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Guillaume Pasero authoredf52ef084
#ifndef cbTrainAutoencoder_txx#define cbTrainAutoencoder_txx#include "otbTrainDimensionalityReductionApplicationBase.h"#include "otbAutoencoderModel.h"namespace otb{namespace Wrapper{template <class TInputValue, class TOutputValue>voidTrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>::InitAutoencoderParams(){ AddChoice("algorithm.tiedautoencoder", "Shark Tied Autoencoder"); AddChoice("algorithm.autoencoder", "Shark Autoencoder"); SetParameterDescription("algorithm.autoencoder", "This group of parameters allows setting Shark autoencoder parameters. " ); //Tied Autoencoder AddParameter(ParameterType_Choice, "algorithm.autoencoder.istied", "tied weighth <tied/untied>"); SetParameterDescription( "algorithm.autoencoder.istied", "Parameter that determine if the weights are tied or not <tied/untied>"); AddChoice("algorithm.autoencoder.istied.yes","Tied weigths"); AddChoice("algorithm.autoencoder.istied.no","Untied weights"); //Number Of Iterations AddParameter(ParameterType_Int, "algorithm.autoencoder.nbiter", "Maximum number of iterations during training"); SetParameterInt("algorithm.autoencoder.nbiter",100, false); SetParameterDescription( "algorithm.autoencoder.nbiter", "The maximum number of iterations used during training."); AddParameter(ParameterType_Int, "algorithm.autoencoder.nbiterfinetuning", "Maximum number of iterations during training"); SetParameterInt("algorithm.autoencoder.nbiterfinetuning",0, false); SetParameterDescription( "algorithm.autoencoder.nbiterfinetuning", "The maximum number of iterations used during fine tuning of the whole network."); AddParameter(ParameterType_Float, "algorithm.autoencoder.epsilon", " "); SetParameterFloat("algorithm.autoencoder.epsilon",0, false); SetParameterDescription( "algorithm.autoencoder.epsilon", " "); AddParameter(ParameterType_Float, "algorithm.autoencoder.initfactor", " "); SetParameterFloat("algorithm.autoencoder.initfactor",1, false); SetParameterDescription( "algorithm.autoencoder.initfactor", "parameter that control the weight initialization of the autoencoder"); //Number Of Hidden Neurons AddParameter(ParameterType_StringList , "algorithm.autoencoder.nbneuron", "Size");7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140
/*AddParameter(ParameterType_Int, "algorithm.autoencoder.nbneuron",
"Number of neurons in the hidden layer");
SetParameterInt("algorithm.autoencoder.nbneuron",10, false);*/
SetParameterDescription(
"algorithm.autoencoder.nbneuron",
"The number of neurons in each hidden layer.");
//Regularization
AddParameter(ParameterType_StringList, "algorithm.autoencoder.regularization", "Strength of the regularization");
SetParameterDescription("algorithm.autoencoder.regularization",
"Strength of the L2 regularization used during training");
//Noise strength
AddParameter(ParameterType_StringList, "algorithm.autoencoder.noise", "Strength of the noise");
SetParameterDescription("algorithm.autoencoder.noise",
"Strength of the noise");
// Sparsity parameter
AddParameter(ParameterType_StringList, "algorithm.autoencoder.rho", "Sparsity parameter");
SetParameterDescription("algorithm.autoencoder.rho",
"Sparsity parameter");
// Sparsity regularization strength
AddParameter(ParameterType_StringList, "algorithm.autoencoder.beta", "Sparsity regularization strength");
SetParameterDescription("algorithm.autoencoder.beta",
"Sparsity regularization strength");
AddParameter(ParameterType_OutputFilename, "algorithm.autoencoder.learningcurve", "Learning curve");
SetParameterDescription("algorithm.autoencoder.learningcurve", "Learning error values");
MandatoryOff("algorithm.autoencoder.learningcurve");
}
template <class TInputValue, class TOutputValue>
void
TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>
::BeforeTrainAutoencoder(typename ListSampleType::Pointer trainingListSample,
std::string modelPath)
{
// typedef shark::Autoencoder< shark::TanhNeuron, shark::LinearNeuron> AutoencoderType;
typedef shark::LogisticNeuron 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;
*/
std::string TiedWeigth = GetParameterString("algorithm.autoencoder.istied");
std::cout << TiedWeigth << std::endl;
if(TiedWeigth == "no")
{
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;
}
}
template <class TInputValue, class TOutputValue>
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template <typename autoencoderchoice>
void TrainDimensionalityReductionApplicationBase<TInputValue,TOutputValue>::TrainAutoencoder(typename ListSampleType::Pointer trainingListSample,std::string modelPath)
{
typename autoencoderchoice::Pointer dimredTrainer = autoencoderchoice::New();
itk::Array<unsigned int> nb_neuron;
itk::Array<float> noise;
itk::Array<float> regularization;
itk::Array<float> rho;
itk::Array<float> beta;
std::vector<std::basic_string<char>> s_nbneuron= GetParameterStringList("algorithm.autoencoder.nbneuron");
std::vector<std::basic_string<char>> s_noise= GetParameterStringList("algorithm.autoencoder.noise");
std::vector<std::basic_string<char>> s_regularization= GetParameterStringList("algorithm.autoencoder.regularization");
std::vector<std::basic_string<char>> s_rho= GetParameterStringList("algorithm.autoencoder.rho");
std::vector<std::basic_string<char>> s_beta= GetParameterStringList("algorithm.autoencoder.beta");
nb_neuron.SetSize(s_nbneuron.size());
noise.SetSize(s_nbneuron.size());
regularization.SetSize(s_nbneuron.size());
rho.SetSize(s_nbneuron.size());
beta.SetSize(s_nbneuron.size());
for (unsigned int i=0; i<s_nbneuron.size(); i++){
nb_neuron[i]=std::stoi(s_nbneuron[i]);
noise[i]=std::stof(s_noise[i]);
regularization[i]=std::stof(s_regularization[i]);
rho[i]=std::stof(s_rho[i]);
beta[i]=std::stof(s_beta[i]);
}
dimredTrainer->SetNumberOfHiddenNeurons(nb_neuron);
dimredTrainer->SetNumberOfIterations(GetParameterInt("algorithm.autoencoder.nbiter"));
dimredTrainer->SetNumberOfIterationsFineTuning(GetParameterInt("algorithm.autoencoder.nbiterfinetuning"));
dimredTrainer->SetEpsilon(GetParameterFloat("algorithm.autoencoder.epsilon"));
dimredTrainer->SetInitFactor(GetParameterFloat("algorithm.autoencoder.initfactor"));
dimredTrainer->SetRegularization(regularization);
dimredTrainer->SetNoise(noise);
dimredTrainer->SetRho(rho);
dimredTrainer->SetBeta(beta);
dimredTrainer->SetWriteWeights(true);
if (HasValue("algorithm.autoencoder.learningcurve") && IsParameterEnabled("algorithm.autoencoder.learningcurve"))
{
std::cout << "yo" << std::endl;
dimredTrainer->SetWriteLearningCurve(true);
dimredTrainer->SetLearningCurveFileName(GetParameterString("algorithm.autoencoder.learningcurve"));
}
dimredTrainer->SetInputListSample(trainingListSample);
std::cout << "before train" << std::endl;
dimredTrainer->Train();
std::cout << "after train" << std::endl;
dimredTrainer->Save(modelPath);
}
} //end namespace wrapper
} //end namespace otb
#endif