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/*
* Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef otbAutoencoderModel_h
#define otbAutoencoderModel_h
#include "otbMachineLearningModelTraits.h"
#include "otbMachineLearningModel.h"
#include <fstream>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wshadow"
#pragma GCC diagnostic ignored "-Wunused-parameter"
#pragma GCC diagnostic ignored "-Woverloaded-virtual"
#pragma GCC diagnostic ignored "-Wsign-compare"
#pragma GCC diagnostic ignored "-Wunused-local-typedefs"
#if defined(__clang__)
#pragma clang diagnostic ignored "-Wheader-guard"
#pragma clang diagnostic ignored "-Wdivision-by-zero"
#pragma clang diagnostic ignored "-Wexpansion-to-defined"
#else
#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
#endif
#endif
#include "otb_shark.h"
#include <shark/Algorithms/StoppingCriteria/AbstractStoppingCriterion.h>
#include <shark/Models/FFNet.h>
#include <shark/Models/Autoencoder.h>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif
namespace otb
{
/**
* \class AutoencoderModel
*
* Autoencoder model wrapper class
*
* \ingroup OTBDimensionalityReductionLearning
*/
template <class TInputValue, class NeuronType>
class ITK_EXPORT AutoencoderModel
: public MachineLearningModel<
itk::VariableLengthVector< TInputValue>,
itk::VariableLengthVector< TInputValue> >
{
public:
typedef AutoencoderModel Self;
typedef MachineLearningModel<
itk::VariableLengthVector< TInputValue>,
itk::VariableLengthVector< TInputValue> > Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
typedef typename Superclass::InputValueType InputValueType;
typedef typename Superclass::InputSampleType InputSampleType;
typedef typename Superclass::InputListSampleType InputListSampleType;
typedef typename InputListSampleType::Pointer ListSamplePointerType;
typedef typename Superclass::TargetValueType TargetValueType;
typedef typename Superclass::TargetSampleType TargetSampleType;
typedef typename Superclass::TargetListSampleType TargetListSampleType;
/// Confidence map related typedefs
typedef typename Superclass::ConfidenceValueType ConfidenceValueType;
typedef typename Superclass::ConfidenceSampleType ConfidenceSampleType;
typedef typename Superclass::ConfidenceListSampleType ConfidenceListSampleType;
/// Neural network related typedefs
typedef shark::Autoencoder<NeuronType,shark::LinearNeuron> OutAutoencoderType;
typedef shark::Autoencoder<NeuronType,NeuronType> AutoencoderType;
typedef shark::FFNet<NeuronType,shark::LinearNeuron> NetworkType;
itkNewMacro(Self);
itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);
itkGetMacro(NumberOfHiddenNeurons,itk::Array<unsigned int>);
itkSetMacro(NumberOfHiddenNeurons,itk::Array<unsigned int>);
itkGetMacro(NumberOfIterations,unsigned int);
itkSetMacro(NumberOfIterations,unsigned int);
itkGetMacro(NumberOfIterationsFineTuning,unsigned int);
itkSetMacro(NumberOfIterationsFineTuning,unsigned int);
itkGetMacro(Epsilon,double);
itkSetMacro(Epsilon,double);
itkGetMacro(InitFactor,double);
itkSetMacro(InitFactor,double);
itkGetMacro(Regularization,itk::Array<double>);
itkSetMacro(Regularization,itk::Array<double>);
itkGetMacro(Noise,itk::Array<double>);
itkSetMacro(Noise,itk::Array<double>);
itkGetMacro(Rho,itk::Array<double>);
itkSetMacro(Rho,itk::Array<double>);
itkGetMacro(Beta,itk::Array<double>);
itkSetMacro(Beta,itk::Array<double>);
itkGetMacro(WriteLearningCurve,bool);
itkSetMacro(WriteLearningCurve,bool);
itkSetMacro(WriteWeights, bool);
itkGetMacro(WriteWeights, bool);
itkGetMacro(LearningCurveFileName,std::string);
itkSetMacro(LearningCurveFileName,std::string);
bool CanReadFile(const std::string & filename) override;
bool CanWriteFile(const std::string & filename) override;
void Save(const std::string & filename, const std::string & name="") override;
void Load(const std::string & filename, const std::string & name="") override;
void Train() override;
template <class T, class Autoencoder>
void TrainOneLayer(
shark::AbstractStoppingCriterion<T> & criterion,
Autoencoder &,
unsigned int,
shark::Data<shark::RealVector> &,
std::ostream&);
template <class T, class Autoencoder>
void TrainOneSparseLayer(
shark::AbstractStoppingCriterion<T> & criterion,
Autoencoder &,
unsigned int,
shark::Data<shark::RealVector> &,
std::ostream&);
template <class T>
void TrainNetwork(
shark::AbstractStoppingCriterion<T> & criterion,
shark::Data<shark::RealVector> &,
std::ostream&);
protected:
AutoencoderModel();
~AutoencoderModel() override;
virtual TargetSampleType DoPredict(
const InputSampleType& input,
ConfidenceValueType * quality = ITK_NULLPTR) const override;
virtual void DoPredictBatch(
const InputListSampleType *,
const unsigned int & startIndex,
const unsigned int & size,
TargetListSampleType *,
ConfidenceListSampleType * quality = ITK_NULLPTR) const override;
private:
/** Internal Network */
NetworkType m_Net;
itk::Array<unsigned int> m_NumberOfHiddenNeurons;
/** Training parameters */
unsigned int m_NumberOfIterations; // stop the training after a fixed number of iterations
unsigned int m_NumberOfIterationsFineTuning; // stop the fine tuning after a fixed number of iterations
double m_Epsilon; // Stops the training when the training error seems to converge
itk::Array<double> m_Regularization; // L2 Regularization parameter
itk::Array<double> m_Noise; // probability for an input to be set to 0 (denosing autoencoder)
itk::Array<double> m_Rho; // Sparsity parameter
itk::Array<double> m_Beta; // Sparsity regularization parameter
double m_InitFactor; // Weight initialization factor (the weights are intialized at m_initfactor/sqrt(inputDimension) )
bool m_WriteLearningCurve; // Flag for writing the learning curve into a txt file
std::string m_LearningCurveFileName; // Name of the output learning curve printed after training
bool m_WriteWeights;
};
} // end namespace otb
#ifndef OTB_MANUAL_INSTANTIATION
#include "otbAutoencoderModel.txx"
#endif
#endif
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