otbAutoencoderModel.h 6.76 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
/*
 * 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.
 */
20 21
#ifndef otbAutoencoderModel_h
#define otbAutoencoderModel_h
22

23 24
#include "otbMachineLearningModelTraits.h"
#include "otbMachineLearningModel.h"
25
#include <string>
26

27 28 29 30 31
#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"
32
#pragma GCC diagnostic ignored "-Wsign-compare"
33
#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
34 35 36 37
#if defined(__clang__)
#pragma clang diagnostic ignored "-Wheader-guard"
#pragma clang diagnostic ignored "-Wdivision-by-zero"
#endif
38 39 40
#endif
#include "otb_shark.h"
#include <shark/Algorithms/StoppingCriteria/AbstractStoppingCriterion.h>
41 42 43
#include <shark/Models/LinearModel.h>
#include <shark/Models/ConcatenatedModel.h>
#include <shark/Models/NeuronLayers.h>
44 45 46 47
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif

48 49
namespace otb
{
50 51 52 53 54 55 56
/**
 * \class AutoencoderModel
 *
 * Autoencoder model wrapper class
 *
 * \ingroup OTBDimensionalityReductionLearning
 */
57
template <class TInputValue, class NeuronType>
58 59 60 61
class ITK_EXPORT AutoencoderModel
  : public  MachineLearningModel<
    itk::VariableLengthVector< TInputValue>,
    itk::VariableLengthVector< TInputValue> >
62 63
{
public:
64
  typedef AutoencoderModel Self;
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
  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
85 86 87
  typedef shark::ConcatenatedModel<shark::RealVector> ModelType;
  typedef shark::LinearModel<shark::RealVector,NeuronType> LayerType;
  typedef shark::LinearModel<shark::RealVector, shark::LinearNeuron> OutLayerType;
88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127

  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);

128 129
  bool CanReadFile(const std::string & filename) override;
  bool CanWriteFile(const std::string & filename) override;
130

131 132
  void Save(const std::string & filename, const std::string & name="")  override;
  void Load(const std::string & filename, const std::string & name="")  override;
133

134
  void Train() override;
135

136
  template <class T>
137 138 139 140 141 142
  void TrainOneLayer(
    shark::AbstractStoppingCriterion<T> & criterion,
    unsigned int,
    shark::Data<shark::RealVector> &,
    std::ostream&);

143
  template <class T>
144 145 146 147 148 149 150 151 152 153 154 155
  void TrainOneSparseLayer(
    shark::AbstractStoppingCriterion<T> & criterion,
    unsigned int,
    shark::Data<shark::RealVector> &,
    std::ostream&);

  template <class T>
  void TrainNetwork(
    shark::AbstractStoppingCriterion<T> & criterion,
    shark::Data<shark::RealVector> &,
    std::ostream&);

156
protected:
157
  AutoencoderModel();
158
  ~AutoencoderModel() override;
159 160 161

  virtual TargetSampleType DoPredict(
    const InputSampleType& input,
162
    ConfidenceValueType * quality = nullptr) const override;
163 164 165 166 167 168

  virtual void DoPredictBatch(
    const InputListSampleType *,
    const unsigned int & startIndex,
    const unsigned int & size,
    TargetListSampleType *,
169
    ConfidenceListSampleType * quality = nullptr) const override;
170

Cédric Traizet's avatar
Cédric Traizet committed
171
private:
172
  /** Internal Network */
173 174 175
  ModelType m_Encoder;
  std::vector<LayerType> m_InLayers;
  OutLayerType m_OutLayer;
176 177 178 179 180 181 182 183 184 185 186
  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)  )

187
  bool m_WriteLearningCurve; // Flag for writing the learning curve into a txt file
188 189
  std::string m_LearningCurveFileName; // Name of the output learning curve printed after training
  bool m_WriteWeights;
190 191 192 193
};
} // end namespace otb

#ifndef OTB_MANUAL_INSTANTIATION
194
#include "otbAutoencoderModel.hxx"
195 196 197
#endif

#endif
198