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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.
*/
#include "otbAutoencoderModel.h"
#include "otbReadDataFile.h"
#include "itkMacro.h"
typedef otb::AutoencoderModel<double, shark::LogisticNeuron> LogAutoencoderModel;
typedef LogAutoencoderModel::InputListSampleType InputListSampleType;
typedef LogAutoencoderModel::TargetListSampleType TargetListSampleType;
int otbAutoencoderModelCanRead(int argc, char* argv[])
{
if (argc < 2)
{
std::cerr << "Usage: " << argv[0] << " <model>" << std::endl;
return EXIT_FAILURE;
}
LogAutoencoderModel::Pointer model = LogAutoencoderModel::New();
std::string filename(argv[1]);
if (!model->CanReadFile(filename))
{
std::cerr << "Failed to read model file : " << filename << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
int otbAutoencoderModeTrain(int argc, char* argv[])
{
if (argc < 3)
{
std::cerr << "Usage: " << argv[0] << " letter.scale model.out" << std::endl;
return EXIT_FAILURE;
}
// Extract data from letter.scale
InputListSampleType::Pointer samples = InputListSampleType::New();
TargetListSampleType::Pointer target = TargetListSampleType::New();
if (!otb::ReadDataFile(argv[1], samples, target))
{
std::cout << "Failed to read samples file " << argv[1] << std::endl;
return EXIT_FAILURE;
}
itk::Array<unsigned int> nb_neuron;
itk::Array<float> noise;
itk::Array<float> regularization;
itk::Array<float> rho;
itk::Array<float> beta;
nb_neuron.SetSize(1);
noise.SetSize(1);
regularization.SetSize(1);
rho.SetSize(1);
beta.SetSize(1);
nb_neuron[0] = 14;
noise[0] = 0.0;
regularization[0] = 0.01;
rho[0] = 0.0;
beta[0] = 0.0;
LogAutoencoderModel::Pointer model = LogAutoencoderModel::New();
model->SetNumberOfHiddenNeurons(nb_neuron);
model->SetNumberOfIterations(50);
model->SetNumberOfIterationsFineTuning(0);
model->SetEpsilon(0.0);
model->SetInitFactor(1.0);
model->SetRegularization(regularization);
model->SetNoise(noise);
model->SetRho(rho);
model->SetBeta(beta);
model->SetWriteWeights(true);
model->SetInputListSample(samples);
model->Train();
model->Save(std::string(argv[2]));
return EXIT_SUCCESS;
}
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