#include <shark/Models/LinearModel.h>#include <shark/Models/ConcatenatedModel.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.
Functions | |
| LabeledData< RealVector, unsigned int > | createProblem () |
| BinaryRBM | trainRBM (UnlabeledData< RealVector > const &data, std::size_t numHidden, std::size_t iterations, double regularisation, double learningRate) |
| int | main () |
| LabeledData<RealVector,unsigned int> createProblem | ( | ) |
Definition at line 19 of file DeepNetworkTrainingRBM.cpp.
References shark::random::coinToss(), and shark::random::globalRng.
Referenced by main().
| int main | ( | ) |
Definition at line 86 of file DeepNetworkTrainingRBM.cpp.
References createProblem(), shark::ZeroOneLoss< unsigned int, RealVector >::eval(), shark::RBM< VisibleLayerT, HiddenLayerT, randomT >::evaluationType(), shark::RBM< VisibleLayerT, HiddenLayerT, randomT >::hiddenNeurons(), shark::ErrorFunction::init(), shark::IRpropPlusFull::init(), shark::initRandomNormal(), 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::ErrorFunction::setRegularizer(), shark::LabeledData< InputT, LabelT >::shuffle(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), shark::splitAtElement(), shark::IRpropPlusFull::step(), trainRBM(), shark::ResultSet< SearchPointT, ResultT >::value, and shark::RBM< VisibleLayerT, HiddenLayerT, randomT >::weightMatrix().
| BinaryRBM trainRBM | ( | UnlabeledData< RealVector > const & | data, |
| std::size_t | numHidden, | ||
| std::size_t | iterations, | ||
| double | regularisation, | ||
| double | learningRate | ||
| ) |
Definition at line 48 of file DeepNetworkTrainingRBM.cpp.
References shark::dataDimension(), shark::random::globalRng, shark::SteepestDescent::init(), shark::AbstractObjectiveFunction< PointType, ResultT >::init(), shark::initRandomUniform(), shark::ResultSet< SearchPointT, ResultT >::point, shark::ContrastiveDivergence< Operator >::setData(), shark::ContrastiveDivergence< Operator >::setK(), shark::SteepestDescent::setLearningRate(), shark::RBM< VisibleLayerT, HiddenLayerT, randomT >::setParameterVector(), shark::ContrastiveDivergence< Operator >::setRegularizer(), shark::RBM< VisibleLayerT, HiddenLayerT, randomT >::setStructure(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), and shark::SteepestDescent::step().
Referenced by main().