OTB  9.0.0
Orfeo Toolbox
Public Types | List of all members
otb::NeuralNetworkMachineLearningModel< TInputValue, TTargetValue > Class Template Reference

#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 SelfConstPointer
 
typedef Superclass::InputListSampleType InputListSampleType
 
typedef Superclass::InputSampleType InputSampleType
 
typedef Superclass::InputValueType InputValueType
 
typedef std::map< TargetValueType, unsigned int > MapOfLabelsType
 
typedef itk::SmartPointer< SelfPointer
 
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< SelfPointer
 
typedef itk::SmartPointer< const SelfConstPointer
 
typedef MLMSampleTraits< TInputValue >::ValueType InputValueType
 
typedef MLMSampleTraits< TInputValue >::SampleType InputSampleType
 
typedef itk::Statistics::ListSample< InputSampleTypeInputListSampleType
 
typedef MLMTargetTraits< TTargetValue >::ValueType TargetValueType
 
typedef MLMTargetTraits< TTargetValue >::SampleType TargetSampleType
 
typedef itk::Statistics::ListSample< TargetSampleTypeTargetListSampleType
 
typedef MLMTargetTraits< double >::ValueType ConfidenceValueType
 
typedef MLMTargetTraits< double >::SampleType ConfidenceSampleType
 
typedef itk::Statistics::ListSample< ConfidenceSampleTypeConfidenceListSampleType
 
typedef itk::VariableLengthVector< double > ProbaSampleType
 
typedef itk::Statistics::ListSample< ProbaSampleTypeProbaListSampleType
 
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 InputListSampleTypeGetInputListSample () 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

◆ ~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:

◆ 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:

◆ 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: