#include <shark/Models/FFNet.h>#include <shark/Models/Autoencoder.h>#include <shark/Models/ConcatenatedModel.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 Autoencoder< RectifierNeuron, LinearNeuron > | AutoencoderModel |
| typedef FFNet< RectifierNeuron, LinearNeuron > | Network |
Functions | |
| LabeledData< RealVector, unsigned int > | createProblem () |
| template<class AutoencoderModel > | |
| AutoencoderModel | trainAutoencoderModel (UnlabeledData< RealVector > const &data, std::size_t numHidden, double regularisation, std::size_t iterations) |
| Network | unsupervisedPreTraining (UnlabeledData< RealVector > const &data, std::size_t numHidden1, std::size_t numHidden2, std::size_t numOutputs, double regularisation, std::size_t iterations) |
| int | main () |
| typedef Autoencoder<RectifierNeuron,LinearNeuron> AutoencoderModel |
Definition at line 79 of file DeepNetworkTraining.cpp.
| typedef FFNet<RectifierNeuron,LinearNeuron> Network |
Definition at line 80 of file DeepNetworkTraining.cpp.
| LabeledData<RealVector,unsigned int> createProblem | ( | ) |
Definition at line 19 of file DeepNetworkTraining.cpp.
References shark::random::coinToss(), and shark::random::globalRng.
Referenced by main().
| int main | ( | ) |
Definition at line 115 of file DeepNetworkTraining.cpp.
References createProblem(), shark::ZeroOneLoss< unsigned int, RealVector >::eval(), shark::ErrorFunction::init(), 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::ErrorFunction::setRegularizer(), shark::LabeledData< InputT, LabelT >::shuffle(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), shark::splitAtElement(), shark::IRpropPlusFull::step(), unsupervisedPreTraining(), and shark::ResultSet< SearchPointT, ResultT >::value.
| AutoencoderModel trainAutoencoderModel | ( | UnlabeledData< RealVector > const & | data, |
| std::size_t | numHidden, | ||
| double | regularisation, | ||
| std::size_t | iterations | ||
| ) |
Definition at line 49 of file DeepNetworkTraining.cpp.
References shark::dataDimension(), shark::ErrorFunction::init(), shark::IRpropPlusFull::init(), shark::initRandomUniform(), shark::ErrorFunction::numberOfVariables(), shark::ResultSet< SearchPointT, ResultT >::point, shark::ErrorFunction::setRegularizer(), shark::AbstractSingleObjectiveOptimizer< PointType >::solution(), shark::IRpropPlusFull::step(), and shark::ResultSet< SearchPointT, ResultT >::value.
| Network unsupervisedPreTraining | ( | UnlabeledData< RealVector > const & | data, |
| std::size_t | numHidden1, | ||
| std::size_t | numHidden2, | ||
| std::size_t | numOutputs, | ||
| double | regularisation, | ||
| std::size_t | iterations | ||
| ) |
Definition at line 83 of file DeepNetworkTraining.cpp.
References shark::dataDimension(), and shark::initRandomNormal().
Referenced by main().