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/*
* Copyright (C) 2005-2022 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.
*/
#ifndef otbDimensionalityReductionTrainAutoencoder_hxx
#define otbDimensionalityReductionTrainAutoencoder_hxx
#include "otbTrainDimensionalityReductionApplicationBase.h"
#include "otbAutoencoderModel.h"
namespace otb
{
namespace Wrapper
{
template <class TInputValue, class TOutputValue>
void TrainDimensionalityReductionApplicationBase<TInputValue, TOutputValue>::InitAutoencoderParams()
{
AddChoice("algorithm.autoencoder", "Shark Autoencoder");
SetParameterDescription("algorithm.autoencoder", "This group of parameters allows setting Shark autoencoder parameters. ");
// 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", "Epsilon");
SetParameterFloat("algorithm.autoencoder.epsilon", 0, false);
SetParameterDescription("algorithm.autoencoder.epsilon", "Epsilon");
AddParameter(ParameterType_Float, "algorithm.autoencoder.initfactor", "Weight initialization factor");
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");
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::LogisticNeuron NeuronType;
typedef otb::AutoencoderModel<InputValueType, NeuronType> AutoencoderModelType;
TrainAutoencoder<AutoencoderModelType>(trainingListSample, modelPath);
}
template <class TInputValue, class TOutputValue>
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"))
{
dimredTrainer->SetWriteLearningCurve(true);
dimredTrainer->SetLearningCurveFileName(GetParameterString("algorithm.autoencoder.learningcurve"));
}
dimredTrainer->SetInputListSample(trainingListSample);
dimredTrainer->Train();
dimredTrainer->Save(modelPath);
}
} // end namespace wrapper
} // end namespace otb
#endif
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