#include <shark/Models/FFNet.h>
#include <shark/Unsupervised/RBM/BinaryRBM.h>
#include <shark/ObjectiveFunctions/ErrorFunction.h>
#include <shark/ObjectiveFunctions/Loss/SquaredLoss.h>
#include <shark/ObjectiveFunctions/Loss/CrossEntropy.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>
#include <shark/ObjectiveFunctions/Regularizer.h>
#include <shark/Algorithms/GradientDescent/SteepestDescent.h>
#include <shark/Algorithms/GradientDescent/Rprop.h>
Go to the source code of this file.
Typedefs | |
typedef FFNet< LogisticNeuron, LinearNeuron > | Network |
Functions | |
LabeledData< RealVector, unsigned int > | createProblem () |
BinaryRBM | trainRBM (UnlabeledData< RealVector > const &data, std::size_t numHidden, std::size_t iterations, double regularisation, double learningRate) |
Network | unsupervisedPreTraining (UnlabeledData< RealVector > const &data, std::size_t numHidden1, std::size_t numHidden2, std::size_t numOutputs, double regularisation, std::size_t iterations, double learningRate) |
int | main () |
typedef FFNet<LogisticNeuron,LinearNeuron> Network |
Definition at line 83 of file DeepNetworkTrainingRBM.cpp.
LabeledData<RealVector,unsigned int> createProblem | ( | ) |
Definition at line 18 of file DeepNetworkTrainingRBM.cpp.
References shark::coinToss(), shark::createLabeledDataFromRange(), and shark::blas::subrange().
Referenced by main().
int main | ( | ) |
Definition at line 118 of file DeepNetworkTrainingRBM.cpp.
References createProblem(), shark::ZeroOneLoss< unsigned int, RealVector >::eval(), shark::IRpropPlusFull::init(), shark::LabeledData< InputT, LabelT >::inputs(), shark::LabeledData< InputT, LabelT >::labels(), shark::numberOfClasses(), shark::LabeledData< InputT, LabelT >::numberOfElements(), shark::ErrorFunction::numberOfVariables(), shark::ResultSet< SearchPointT, ResultT >::point, shark::FFNet< HiddenNeuron, OutputNeuron >::setParameterVector(), shark::ErrorFunction::setRegularizer(), shark::LabeledData< InputT, LabelT >::shuffle(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), shark::splitAtElement(), shark::IRpropPlusFull::step(), unsupervisedPreTraining(), and shark::ResultSet< SearchPointT, ResultT >::value.
BinaryRBM trainRBM | ( | UnlabeledData< RealVector > const & | data, |
std::size_t | numHidden, | ||
std::size_t | iterations, | ||
double | regularisation, | ||
double | learningRate | ||
) |
Definition at line 47 of file DeepNetworkTrainingRBM.cpp.
References shark::dataDimension(), shark::SteepestDescent::init(), shark::initRandomUniform(), shark::ResultSet< SearchPointT, ResultT >::point, shark::ContrastiveDivergence< Operator >::setData(), shark::ContrastiveDivergence< Operator >::setK(), shark::SteepestDescent::setLearningRate(), shark::RBM< VisibleLayerT, HiddenLayerT, RngT >::setParameterVector(), shark::ContrastiveDivergence< Operator >::setRegularizer(), shark::RBM< VisibleLayerT, HiddenLayerT, RngT >::setStructure(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), and shark::SteepestDescent::step().
Referenced by unsupervisedPreTraining().
Network unsupervisedPreTraining | ( | UnlabeledData< RealVector > const & | data, |
std::size_t | numHidden1, | ||
std::size_t | numHidden2, | ||
std::size_t | numOutputs, | ||
double | regularisation, | ||
std::size_t | iterations, | ||
double | learningRate | ||
) |
Definition at line 86 of file DeepNetworkTrainingRBM.cpp.
References shark::dataDimension(), shark::RBM< VisibleLayerT, HiddenLayerT, RngT >::evaluationType(), shark::RBM< VisibleLayerT, HiddenLayerT, RngT >::hiddenNeurons(), shark::initRandomNormal(), shark::FFNet< HiddenNeuron, OutputNeuron >::setLayer(), shark::FFNet< HiddenNeuron, OutputNeuron >::setStructure(), trainRBM(), and shark::RBM< VisibleLayerT, HiddenLayerT, RngT >::weightMatrix().
Referenced by main().