#include <shark/Data/Pgm.h>
#include <shark/Data/SparseData.h>
#include <shark/Models/Autoencoder.h>
#include <shark/Models/TiedAutoencoder.h>
#include <shark/Models/ImpulseNoiseModel.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>

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)

Function Documentation

§ main()

int main	(	int	argc,
		char **	argv
	)

Definition at line 49 of file DenoisingAutoencoderTutorial.cpp.

References shark::LabeledData< InputT, LabelT >::element(), shark::exportFiltersToPGMGrid(), shark::importSparseData(), shark::LabeledData< InputT, LabelT >::inputs(), and shark::LabeledData< InputT, LabelT >::numberOfElements().

§ trainAutoencoderModel()

template<class AutoencoderModel >

AutoencoderModel trainAutoencoderModel	(	UnlabeledData< RealVector > const &	data,
		std::size_t	numHidden,
		std::size_t	iterations,
		double	regularisation,
		double	noiseStrength
	)

Definition at line 18 of file DenoisingAutoencoderTutorial.cpp.

References shark::dataDimension(), shark::IRpropPlusFull::init(), shark::initRandomUniform(), shark::ConcatenatedModel< InputType, OutputType >::name(), noise, shark::ErrorFunction::numberOfVariables(), shark::ResultSet< SearchPointT, ResultT >::point, shark::ConcatenatedModel< InputType, OutputType >::setParameterVector(), shark::ErrorFunction::setRegularizer(), shark::Autoencoder< HiddenNeuron, OutputNeuron >::setStructure(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), shark::IRpropPlusFull::step(), and shark::ResultSet< SearchPointT, ResultT >::value.