackPropDWScale()

◆ GetBackPropMomentScale()

◆ GetBeta()

◆ GetEpsilon()

◆ GetMaxIter()

◆ GetNameOfClass()

◆ GetRegPropDW0()

◆ GetRegPropDWMin()

◆ GetTermCriteriaType()

◆ GetTrainMethod()

◆ LabelsToMat()

◆ Load()

◆ New()

◆ operator=()

◆ PrintSelf()

◆ Save()

◆ SetActivateFunction()

◆ SetAlpha()

◆ SetBackPropDWScale()

◆ SetBackPropMomentScale()

◆ SetBeta()

◆ SetEpsilon()

◆ SetLayerSizes()

◆ SetMaxIter()

◆ SetRegPropDW0()

◆ SetRegPropDWMin()

◆ SetTermCriteriaType()

◆ SetTrainMethod()

◆ SetupNetworkAndTrain()

◆ Train()

Member Data Documentation

◆ m_ActivateFunction

◆ m_Alpha

◆ m_ANNModel

◆ m_BackPropDWScale

◆ m_BackPropMomentScale

◆ m_Beta

◆ m_Epsilon

◆ m_LayerSizes

◆ m_MapOfLabels

◆ m_MatrixOfLabels

◆ m_MaxIter

◆ m_RegPropDW0

◆ m_RegPropDWMin

◆ m_TermCriteriaType

◆ m_TrainMethod

template<class TInputValue, class TTargetValue>
class otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue >