Commit b2b0fc4e authored by Cédric Traizet's avatar Cédric Traizet
Browse files

autoencodermodel's attribute "m_net" is now a vector (works for a vector of size 1)

parent cf6f75b9
......@@ -58,7 +58,7 @@ protected:
private:
/** Network attributes */
AutoencoderType m_net;
std::vector<AutoencoderType> m_net;
unsigned int m_NumberOfHiddenNeurons;
/** Training parameters */
......
......@@ -35,6 +35,7 @@ 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);
......@@ -42,11 +43,10 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Train()
shark::Data<shark::RealVector> inputSamples = shark::createDataFromRange( features );
std::size_t inputs = dataDimension(inputSamples);
m_net.setStructure(inputs, m_NumberOfHiddenNeurons);
initRandomUniform(m_net,-0.1*std::sqrt(1.0/inputs),0.1*std::sqrt(1.0/inputs));
net.setStructure(inputs, m_NumberOfHiddenNeurons);
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>> m_net;
shark::ConcatenatedModel<shark::RealVector,shark::RealVector> model = noise>> net;
shark::LabeledData<shark::RealVector,shark::RealVector> trainSet(inputSamples,inputSamples);//labels identical to inputs
shark::SquaredLoss<shark::RealVector> loss;
shark::ErrorFunction error(trainSet, &model, &loss);
......@@ -56,13 +56,13 @@ void AutoencoderModel<TInputValue,AutoencoderType>::Train()
shark::IRpropPlusFull optimizer;
error.init();
optimizer.init(error);
std::cout<<"Optimizing model: "+m_net.name()<<std::endl;
std::cout<<"Optimizing model: "+net.name()<<std::endl;
for(std::size_t i = 0; i != m_NumberOfIterations; ++i){
optimizer.step(error);
std::cout<<i<<" "<<optimizer.solution().value<<std::endl;
}
m_net.setParameterVector(optimizer.solution().point);
net.setParameterVector(optimizer.solution().point);
m_net.push_back(net);
}
......@@ -73,7 +73,7 @@ bool AutoencoderModel<TInputValue,AutoencoderType>::CanReadFile(const std::strin
try
{
this->Load(filename);
m_net.name();
m_net[0].name();
}
catch(...)
{
......@@ -93,27 +93,31 @@ template <class TInputValue, class AutoencoderType>
void AutoencoderModel<TInputValue,AutoencoderType>::Save(const std::string & filename, const std::string & name)
{
std::ofstream ofs(filename);
ofs << m_net.name() << std::endl; // the first line of the model file contains a key
ofs << m_net[0].name() << std::endl; // the first line of the model file contains a key
boost::archive::polymorphic_text_oarchive oa(ofs);
m_net.write(oa);
//m_net.write(oa);
oa << m_net;
ofs.close();
}
template <class TInputValue, class AutoencoderType>
void AutoencoderModel<TInputValue,AutoencoderType>::Load(const std::string & filename, const std::string & name)
{
AutoencoderType net;
std::ifstream ifs(filename);
char autoencoder[256];
ifs.getline(autoencoder,256);
std::string autoencoderstr(autoencoder);
if (autoencoderstr != m_net.name()){
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);
//m_net.read(ia);
ia >> m_net;
ifs.close();
m_NumberOfHiddenNeurons = m_net.numberOfHiddenNeurons();
m_NumberOfHiddenNeurons = m_net[0].numberOfHiddenNeurons();
}
......@@ -132,7 +136,7 @@ AutoencoderModel<TInputValue,AutoencoderType>::DoPredict(const InputSampleType &
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
data = m_net.encode(data);
data = m_net[0].encode(data);
TargetSampleType target;
target.SetSize(m_NumberOfHiddenNeurons);
......@@ -151,7 +155,7 @@ void AutoencoderModel<TInputValue,AutoencoderType>
Shark::ListSampleRangeToSharkVector(input, features,startIndex,size);
shark::Data<shark::RealVector> data = shark::createDataFromRange(features);
TargetSampleType target;
data = m_net.encode(data);
data = m_net[0].encode(data);
unsigned int id = startIndex;
target.SetSize(m_NumberOfHiddenNeurons);
for(const auto& p : data.elements()){
......
......@@ -35,7 +35,7 @@ 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::LinearNeuron>> ;
template <class TInputValue, class TTargetValue>
......
......@@ -105,7 +105,9 @@ void cbLearningApplicationBaseDR<TInputValue,TOutputValue>
dimredTrainer->SetRegularization(GetParameterFloat("model.autoencoder.regularization"));
dimredTrainer->SetRegularization(GetParameterFloat("model.autoencoder.noise"));
dimredTrainer->SetInputListSample(trainingListSample);
std::cout << "before train" << std::endl;
dimredTrainer->Train();
std::cout << "after train" << std::endl;
dimredTrainer->Save(modelPath);
}
......
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