diff --git a/include/AutoencoderModel.h b/include/AutoencoderModel.h
index df5f03280a366d00012480856f56faa068ac8d65..cde280d83a9a99824d5c8a99bc585f5cd8b0b286 100644
--- a/include/AutoencoderModel.h
+++ b/include/AutoencoderModel.h
@@ -27,9 +27,9 @@ public:
itkNewMacro(Self);
itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);
- unsigned int GetDimension() {return m_NumberOfHiddenNeurons;}; // Override the Dimensionality Reduction model method, it is used in the dimensionality reduction filter to set the output image size
- itkGetMacro(NumberOfHiddenNeurons,unsigned int);
- itkSetMacro(NumberOfHiddenNeurons,unsigned int);
+ unsigned int GetDimension() {return m_NumberOfHiddenNeurons[m_net.size()-1];}; // Override the Dimensionality Reduction model method, it is used in the dimensionality reduction filter to set the output image size
+ itkGetMacro(NumberOfHiddenNeurons,itk::Array<unsigned int>);
+ itkSetMacro(NumberOfHiddenNeurons,itk::Array<unsigned int>);
itkGetMacro(NumberOfIterations,unsigned int);
itkSetMacro(NumberOfIterations,unsigned int);
@@ -47,6 +47,7 @@ public:
void Load(const std::string & filename, const std::string & name="") ITK_OVERRIDE;
void Train() ITK_OVERRIDE;
+ void TrainOneLayer(unsigned int, shark::Data<shark::RealVector> &);
protected:
AutoencoderModel();
@@ -59,8 +60,9 @@ private:
/** Network attributes */
std::vector<AutoencoderType> m_net;
- unsigned int m_NumberOfHiddenNeurons;
+
+ itk::Array<unsigned int> m_NumberOfHiddenNeurons;
/** Training parameters */
unsigned int m_NumberOfIterations;
double m_Regularization; // L2 Regularization parameter
diff --git a/include/AutoencoderModel.txx b/include/AutoencoderModel.txx
index d13105ef277e2018b813b5f9c4f980835da6acde..ac949029818171e74378c44de9168b58cad20175 100644
--- a/include/AutoencoderModel.txx
+++ b/include/AutoencoderModel.txx
@@ -31,23 +31,35 @@ AutoencoderModel<TInputValue,AutoencoderType>::~AutoencoderModel()
{
}
-
template <class TInputValue, class AutoencoderType>
void AutoencoderModel<TInputValue,AutoencoderType>::Train()
{
- AutoencoderType net;
std::vector<shark::RealVector> features;
+ Shark::ListSampleToSharkVector(this->GetInputListSample(), features);
+ shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
+
+ for (unsigned int i = 0 ; i < m_NumberOfHiddenNeurons.Size(); ++i)
+ {
+ TrainOneLayer( m_NumberOfHiddenNeurons[i], inputSamples);
+ }
+}
+
+template <class TInputValue, class AutoencoderType>
+void AutoencoderModel<TInputValue,AutoencoderType>::TrainOneLayer(unsigned int nbneuron, shark::Data<shark::RealVector> &samples)
+{
+ AutoencoderType net;
+ /*std::vector<shark::RealVector> features;
Shark::ListSampleToSharkVector(this->GetInputListSample(), features);
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
-
- std::size_t inputs = dataDimension(inputSamples);
- net.setStructure(inputs, m_NumberOfHiddenNeurons);
+ */ //in Train() now
+ std::size_t inputs = dataDimension(samples);
+ net.setStructure(inputs, nbneuron);
initRandomUniform(net,-0.1*std::sqrt(1.0/inputs),0.1*std::sqrt(1.0/inputs));
shark::ImpulseNoiseModel noise(m_Noise,0.0); //set an input pixel with probability m_Noise to 0
shark::ConcatenatedModel<shark::RealVector,shark::RealVector> model = noise>> net;
- shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(inputSamples,inputSamples);//labels identical to inputs
+ shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(samples,samples);//labels identical to inputs
shark::SquaredLoss<shark::RealVector> loss;
shark::ErrorFunction error(trainSet, &model, &loss);
shark::TwoNormRegularizer regularizer(error.numberOfVariables());
@@ -63,7 +75,7 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Train()
}
net.setParameterVector(optimizer.solution().point);
m_net.push_back(net);
-
+ samples = net.encode(samples);
}
@@ -108,16 +120,19 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Load(const std::string & fil
char autoencoder[256];
ifs.getline(autoencoder,256);
std::string autoencoderstr(autoencoder);
- std::cout << "oy" << std::endl;
if (autoencoderstr != net.name()){
itkExceptionMacro(<< "Error opening " << filename.c_str() );
}
- std::cout << "yo" << std::endl;
boost::archive::polymorphic_text_iarchive ia(ifs);
//m_net.read(ia);
ia >> m_net;
ifs.close();
- m_NumberOfHiddenNeurons = m_net[0].numberOfHiddenNeurons();
+
+ m_NumberOfHiddenNeurons.SetSize(m_net.size());
+ for (int i=0; i<m_net.size(); i++){
+ m_NumberOfHiddenNeurons[i] = m_net[i].numberOfHiddenNeurons();
+ }
+
}
@@ -135,15 +150,18 @@ AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType &
features.push_back(samples);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
-
- data = m_net[0].encode(data);
+
+ for (int i=0; i<m_net.size(); i++){ // loop over all autoencoders in m_net
+ data = m_net[i].encode(data);
+ }
TargetSampleType target;
- target.SetSize(m_NumberOfHiddenNeurons);
+ target.SetSize(m_NumberOfHiddenNeurons[m_net.size()-1]);
- for(unsigned int a = 0; a < m_NumberOfHiddenNeurons; ++a){
+ for(unsigned int a = 0; a < m_NumberOfHiddenNeurons[m_net.size()-1]; ++a){
target[a]=data.element(0)[a];
}
return target;
+
}
@@ -155,12 +173,16 @@ void AutoencoderModel<TInputValue,AutoencoderType>
Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
TargetSampleType target;
- data = m_net[0].encode(data);
+
+ for (auto net :m_net ){ // loop over all autoencoders in m_net
+ data = net.encode(data);
+ }
+
unsigned int id = startIndex;
- target.SetSize(m_NumberOfHiddenNeurons);
- for(const auto& p : data.elements()){
-
- for(unsigned int a = 0; a < m_NumberOfHiddenNeurons; ++a){
+ target.SetSize(m_NumberOfHiddenNeurons[m_net.size()-1]);
+ for(const auto& p : data.elements())
+ {
+ for(unsigned int a = 0; a < m_NumberOfHiddenNeurons[m_net.size()-1]; ++a){
target[a]=p[a];
}
targets->SetMeasurementVector(id,target);
diff --git a/include/cbTrainAutoencoder.txx b/include/cbTrainAutoencoder.txx
index 7cef519d144f934f6b5319d321bc5234a4b8f4d6..62b66cf8a9ff5349dbd1f4bdd7ee6d908f4b7c16 100644
--- a/include/cbTrainAutoencoder.txx
+++ b/include/cbTrainAutoencoder.txx
@@ -46,12 +46,13 @@ cbLearningApplicationBaseDR<TInputValue,TOutputValue>
//Number Of Hidden Neurons
- AddParameter(ParameterType_Int, "model.autoencoder.nbneuron",
+ AddParameter(ParameterType_StringList , "model.autoencoder.nbneuron", "Size");
+ /*AddParameter(ParameterType_Int, "model.autoencoder.nbneuron",
"Number of neurons in the hidden layer");
- SetParameterInt("model.autoencoder.nbneuron",10, false);
+ SetParameterInt("model.autoencoder.nbneuron",10, false);*/
SetParameterDescription(
"model.autoencoder.nbneuron",
- "The number of neurons in the hidden layer.");
+ "The number of neurons in each hidden layer.");
//Regularization
AddParameter(ParameterType_Float, "model.autoencoder.regularization", "Strength of the regularization");
@@ -100,7 +101,14 @@ void cbLearningApplicationBaseDR<TInputValue,TOutputValue>
::TrainAutoencoder(typename ListSampleType::Pointer trainingListSample,std::string modelPath)
{
typename autoencoderchoice::Pointer dimredTrainer = autoencoderchoice::New();
- dimredTrainer->SetNumberOfHiddenNeurons(GetParameterInt("model.autoencoder.nbneuron"));
+ itk::Array<unsigned int> nb_neuron;
+ std::vector<std::basic_string<char>> s= GetParameterStringList("model.autoencoder.nbneuron");
+ nb_neuron.SetSize(s.size());
+ for (int i=0; i<s.size(); i++){ // This will be templated later (the 3)
+ nb_neuron[i]=std::stoi(s[i]);
+ }
+ std::cout << nb_neuron << std::endl;
+ dimredTrainer->SetNumberOfHiddenNeurons(nb_neuron);
dimredTrainer->SetNumberOfIterations(GetParameterInt("model.autoencoder.nbiter"));
dimredTrainer->SetRegularization(GetParameterFloat("model.autoencoder.regularization"));
dimredTrainer->SetRegularization(GetParameterFloat("model.autoencoder.noise"));
@@ -108,7 +116,9 @@ void cbLearningApplicationBaseDR<TInputValue,TOutputValue>
std::cout << "before train" << std::endl;
dimredTrainer->Train();
std::cout << "after train" << std::endl;
- dimredTrainer->Save(modelPath);
+ dimredTrainer->Save(modelPath);
+
+
}
} //end namespace wrapper