diff --git a/include/AutoencoderModel.h b/include/AutoencoderModel.h
index b957d5d3115090392651afcb8d29db4c3638f639..cb3adeac6d1a7dbbeb0420f769f7e35843d8e5b2 100644
--- a/include/AutoencoderModel.h
+++ b/include/AutoencoderModel.h
@@ -6,9 +6,11 @@
#include <fstream>
#include <shark/Algorithms/StoppingCriteria/AbstractStoppingCriterion.h>
+#include <shark/Models/FFNet.h>
+#include <shark/Models/Autoencoder.h>
namespace otb
{
-template <class TInputValue, class AutoencoderType>
+template <class TInputValue, class NeuronType>
class ITK_EXPORT AutoencoderModel: public MachineLearningModel<itk::VariableLengthVector< TInputValue> , itk::VariableLengthVector< TInputValue>>
{
@@ -33,7 +35,12 @@ public:
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);
@@ -77,10 +84,10 @@ public:
void Train() ITK_OVERRIDE;
template <class T>
- void TrainOneLayer(shark::AbstractStoppingCriterion<T> & criterion, unsigned int,double, double, shark::Data<shark::RealVector> &, std::ostream&);
+ void TrainOneLayer(shark::AbstractStoppingCriterion<T> & criterion,unsigned int, unsigned int,double, double, shark::Data<shark::RealVector> &, std::ostream&);
template <class T>
- void TrainOneSparseLayer(shark::AbstractStoppingCriterion<T> & criterion, unsigned int,double, double,double, shark::Data<shark::RealVector> &, std::ostream&);
+ void TrainOneSparseLayer(shark::AbstractStoppingCriterion<T> & criterion,unsigned int, unsigned int,double, double,double, shark::Data<shark::RealVector> &, std::ostream&);
protected:
AutoencoderModel();
@@ -93,8 +100,8 @@ protected:
private:
/** Network attributes */
- std::vector<AutoencoderType> m_net;
-
+ //std::vector<AutoencoderType> m_net;
+ NetworkType m_net;
itk::Array<unsigned int> m_NumberOfHiddenNeurons;
diff --git a/include/AutoencoderModel.txx b/include/AutoencoderModel.txx
index 7103ec98eba48f7261fd068507af384f792c4744..95d8721ce53672001b401516d58dfd7442d6bb4e 100644
--- a/include/AutoencoderModel.txx
+++ b/include/AutoencoderModel.txx
@@ -21,40 +21,35 @@
namespace otb
{
-template <class TInputValue, class AutoencoderType>
-AutoencoderModel<TInputValue,AutoencoderType>::AutoencoderModel()
+template <class TInputValue, class NeuronType>
+AutoencoderModel<TInputValue,NeuronType>::AutoencoderModel()
{
this->m_IsDoPredictBatchMultiThreaded = true;
this->m_WriteLearningCurve = false;
}
-template <class TInputValue, class AutoencoderType>
-AutoencoderModel<TInputValue,AutoencoderType>::~AutoencoderModel()
+template <class TInputValue, class NeuronType>
+AutoencoderModel<TInputValue,NeuronType>::~AutoencoderModel()
{
}
-template <class TInputValue, class AutoencoderType>
-void AutoencoderModel<TInputValue,AutoencoderType>::Train()
+template <class TInputValue, class NeuronType>
+void AutoencoderModel<TInputValue,NeuronType>::Train()
{
- std::cout << this->m_WriteLearningCurve << std::endl;
std::vector<shark::RealVector> features;
- std::cout << "converting the input ListSample to Shark vector" << std::endl;
Shark::ListSampleToSharkVector(this->GetInputListSample(), features);
- std::cout << "creating the data vector" << std::endl;
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
-
std::ofstream ofs;
if (this->m_WriteLearningCurve =true)
{
ofs.open(m_LearningCurveFileName);
ofs << "learning curve" << std::endl;
}
- /*
- std::ofstream ofs("/mnt/data/home/traizetc/computation/learning_curve.txt"); //learning curve
- ofs << "learning curve" << std::endl;
- */
+
+
+
if (m_Epsilon > 0){
shark::TrainingProgress<> criterion(5,m_Epsilon);
@@ -62,11 +57,11 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Train()
{
if (m_Noise[i] != 0) // Shark doesn't allow to train a layer using a sparsity term AND a noisy input. (shark::SparseAutoencoderError takes an autoen
{
- TrainOneLayer(criterion, m_NumberOfHiddenNeurons[i],m_Noise[i],m_Regularization[i], inputSamples,ofs);
+ TrainOneLayer(criterion,i , m_NumberOfHiddenNeurons[i],m_Noise[i],m_Regularization[i], inputSamples,ofs);
}
else
{
- TrainOneSparseLayer( criterion,m_NumberOfHiddenNeurons[i],m_Rho[i],m_Beta[i],m_Regularization[i],inputSamples, ofs);
+ TrainOneSparseLayer( criterion,i , m_NumberOfHiddenNeurons[i],m_Rho[i],m_Beta[i],m_Regularization[i],inputSamples, ofs);
}
criterion.reset();
}
@@ -80,11 +75,11 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Train()
{
if (m_Noise[i] != 0) // Shark doesn't allow to train a layer using a sparsity term AND a noisy input. (shark::SparseAutoencoderError takes an autoen
{
- TrainOneLayer(criterion, m_NumberOfHiddenNeurons[i],m_Noise[i],m_Regularization[i], inputSamples, ofs);
+ TrainOneLayer(criterion,i, m_NumberOfHiddenNeurons[i],m_Noise[i],m_Regularization[i], inputSamples, ofs);
}
else
{
- TrainOneSparseLayer(criterion, m_NumberOfHiddenNeurons[i],m_Rho[i],m_Beta[i],m_Regularization[i], inputSamples, ofs);
+ TrainOneSparseLayer(criterion,i, m_NumberOfHiddenNeurons[i],m_Rho[i],m_Beta[i],m_Regularization[i], inputSamples, ofs);
}
criterion.reset();
}
@@ -92,9 +87,9 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Train()
}
}
-template <class TInputValue, class AutoencoderType>
+template <class TInputValue, class NeuronType>
template <class T>
-void AutoencoderModel<TInputValue,AutoencoderType>::TrainOneLayer(shark::AbstractStoppingCriterion<T> & criterion, unsigned int nbneuron,double noise_strength,double regularization, shark::Data<shark::RealVector> &samples, std::ostream& File)
+void AutoencoderModel<TInputValue,NeuronType>::TrainOneLayer(shark::AbstractStoppingCriterion<T> & criterion,unsigned int layer_index, unsigned int nbneuron,double noise_strength,double regularization, shark::Data<shark::RealVector> &samples, std::ostream& File)
{
AutoencoderType net;
@@ -133,14 +128,16 @@ void AutoencoderModel<TInputValue,AutoencoderType>::TrainOneLayer(shark::Abstrac
std::cout<<"error after " << i << "iterations : " << optimizer.solution().value<<std::endl;
net.setParameterVector(optimizer.solution().point);
- m_net.push_back(net);
+ // m_net.push_back(net);
+ m_net.setLayer(layer_index,net.encoderMatrix(),net.hiddenBias());
+ m_net.setLayer( m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index,net.decoderMatrix(),net.outputBias());
samples = net.encode(samples);
}
-template <class TInputValue, class AutoencoderType>
+template <class TInputValue, class NeuronType>
template <class T>
-void AutoencoderModel<TInputValue,AutoencoderType>::TrainOneSparseLayer(shark::AbstractStoppingCriterion<T> & criterion, unsigned int nbneuron,double rho,double beta, double regularization, shark::Data<shark::RealVector> &samples, std::ostream& File)
+void AutoencoderModel<TInputValue,NeuronType>::TrainOneSparseLayer(shark::AbstractStoppingCriterion<T> & criterion,unsigned int layer_index, unsigned int nbneuron,double rho,double beta, double regularization, shark::Data<shark::RealVector> &samples, std::ostream& File)
{
AutoencoderType net;
@@ -174,17 +171,19 @@ void AutoencoderModel<TInputValue,AutoencoderType>::TrainOneSparseLayer(shark::A
File << "end layer" << std::endl;
}
net.setParameterVector(optimizer.solution().point);
- m_net.push_back(net);
+ //m_net.push_back(net);
+ m_net.setLayer(layer_index,net.encoderMatrix(),net.hiddenBias());
+ m_net.setLayer( m_NumberOfHiddenNeurons.Size()*2 - 1 - layer_index,net.decoderMatrix(),net.outputBias());
samples = net.encode(samples);
}
-template <class TInputValue, class AutoencoderType>
-bool AutoencoderModel<TInputValue,AutoencoderType>::CanReadFile(const std::string & filename)
+template <class TInputValue, class NeuronType>
+bool AutoencoderModel<TInputValue,NeuronType>::CanReadFile(const std::string & filename)
{
try
{
this->Load(filename);
- m_net[0].name();
+ m_net.name();
}
catch(...)
{
@@ -194,27 +193,28 @@ bool AutoencoderModel<TInputValue,AutoencoderType>::CanReadFile(const std::strin
}
-template <class TInputValue, class AutoencoderType>
-bool AutoencoderModel<TInputValue,AutoencoderType>::CanWriteFile(const std::string & filename)
+template <class TInputValue, class NeuronType>
+bool AutoencoderModel<TInputValue,NeuronType>::CanWriteFile(const std::string & filename)
{
return true;
}
-template <class TInputValue, class AutoencoderType>
-void AutoencoderModel<TInputValue,AutoencoderType>::Save(const std::string & filename, const std::string & name)
+template <class TInputValue, class NeuronType>
+void AutoencoderModel<TInputValue,NeuronType>::Save(const std::string & filename, const std::string & name)
{
std::ofstream ofs(filename);
- ofs << m_net[0].name() << std::endl; // the first line of the model file contains a key
+ ofs << m_net.name() << std::endl; // the first line of the model file contains a key
boost::archive::polymorphic_text_oarchive oa(ofs);
//m_net.write(oa);
oa << m_net;
ofs.close();
-
+ /*
if (this->m_WriteWeights == true) // output the map vectors in a txt file
{
std::ofstream otxt(filename+".txt");
+
for (unsigned int i = 0 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
{
otxt << m_net[i].encoderMatrix() << std::endl;
@@ -248,19 +248,22 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Save(const std::string & fil
}
-
+ */
}
-template <class TInputValue, class AutoencoderType>
-void AutoencoderModel<TInputValue,AutoencoderType>::Load(const std::string & filename, const std::string & name)
+template <class TInputValue, class NeuronType>
+void AutoencoderModel<TInputValue,NeuronType>::Load(const std::string & filename, const std::string & name)
{
- AutoencoderType net;
+
+ NetworkType net;
std::ifstream ifs(filename);
char autoencoder[256];
ifs.getline(autoencoder,256);
std::string autoencoderstr(autoencoder);
+
+ std::cout << autoencoderstr << std::endl;
if (autoencoderstr != net.name()){
itkExceptionMacro(<< "Error opening " << filename.c_str() );
}
@@ -274,13 +277,15 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Load(const std::string & fil
m_NumberOfHiddenNeurons[i] = m_net[i].numberOfHiddenNeurons();
}
this->m_Dimension = m_NumberOfHiddenNeurons[m_net.size()-1];
+
}
-template <class TInputValue, class AutoencoderType>
-typename AutoencoderModel<TInputValue,AutoencoderType>::TargetSampleType
-AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType & value, ConfidenceValueType * quality) const
+template <class TInputValue, class NeuronType>
+typename AutoencoderModel<TInputValue,NeuronType>::TargetSampleType
+AutoencoderModel<TInputValue,NeuronType>::DoPredict(const InputSampleType & value, ConfidenceValueType * quality) const
{
+ /*
shark::RealVector samples(value.Size());
for(size_t i = 0; i < value.Size();i++)
{
@@ -302,14 +307,15 @@ AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType &
target[a]=data.element(0)[a];
}
return target;
-
+ */
}
-template <class TInputValue, class AutoencoderType>
-void AutoencoderModel<TInputValue,AutoencoderType>
+template <class TInputValue, class NeuronType>
+void AutoencoderModel<TInputValue,NeuronType>
::DoPredictBatch(const InputListSampleType *input, const unsigned int & startIndex, const unsigned int & size, TargetListSampleType * targets, ConfidenceListSampleType * quality) const
{
+ /*
std::vector<shark::RealVector> features;
Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
@@ -329,6 +335,7 @@ void AutoencoderModel<TInputValue,AutoencoderType>
targets->SetMeasurementVector(id,target);
++id;
}
+ */
}
} // namespace otb
diff --git a/include/AutoencoderModelFactory.h b/include/AutoencoderModelFactory.h
index 73d841e9a8cbd512d8fcd036e13bb3794b46a3f3..c9c993b57e7e258e4671777ae2b0e96bee4c4b34 100644
--- a/include/AutoencoderModelFactory.h
+++ b/include/AutoencoderModelFactory.h
@@ -10,7 +10,7 @@
namespace otb
{
-template <class TInputValue, class TTargetValue, class AutoencoderType>
+template <class TInputValue, class TTargetValue, class NeuronType>
class ITK_EXPORT AutoencoderModelFactoryBase : public itk::ObjectFactoryBase
{
public:
diff --git a/include/AutoencoderModelFactory.txx b/include/AutoencoderModelFactory.txx
index 5b0d0b754a5854b06513e9c125af36065cb2997b..47b4669ab4ad2761bd3174ac71b5d96d74b9becf 100644
--- a/include/AutoencoderModelFactory.txx
+++ b/include/AutoencoderModelFactory.txx
@@ -27,8 +27,8 @@
namespace otb
{
-template <class TInputValue, class TOutputValue, class AutoencoderType>
-AutoencoderModelFactoryBase<TInputValue,TOutputValue, AutoencoderType>::AutoencoderModelFactoryBase()
+template <class TInputValue, class TOutputValue, class NeuronType>
+AutoencoderModelFactoryBase<TInputValue,TOutputValue, NeuronType>::AutoencoderModelFactoryBase()
{
std::string classOverride = std::string("DimensionalityReductionModel");
@@ -39,22 +39,22 @@ AutoencoderModelFactoryBase<TInputValue,TOutputValue, AutoencoderType>::Autoenco
"Shark AE ML Model",
1,
// itk::CreateObjectFunction<AutoencoderModel<TInputValue,TOutputValue> >::New());
- itk::CreateObjectFunction<AutoencoderModel<TInputValue,AutoencoderType > >::New());
+ itk::CreateObjectFunction<AutoencoderModel<TInputValue,NeuronType > >::New());
}
-template <class TInputValue, class TOutputValue, class AutoencoderType>
-AutoencoderModelFactoryBase<TInputValue,TOutputValue, AutoencoderType>::~AutoencoderModelFactoryBase()
+template <class TInputValue, class TOutputValue, class NeuronType>
+AutoencoderModelFactoryBase<TInputValue,TOutputValue, NeuronType>::~AutoencoderModelFactoryBase()
{
}
-template <class TInputValue, class TOutputValue, class AutoencoderType>
-const char* AutoencoderModelFactoryBase<TInputValue,TOutputValue, AutoencoderType>::GetITKSourceVersion(void) const
+template <class TInputValue, class TOutputValue, class NeuronType>
+const char* AutoencoderModelFactoryBase<TInputValue,TOutputValue, NeuronType>::GetITKSourceVersion(void) const
{
return ITK_SOURCE_VERSION;
}
-template <class TInputValue, class TOutputValue, class AutoencoderType>
-const char* AutoencoderModelFactoryBase<TInputValue,TOutputValue, AutoencoderType>::GetDescription() const
+template <class TInputValue, class TOutputValue, class NeuronType>
+const char* AutoencoderModelFactoryBase<TInputValue,TOutputValue, NeuronType>::GetDescription() const
{
return "Autoencoder model factory";
}
diff --git a/include/DimensionalityReductionModelFactory.txx b/include/DimensionalityReductionModelFactory.txx
index 23fcadddde79b3bbdf2973351f844523ca1b180d..fb65be910ec931cc5b86a6be1c659babe1b33ac5 100644
--- a/include/DimensionalityReductionModelFactory.txx
+++ b/include/DimensionalityReductionModelFactory.txx
@@ -34,6 +34,7 @@
namespace otb
{
+/*
template <class TInputValue, class TTargetValue>
// using AutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::Autoencoder<shark::TanhNeuron, shark::LinearNeuron>> ;
using AutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::Autoencoder<shark::TanhNeuron, shark::TanhNeuron>> ;
@@ -42,6 +43,10 @@ using AutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTarget
template <class TInputValue, class TTargetValue>
// using TiedAutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron>> ;
using TiedAutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::TiedAutoencoder< shark::TanhNeuron, shark::TanhNeuron>> ;
+*/
+
+template <class TInputValue, class TTargetValue>
+using AutoencoderModelFactory = AutoencoderModelFactoryBase<TInputValue, TTargetValue, shark::TanhNeuron> ;
template <class TInputValue, class TTargetValue>
@@ -125,7 +130,7 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
#ifdef OTB_USE_SHARK
RegisterFactory(PCAModelFactory<TInputValue,TOutputValue>::New());
RegisterFactory(AutoencoderModelFactory<TInputValue,TOutputValue>::New());
- RegisterFactory(TiedAutoencoderModelFactory<TInputValue,TOutputValue>::New());
+ // RegisterFactory(TiedAutoencoderModelFactory<TInputValue,TOutputValue>::New());
#endif
}
@@ -200,6 +205,7 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
continue;
}
+ /*
TiedAutoencoderModelFactory<TInputValue,TOutputValue> *taeFactory =
dynamic_cast<TiedAutoencoderModelFactory<TInputValue,TOutputValue> *>(*itFac);
if (taeFactory)
@@ -207,7 +213,7 @@ DimensionalityReductionModelFactory<TInputValue,TOutputValue>
itk::ObjectFactoryBase::UnRegisterFactory(taeFactory);
continue;
}
-
+ */
// PCA
PCAModelFactory<TInputValue,TOutputValue> *pcaFactory =
dynamic_cast<PCAModelFactory<TInputValue,TOutputValue> *>(*itFac);
diff --git a/include/cbLearningApplicationBaseDR.h b/include/cbLearningApplicationBaseDR.h
index 6207527baadd28134925ab380ea80401ca3466c3..084db66a4caab98a4dfffc51745c099c4dd16c90 100644
--- a/include/cbLearningApplicationBaseDR.h
+++ b/include/cbLearningApplicationBaseDR.h
@@ -103,13 +103,13 @@ public:
#ifdef OTB_USE_SHARK
// typedef shark::Autoencoder< shark::TanhNeuron, shark::LinearNeuron> AutoencoderType;
- typedef shark::Autoencoder< shark::TanhNeuron, shark::TanhNeuron> AutoencoderType;
- typedef otb::AutoencoderModel<InputValueType, AutoencoderType> AutoencoderModelType;
-
+ typedef shark::TanhNeuron NeuronType;
+ typedef otb::AutoencoderModel<InputValueType, NeuronType> AutoencoderModelType;
+ /*
// typedef shark::TiedAutoencoder< shark::TanhNeuron, shark::LinearNeuron> TiedAutoencoderType;
typedef shark::TiedAutoencoder< shark::TanhNeuron, shark::TanhNeuron> TiedAutoencoderType;
typedef otb::AutoencoderModel<InputValueType, TiedAutoencoderType> TiedAutoencoderModelType;
-
+ */
typedef otb::PCAModel<InputValueType> PCAModelType;
#endif
diff --git a/include/cbLearningApplicationBaseDR.txx b/include/cbLearningApplicationBaseDR.txx
index 6e1635e24db59edaff241405174f1e54ce5f5143..19b5e4e414f98c1fb49d7d62a8f08dfc1946e8ab 100644
--- a/include/cbLearningApplicationBaseDR.txx
+++ b/include/cbLearningApplicationBaseDR.txx
@@ -89,6 +89,7 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
#endif
}
+ /*
if(modelName == "tiedautoencoder")
{
#ifdef OTB_USE_SHARK
@@ -97,7 +98,7 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
otbAppLogFATAL("Module SharkLearning is not installed. You should consider turning OTB_USE_SHARK on during cmake configuration.");
#endif
}
-
+ */
if(modelName == "pca")
{
#ifdef OTB_USE_SHARK
diff --git a/include/cbTrainAutoencoder.txx b/include/cbTrainAutoencoder.txx
index 05c5ee0207a1411f927bbde925cec7b2d217130d..b44f99461a8f29ae072973012d5f1a7d0ad356d0 100644
--- a/include/cbTrainAutoencoder.txx
+++ b/include/cbTrainAutoencoder.txx
@@ -101,12 +101,12 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
{
TrainAutoencoder<AutoencoderModelType>(trainingListSample,modelPath);
}
-
+ /*
if(TiedWeigth == "yes")
{
TrainAutoencoder<TiedAutoencoderModelType>(trainingListSample,modelPath);
}
-
+ */
if(TiedWeigth != "yes" && TiedWeigth != "no")
{
std::cerr << "istied : invalid choice <yes/no>" << std::endl;