#include <shark/Data/Pgm.h>
#include <shark/Data/SparseData.h>
#include <shark/Models/Autoencoder.h>
#include <shark/Models/TiedAutoencoder.h>
#include <shark/Models/ConcatenatedModel.h>
#include <shark/ObjectiveFunctions/ErrorFunction.h>
#include <shark/Algorithms/GradientDescent/Rprop.h>
#include <shark/ObjectiveFunctions/Loss/SquaredLoss.h>
#include <shark/ObjectiveFunctions/Regularizer.h>
Go to the source code of this file.
Functions | |
template<class AutoencoderModel > | |
AutoencoderModel | trainAutoencoderModel (UnlabeledData< RealVector > const &data, std::size_t numHidden, std::size_t iterations, double regularisation, double noiseStrength) |
int | main (int argc, char **argv) |
int main | ( | int | argc, |
char ** | argv | ||
) |
AutoencoderModel trainAutoencoderModel | ( | UnlabeledData< RealVector > const & | data, |
std::size_t | numHidden, | ||
std::size_t | iterations, | ||
double | regularisation, | ||
double | noiseStrength | ||
) |
Definition at line 62 of file DenoisingAutoencoderTutorial.cpp.
References shark::dataDimension(), shark::ConcatenatedModel< VectorType >::enableModelOptimization(), shark::ErrorFunction::init(), shark::IRpropPlusFull::init(), shark::initRandomUniform(), shark::ConcatenatedModel< VectorType >::name(), noise, shark::ErrorFunction::numberOfVariables(), shark::ResultSet< SearchPointT, ResultT >::point, shark::ConcatenatedModel< VectorType >::setParameterVector(), shark::ErrorFunction::setRegularizer(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), shark::IRpropPlusFull::step(), and shark::ResultSet< SearchPointT, ResultT >::value.