Trainer responsible of fitting a neural network on predefined data. More...

#include <LibtorchANNTrainer.h>

Inheritance diagram for LibtorchANNTrainer:

Public Types
typedef DataFileName	DataFileParameterType

Public Member Functions
	LibtorchANNTrainer (const InputParameters &parameters)
	Construct using input parameters. More...

virtual void	preTrain () override
	Contains processes which are executed before the training loop. More...

virtual void	train () override
	Contains processes which are executed for every sample in the training loop. More...

virtual void	postTrain () override
	Contains processes which are executed after the training loop. More...

virtual void	initialize () final

virtual void	execute () final

virtual void	finalize () final

virtual void	threadJoin (const UserObject &) final

SubProblem &	getSubProblem () const

bool	shouldDuplicateInitialExecution () const

virtual Real	spatialValue (const Point &) const

virtual const std::vector< Point >	spatialPoints () const

void	gatherSum (T &value)

void	gatherMax (T &value)

void	gatherMin (T &value)

void	gatherProxyValueMax (T1 &proxy, T2 &value)

void	gatherProxyValueMin (T1 &proxy, T2 &value)

void	setPrimaryThreadCopy (UserObject *primary)

UserObject *	primaryThreadCopy ()

std::set< UserObjectName >	getDependObjects () const

virtual bool	needThreadedCopy () const

const std::set< std::string > &	getRequestedItems () override

const std::set< std::string > &	getSuppliedItems () override

unsigned int	systemNumber () const

virtual bool	enabled () const

std::shared_ptr< MooseObject >	getSharedPtr ()

std::shared_ptr< const MooseObject >	getSharedPtr () const

MooseApp &	getMooseApp () const

const std::string &	type () const

virtual const std::string &	name () const

std::string	typeAndName () const

std::string	errorPrefix (const std::string &error_type) const

void	callMooseError (std::string msg, const bool with_prefix) const

MooseObjectParameterName	uniqueParameterName (const std::string &parameter_name) const

const InputParameters &	parameters () const

MooseObjectName	uniqueName () const

const T &	getParam (const std::string &name) const

std::vector< std::pair< T1, T2 > >	getParam (const std::string &param1, const std::string &param2) const

const T &	getRenamedParam (const std::string &old_name, const std::string &new_name) const

T	getCheckedPointerParam (const std::string &name, const std::string &error_string="") const

bool	isParamValid (const std::string &name) const

bool	isParamSetByUser (const std::string &nm) const

void	paramError (const std::string &param, Args... args) const

void	paramWarning (const std::string &param, Args... args) const

void	paramInfo (const std::string &param, Args... args) const

void	connectControllableParams (const std::string &parameter, const std::string &object_type, const std::string &object_name, const std::string &object_parameter) const

void	mooseError (Args &&... args) const

void	mooseErrorNonPrefixed (Args &&... args) const

void	mooseDocumentedError (const std::string &repo_name, const unsigned int issue_num, Args &&... args) const

void	mooseWarning (Args &&... args) const

void	mooseWarningNonPrefixed (Args &&... args) const

void	mooseDeprecated (Args &&... args) const

void	mooseInfo (Args &&... args) const

std::string	getDataFileName (const std::string &param) const

std::string	getDataFileNameByName (const std::string &relative_path) const

std::string	getDataFilePath (const std::string &relative_path) const

virtual void	initialSetup ()

virtual void	timestepSetup ()

virtual void	jacobianSetup ()

virtual void	residualSetup ()

virtual void	customSetup (const ExecFlagType &)

const ExecFlagEnum &	getExecuteOnEnum () const

UserObjectName	getUserObjectName (const std::string &param_name) const

const T &	getUserObject (const std::string &param_name, bool is_dependency=true) const

const T &	getUserObjectByName (const UserObjectName &object_name, bool is_dependency=true) const

const UserObject &	getUserObjectBase (const std::string &param_name, bool is_dependency=true) const

const UserObject &	getUserObjectBaseByName (const UserObjectName &object_name, bool is_dependency=true) const

const std::vector< MooseVariableScalar *> &	getCoupledMooseScalarVars ()

const std::set< TagID > &	getScalarVariableCoupleableVectorTags () const

const std::set< TagID > &	getScalarVariableCoupleableMatrixTags () const

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, MaterialData &material_data, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialProperty (const std::string &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, MaterialData &material_data, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialProperty (const std::string &name, const unsigned int state=0)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name, MaterialData &material_data)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name)

const ADMaterialProperty< T > &	getADMaterialProperty (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOld (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name)

const MaterialProperty< T > &	getMaterialPropertyOlder (const std::string &name)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data, const unsigned int state)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const GenericMaterialProperty< T, is_ad > &	getGenericMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const MaterialProperty< T > &	getMaterialPropertyByName (const MaterialPropertyName &name, const unsigned int state=0)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name, MaterialData &material_data)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name)

const ADMaterialProperty< T > &	getADMaterialPropertyByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOldByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name, MaterialData &material_data)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name)

const MaterialProperty< T > &	getMaterialPropertyOlderByName (const MaterialPropertyName &name)

std::pair< const MaterialProperty< T > *, std::set< SubdomainID > >	getBlockMaterialProperty (const MaterialPropertyName &name)

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialProperty (const std::string &name)

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialProperty ()

const GenericMaterialProperty< T, is_ad > &	getGenericZeroMaterialPropertyByName (const std::string &prop_name)

const MaterialProperty< T > &	getZeroMaterialProperty (Ts... args)

std::set< SubdomainID >	getMaterialPropertyBlocks (const std::string &name)

std::vector< SubdomainName >	getMaterialPropertyBlockNames (const std::string &name)

std::set< BoundaryID >	getMaterialPropertyBoundaryIDs (const std::string &name)

std::vector< BoundaryName >	getMaterialPropertyBoundaryNames (const std::string &name)

void	checkBlockAndBoundaryCompatibility (std::shared_ptr< MaterialBase > discrete)

std::unordered_map< SubdomainID, std::vector< MaterialBase *> >	buildRequiredMaterials (bool allow_stateful=true)

void	statefulPropertiesAllowed (bool)

bool	getMaterialPropertyCalled () const

const std::unordered_set< unsigned int > &	getMatPropDependencies () const

virtual void	resolveOptionalProperties ()

const GenericMaterialProperty< T, is_ad > &	getPossiblyConstantGenericMaterialPropertyByName (const MaterialPropertyName &prop_name, MaterialData &material_data, const unsigned int state)

bool	isImplicit ()

Moose::StateArg	determineState () const

virtual void	subdomainSetup () override

virtual void	subdomainSetup () override

bool	hasUserObject (const std::string &param_name) const

bool	hasUserObject (const std::string &param_name) const

bool	hasUserObject (const std::string &param_name) const

bool	hasUserObject (const std::string &param_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

bool	hasUserObjectByName (const UserObjectName &object_name) const

const GenericOptionalMaterialProperty< T, is_ad > &	getGenericOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const GenericOptionalMaterialProperty< T, is_ad > &	getGenericOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalMaterialProperty< T > &	getOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalMaterialProperty< T > &	getOptionalMaterialProperty (const std::string &name, const unsigned int state=0)

const OptionalADMaterialProperty< T > &	getOptionalADMaterialProperty (const std::string &name)

const OptionalADMaterialProperty< T > &	getOptionalADMaterialProperty (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOld (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOld (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOlder (const std::string &name)

const OptionalMaterialProperty< T > &	getOptionalMaterialPropertyOlder (const std::string &name)

MaterialBase &	getMaterial (const std::string &name)

MaterialBase &	getMaterial (const std::string &name)

MaterialBase &	getMaterialByName (const std::string &name, bool no_warn=false)

MaterialBase &	getMaterialByName (const std::string &name, bool no_warn=false)

bool	hasMaterialProperty (const std::string &name)

bool	hasMaterialProperty (const std::string &name)

bool	hasMaterialPropertyByName (const std::string &name)

bool	hasMaterialPropertyByName (const std::string &name)

bool	hasADMaterialProperty (const std::string &name)

bool	hasADMaterialProperty (const std::string &name)

bool	hasADMaterialPropertyByName (const std::string &name)

bool	hasADMaterialPropertyByName (const std::string &name)

bool	hasGenericMaterialProperty (const std::string &name)

bool	hasGenericMaterialProperty (const std::string &name)

bool	hasGenericMaterialPropertyByName (const std::string &name)

bool	hasGenericMaterialPropertyByName (const std::string &name)

const Function &	getFunction (const std::string &name) const

const Function &	getFunctionByName (const FunctionName &name) const

bool	hasFunction (const std::string &param_name) const

bool	hasFunctionByName (const FunctionName &name) const

bool	isDefaultPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

bool	hasPostprocessor (const std::string &param_name, const unsigned int index=0) const

bool	hasPostprocessorByName (const PostprocessorName &name) const

std::size_t	coupledPostprocessors (const std::string &param_name) const

const PostprocessorName &	getPostprocessorName (const std::string &param_name, const unsigned int index=0) const

const VectorPostprocessorValue &	getVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name, bool needs_broadcast) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const VectorPostprocessorValue &	getVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name, bool needs_broadcast) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValue (const std::string &param_name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueByName (const VectorPostprocessorName &name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueOld (const std::string &param_name, const std::string &vector_name) const

const ScatterVectorPostprocessorValue &	getScatterVectorPostprocessorValueOldByName (const VectorPostprocessorName &name, const std::string &vector_name) const

bool	hasVectorPostprocessor (const std::string &param_name, const std::string &vector_name) const

bool	hasVectorPostprocessor (const std::string &param_name) const

bool	hasVectorPostprocessorByName (const VectorPostprocessorName &name, const std::string &vector_name) const

bool	hasVectorPostprocessorByName (const VectorPostprocessorName &name) const

const VectorPostprocessorName &	getVectorPostprocessorName (const std::string &param_name) const

T &	getSampler (const std::string &name)

Sampler &	getSampler (const std::string &name)

T &	getSamplerByName (const SamplerName &name)

Sampler &	getSamplerByName (const SamplerName &name)

virtual void	meshChanged ()

PerfGraph &	perfGraph ()

const PostprocessorValue &	getPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValue (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOld (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOld (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOlder (const std::string &param_name, const unsigned int index=0) const

const PostprocessorValue &	getPostprocessorValueOlder (const std::string &param_name, const unsigned int index=0) const

virtual const PostprocessorValue &	getPostprocessorValueByName (const PostprocessorName &name) const

virtual const PostprocessorValue &	getPostprocessorValueByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOldByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOldByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOlderByName (const PostprocessorName &name) const

const PostprocessorValue &	getPostprocessorValueOlderByName (const PostprocessorName &name) const

bool	isVectorPostprocessorDistributed (const std::string &param_name) const

bool	isVectorPostprocessorDistributed (const std::string &param_name) const

bool	isVectorPostprocessorDistributedByName (const VectorPostprocessorName &name) const

bool	isVectorPostprocessorDistributedByName (const VectorPostprocessorName &name) const

const Distribution &	getDistribution (const std::string &name) const

const T &	getDistribution (const std::string &name) const

const Distribution &	getDistribution (const std::string &name) const

const T &	getDistribution (const std::string &name) const

const Distribution &	getDistributionByName (const DistributionName &name) const

const T &	getDistributionByName (const std::string &name) const

const Distribution &	getDistributionByName (const DistributionName &name) const

const T &	getDistributionByName (const std::string &name) const

const Parallel::Communicator &	comm () const

processor_id_type	n_processors () const

processor_id_type	processor_id () const

const std::string &	modelMetaDataName () const
	Accessor for the name of the model meta data. More...

const FileName &	getModelDataFileName () const
	Get the associated filename. More...

bool	hasModelData () const
	Check if we need to load model data (if the filename parameter is used) More...

template<>
SurrogateModel &	getSurrogateModel (const std::string &name) const

template<>
SurrogateTrainerBase &	getSurrogateTrainer (const std::string &name) const

template<>
SurrogateModel &	getSurrogateModelByName (const UserObjectName &name) const

template<>
SurrogateTrainerBase &	getSurrogateTrainerByName (const UserObjectName &name) const


template<typename T , typename... Args>
T &	declareModelData (const std::string &data_name, Args &&... args)
	Declare model data for loading from file as well as restart. More...


template<typename T , typename... Args>
const T &	getModelData (const std::string &data_name, Args &&... args) const
	Retrieve model data from the interface. More...


template<typename T = SurrogateModel>
T &	getSurrogateModel (const std::string &name) const
	Get a SurrogateModel/Trainer with a given name. More...

template<typename T = SurrogateTrainerBase>
T &	getSurrogateTrainer (const std::string &name) const


template<typename T = SurrogateModel>
T &	getSurrogateModelByName (const UserObjectName &name) const
	Get a sampler with a given name. More...

template<typename T = SurrogateTrainerBase>
T &	getSurrogateTrainerByName (const UserObjectName &name) const

Static Public Member Functions
static InputParameters	validParams ()

static void	sort (typename std::vector< T > &vector)

static void	sortDFS (typename std::vector< T > &vector)

static void	cyclicDependencyError (CyclicDependencyException< T2 > &e, const std::string &header)

Public Attributes
const ConsoleStream	_console

Static Public Attributes
static constexpr PropertyValue::id_type	default_property_id

static constexpr PropertyValue::id_type	zero_property_id

static constexpr auto	SYSTEM

static constexpr auto	NAME

Protected Member Functions
template<typename T >
const T &	getTrainingData (const ReporterName &rname)

const std::vector< Real > &	getSamplerData () const

const std::vector< Real > &	getPredictorData () const

unsigned int	getCurrentSampleSize () const

unsigned int	getLocalSampleSize () const

virtual std::vector< Real >	evaluateModelError (const SurrogateModel &surr)

virtual void	addPostprocessorDependencyHelper (const PostprocessorName &name) const override

virtual void	addVectorPostprocessorDependencyHelper (const VectorPostprocessorName &name) const override

virtual void	addUserObjectDependencyHelper (const UserObject &uo) const override

void	addReporterDependencyHelper (const ReporterName &reporter_name) override

const ReporterName &	getReporterName (const std::string &param_name) const

T &	declareRestartableData (const std::string &data_name, Args &&... args)

ManagedValue< T >	declareManagedRestartableDataWithContext (const std::string &data_name, void *context, Args &&... args)

const T &	getRestartableData (const std::string &data_name) const

T &	declareRestartableDataWithContext (const std::string &data_name, void *context, Args &&... args)

T &	declareRecoverableData (const std::string &data_name, Args &&... args)

T &	declareRestartableDataWithObjectName (const std::string &data_name, const std::string &object_name, Args &&... args)

T &	declareRestartableDataWithObjectNameWithContext (const std::string &data_name, const std::string &object_name, void *context, Args &&... args)

std::string	restartableName (const std::string &data_name) const

const T &	getMeshProperty (const std::string &data_name, const std::string &prefix)

const T &	getMeshProperty (const std::string &data_name)

bool	hasMeshProperty (const std::string &data_name, const std::string &prefix) const

bool	hasMeshProperty (const std::string &data_name, const std::string &prefix) const

bool	hasMeshProperty (const std::string &data_name) const

bool	hasMeshProperty (const std::string &data_name) const

std::string	meshPropertyName (const std::string &data_name) const

PerfID	registerTimedSection (const std::string &section_name, const unsigned int level) const

PerfID	registerTimedSection (const std::string &section_name, const unsigned int level, const std::string &live_message, const bool print_dots=true) const

std::string	timedSectionName (const std::string &section_name) const

bool	isCoupledScalar (const std::string &var_name, unsigned int i=0) const

unsigned int	coupledScalarComponents (const std::string &var_name) const

unsigned int	coupledScalar (const std::string &var_name, unsigned int comp=0) const

libMesh::Order	coupledScalarOrder (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarValue (const std::string &var_name, unsigned int comp=0) const

const ADVariableValue &	adCoupledScalarValue (const std::string &var_name, unsigned int comp=0) const

const GenericVariableValue< is_ad > &	coupledGenericScalarValue (const std::string &var_name, unsigned int comp=0) const

const GenericVariableValue< false > &	coupledGenericScalarValue (const std::string &var_name, const unsigned int comp) const

const GenericVariableValue< true > &	coupledGenericScalarValue (const std::string &var_name, const unsigned int comp) const

const VariableValue &	coupledVectorTagScalarValue (const std::string &var_name, TagID tag, unsigned int comp=0) const

const VariableValue &	coupledMatrixTagScalarValue (const std::string &var_name, TagID tag, unsigned int comp=0) const

const VariableValue &	coupledScalarValueOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarValueOlder (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDot (const std::string &var_name, unsigned int comp=0) const

const ADVariableValue &	adCoupledScalarDot (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDot (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDotOld (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDu (const std::string &var_name, unsigned int comp=0) const

const VariableValue &	coupledScalarDotDotDu (const std::string &var_name, unsigned int comp=0) const

const MooseVariableScalar *	getScalarVar (const std::string &var_name, unsigned int comp) const

virtual void	checkMaterialProperty (const std::string &name, const unsigned int state)

void	markMatPropRequested (const std::string &)

MaterialPropertyName	getMaterialPropertyName (const std::string &name) const

void	checkExecutionStage ()

const T &	getReporterValue (const std::string &param_name, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, const std::size_t time_index=0)

const T &	getReporterValue (const std::string &param_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, const std::size_t time_index=0)

const T &	getReporterValueByName (const ReporterName &reporter_name, ReporterMode mode, const std::size_t time_index=0)

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValue (const std::string &param_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

bool	hasReporterValueByName (const ReporterName &reporter_name) const

const GenericMaterialProperty< T, is_ad > *	defaultGenericMaterialProperty (const std::string &name)

const GenericMaterialProperty< T, is_ad > *	defaultGenericMaterialProperty (const std::string &name)

const MaterialProperty< T > *	defaultMaterialProperty (const std::string &name)

const MaterialProperty< T > *	defaultMaterialProperty (const std::string &name)

const ADMaterialProperty< T > *	defaultADMaterialProperty (const std::string &name)

const ADMaterialProperty< T > *	defaultADMaterialProperty (const std::string &name)

Static Protected Member Functions
static std::string	meshPropertyName (const std::string &data_name, const std::string &prefix)

Protected Attributes
SubProblem &	_subproblem

FEProblemBase &	_fe_problem

SystemBase &	_sys

const THREAD_ID	_tid

Assembly &	_assembly

const Moose::CoordinateSystemType &	_coord_sys

const bool	_duplicate_initial_execution

std::set< std::string >	_depend_uo

const bool &	_enabled

MooseApp &	_app

const std::string	_type

const std::string	_name

const InputParameters &	_pars

Factory &	_factory

ActionFactory &	_action_factory

const ExecFlagEnum &	_execute_enum

const ExecFlagType &	_current_execute_flag

MooseApp &	_restartable_app

const std::string	_restartable_system_name

const THREAD_ID	_restartable_tid

const bool	_restartable_read_only

FEProblemBase &	_mci_feproblem

MooseApp &	_pg_moose_app

const std::string	_prefix

FEProblemBase &	_sc_fe_problem

const THREAD_ID	_sc_tid

const Real &	_real_zero

const VariableValue &	_scalar_zero

const Point &	_point_zero

const InputParameters &	_mi_params

const std::string	_mi_name

const MooseObjectName	_mi_moose_object_name

FEProblemBase &	_mi_feproblem

SubProblem &	_mi_subproblem

const THREAD_ID	_mi_tid

const Moose::MaterialDataType	_material_data_type

MaterialData &	_material_data

bool	_stateful_allowed

bool	_get_material_property_called

std::vector< std::unique_ptr< PropertyValue > >	_default_properties

std::unordered_set< unsigned int >	_material_property_dependencies

const MaterialPropertyName	_get_suffix

const bool	_use_interpolated_state

const InputParameters &	_ti_params

FEProblemBase &	_ti_feproblem

bool	_is_implicit

Real &	_t

const Real &	_t_old

int &	_t_step

Real &	_dt

Real &	_dt_old

bool	_is_transient

const Parallel::Communicator &	_communicator


Sampler &	_sampler

dof_id_type	_row
	During training loop, this is the row index of the data. More...

dof_id_type	_local_row
	During training loop, this is the local row index of the data. More...

const Real *	_rval
	Response value. More...

const std::vector< Real > *	_rvecval
	Vector response value. More...

std::vector< const Real * >	_pvals
	Predictor values from reporters. More...

std::vector< unsigned int >	_pcols
	Columns from sampler for predictors. More...

unsigned int	_n_dims
	Dimension of predictor data - either _sampler.getNumberOfCols() or _pvals.size() + _pcols.size(). More...

unsigned int &	_n_outputs
	The number of outputs. More...

Static Protected Attributes
static const std::string	_interpolated_old

static const std::string	_interpolated_older

Private Attributes
const std::vector< Real > &	_predictor_row
	Data from the current predictor row. More...

std::vector< Real >	_flattened_data
	The gathered data in a flattened form to be able to convert easily to torch::Tensor. More...

std::vector< Real >	_flattened_response
	The gathered response in a flattened form to be able to convert easily to torch::Tensor. More...

std::vector< unsigned int > &	_num_neurons_per_layer
	Number of neurons within the hidden layers (the length of this vector should be the same as _num_hidden_layers) More...

std::vector< std::string > &	_activation_function
	Activation functions for each hidden layer. More...

const std::string	_nn_filename
	Name of the pytorch output file. More...

const bool	_read_from_file
	Switch indicating if an already existing neural net should be read from a file or not. More...

Moose::LibtorchTrainingOptions	_optim_options
	The struct which contains the information for the training of the neural net. More...

std::shared_ptr< Moose::LibtorchArtificialNeuralNet > &	_nn
	Pointer to the neural net object (initialized as null) More...

const bool	_standardize_input
	If the training output should be standardized (scaled and shifted) More...

const bool	_standardize_output
	If the training output should be standardized (scaled and shifted) More...

StochasticTools::Standardizer &	_input_standardizer
	Standardizer for use with input (x) More...

StochasticTools::Standardizer &	_output_standardizer
	Standardizer for use with output response (y) More...

Detailed Description

Trainer responsible of fitting a neural network on predefined data.

Definition at line 23 of file LibtorchANNTrainer.h.

Constructor & Destructor Documentation

◆ LibtorchANNTrainer()

LibtorchANNTrainer::LibtorchANNTrainer ( const InputParameters & parameters )

Construct using input parameters.

Definition at line 66 of file LibtorchANNTrainer.C.

   : SurrogateTrainer(parameters),
     _predictor_row(getPredictorData()),
     _num_neurons_per_layer(declareModelData<std::vector<unsigned int>>(
         "num_neurons_per_layer", getParam<std::vector<unsigned int>>("num_neurons_per_layer"))),
     _activation_function(declareModelData<std::vector<std::string>>(
         "activation_function", getParam<std::vector<std::string>>("activation_function"))),
     _nn_filename(getParam<std::string>("nn_filename")),
     _read_from_file(getParam<bool>("read_from_file")),
     _nn(declareModelData<std::shared_ptr<Moose::LibtorchArtificialNeuralNet>>("nn")),
     _standardize_input(getParam<bool>("standardize_input")),
     _standardize_output(getParam<bool>("standardize_output")),
     _input_standardizer(declareModelData<StochasticTools::Standardizer>("input_standardizer")),
     _output_standardizer(declareModelData<StochasticTools::Standardizer>("output_standardizer"))
 {
   // Fixing the RNG seed to make sure every experiment is the same.
   // Otherwise sampling / stochastic gradient descent would be different.
   torch::manual_seed(getParam<unsigned int>("seed"));
 
   _optim_options.optimizer_type = "adam";
   _optim_options.learning_rate = getParam<Real>("learning_rate");
   _optim_options.num_epochs = getParam<unsigned int>("num_epochs");
   _optim_options.num_batches = getParam<unsigned int>("num_batches");
   _optim_options.rel_loss_tol = getParam<Real>("rel_loss_tol");
   _optim_options.print_loss = getParam<unsigned int>("print_epoch_loss") > 0;
   _optim_options.print_epoch_loss = getParam<unsigned int>("print_epoch_loss");
   _optim_options.parallel_processes = getParam<unsigned int>("max_processes");
 }

Member Function Documentation

◆ declareModelData()

template<typename T , typename... Args>

T & RestartableModelInterface::declareModelData	(	const std::string &	data_name,
		Args &&...	args
	)

inherited

Declare model data for loading from file as well as restart.

Definition at line 78 of file RestartableModelInterface.h.

 {
   return _model_restartable.declareRestartableData<T>(data_name, std::forward<Args>(args)...);
 }

◆ evaluateModelError()

std::vector< Real > SurrogateTrainer::evaluateModelError ( const SurrogateModel & surr )

protectedvirtualinherited

Definition at line 347 of file SurrogateTrainer.C.

Referenced by SurrogateTrainer::crossValidate().

 {
   std::vector<Real> error(1, 0.0);
 
   if (_rval)
   {
     Real model_eval = surr.evaluate(_predictor_data);
     error[0] = MathUtils::pow(model_eval - (*_rval), 2);
   }
   else if (_rvecval)
   {
     error.resize(_rvecval->size());
 
     // Evaluate for vector response.
     std::vector<Real> model_eval(error.size());
     surr.evaluate(_predictor_data, model_eval);
     for (auto r : make_range(_rvecval->size()))
       error[r] = MathUtils::pow(model_eval[r] - (*_rvecval)[r], 2);
   }
 
   return error;
 }

◆ execute()

void SurrogateTrainer::execute ( )

finalvirtualinherited

Implements GeneralUserObject.

Definition at line 176 of file SurrogateTrainer.C.

 {
   if (_doing_cv)
     for (const auto & trial : make_range(_cv_n_trials))
     {
       std::vector<Real> trial_score = crossValidate();
 
       // Expand _cv_trial_scores with more columns if necessary, then insert values.
       for (unsigned int r = _cv_trial_scores.size(); r < trial_score.size(); ++r)
         _cv_trial_scores.push_back(std::vector<Real>(_cv_n_trials, 0.0));
       for (auto r : make_range(trial_score.size()))
         _cv_trial_scores[r][trial] = trial_score[r];
     }
 
   _current_sample_size = _sampler.getNumberOfRows();
   _local_sample_size = _sampler.getNumberOfLocalRows();
   executeTraining();
 }

◆ finalize()

virtual void SurrogateTrainer::finalize ( )

inlinefinalvirtualinherited

Reimplemented from SurrogateTrainerBase.

Definition at line 63 of file SurrogateTrainer.h.

63 {}

◆ getCurrentSampleSize()

unsigned int SurrogateTrainer::getCurrentSampleSize ( ) const

inlineprotectedinherited

Definition at line 102 of file SurrogateTrainer.h.

Referenced by PolynomialChaosTrainer::postTrain(), and PolynomialChaosTrainer::preTrain().

102 { return _current_sample_size; };

SurrogateTrainer::_current_sample_size

unsigned int _current_sample_size

Number of samples used to train the model.

Definition: SurrogateTrainer.h:173

◆ getLocalSampleSize()

unsigned int SurrogateTrainer::getLocalSampleSize ( ) const

inlineprotectedinherited

Definition at line 107 of file SurrogateTrainer.h.

Referenced by NearestPointTrainer::preTrain(), GaussianProcessTrainer::preTrain(), and preTrain().

107 { return _local_sample_size; };

SurrogateTrainer::_local_sample_size

unsigned int _local_sample_size

Number of samples (locally) used to train the model.

Definition: SurrogateTrainer.h:176

◆ getModelData()

template<typename T , typename... Args>

const T & RestartableModelInterface::getModelData	(	const std::string &	data_name,
		Args &&...	args
	)		const

inherited

Retrieve model data from the interface.

Definition at line 85 of file RestartableModelInterface.h.

 {
   return _model_restartable.getRestartableData<T>(data_name, std::forward<Args>(args)...);
 }

◆ getModelDataFileName()

const FileName & RestartableModelInterface::getModelDataFileName ( ) const

inherited

Get the associated filename.

Definition at line 33 of file RestartableModelInterface.C.

 {
   return _model_object.getParam<FileName>("filename");
 }

◆ getPredictorData()

const std::vector<Real>& SurrogateTrainer::getPredictorData ( ) const

inlineprotectedinherited

Definition at line 97 of file SurrogateTrainer.h.

97 { return _predictor_data; };

SurrogateTrainer::_predictor_data

std::vector< Real > _predictor_data

Predictor data for current row - can be combination of Sampler and Reporter values.

Definition: SurrogateTrainer.h:164

◆ getSamplerData()

const std::vector<Real>& SurrogateTrainer::getSamplerData ( ) const

inlineprotectedinherited

Definition at line 92 of file SurrogateTrainer.h.

92 { return _row_data; };

SurrogateTrainer::_row_data

std::vector< Real > _row_data

Sampler data for the current row.

Definition: SurrogateTrainer.h:161

◆ getSurrogateModel() [1/2]

template<>

SurrogateModel& SurrogateModelInterface::getSurrogateModel ( const std::string & name ) const

inherited

Definition at line 46 of file SurrogateModelInterface.C.

 {
   return getSurrogateModelByName<SurrogateModel>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateModel() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateModel ( const std::string & name ) const

inherited

Get a SurrogateModel/Trainer with a given name.

Parameters

name	The name of the parameter key of the sampler to retrieve

Returns: The sampler with name associated with the parameter 'name'

Definition at line 81 of file SurrogateModelInterface.h.

Referenced by SurrogateTrainer::initialize().

 {
   return getSurrogateModelByName<T>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateModelByName() [1/2]

template<>

SurrogateModel& SurrogateModelInterface::getSurrogateModelByName ( const UserObjectName & name ) const

inherited

Definition at line 31 of file SurrogateModelInterface.C.

 {
   std::vector<SurrogateModel *> models;
   _smi_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("SurrogateModel")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a SurrogateModel object with the name '" + name + "'");
   return *(models[0]);
 }

◆ getSurrogateModelByName() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateModelByName ( const UserObjectName & name ) const

inherited

Get a sampler with a given name.

Parameters

name	The name of the sampler to retrieve

Returns: The sampler with name 'name'

Definition at line 88 of file SurrogateModelInterface.h.

Referenced by CrossValidationScores::CrossValidationScores(), EvaluateSurrogate::EvaluateSurrogate(), and InverseMapping::initialSetup().

 {
   std::vector<T *> models;
   _smi_feproblem.theWarehouse()
       .query()
       .condition<AttribName>(name)
       .condition<AttribSystem>("SurrogateModel")
       .queryInto(models);
   if (models.empty())
     mooseError("Unable to find a SurrogateModel object of type " + std::string(typeid(T).name()) +
                " with the name '" + name + "'");
   return *(models[0]);
 }

◆ getSurrogateTrainer() [1/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateTrainer ( const std::string & name ) const

inherited

Definition at line 104 of file SurrogateModelInterface.h.

 {
   return getSurrogateTrainerByName<T>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateTrainer() [2/2]

template<>

SurrogateTrainerBase& SurrogateModelInterface::getSurrogateTrainer ( const std::string & name ) const

inherited

Definition at line 60 of file SurrogateModelInterface.C.

 {
   return getSurrogateTrainerByName<SurrogateTrainerBase>(_smi_params.get<UserObjectName>(name));
 }

◆ getSurrogateTrainerByName() [1/2]

template<>

SurrogateTrainerBase& SurrogateModelInterface::getSurrogateTrainerByName ( const UserObjectName & name ) const

inherited

Definition at line 53 of file SurrogateModelInterface.C.

 {
   return _smi_feproblem.getUserObject<SurrogateTrainerBase>(name);
 }

◆ getSurrogateTrainerByName() [2/2]

template<typename T >

T & SurrogateModelInterface::getSurrogateTrainerByName ( const UserObjectName & name ) const

inherited

Definition at line 111 of file SurrogateModelInterface.h.

Referenced by SurrogateTrainerOutput::output().

 {
   SurrogateTrainerBase * base_ptr =
       &_smi_feproblem.getUserObject<SurrogateTrainerBase>(name, _smi_tid);
   T * obj_ptr = dynamic_cast<T *>(base_ptr);
   if (!obj_ptr)
     mooseError("Failed to find a SurrogateTrainer object of type " + std::string(typeid(T).name()) +
                    " with the name '",
                name,
                "' for the desired type.");
   return *obj_ptr;
 }

◆ getTrainingData()

template<typename T >

const T & SurrogateTrainer::getTrainingData ( const ReporterName & rname )

protectedinherited

Definition at line 208 of file SurrogateTrainer.h.

 {
   auto it = _training_data.find(rname);
   if (it != _training_data.end())
   {
     auto data = std::dynamic_pointer_cast<TrainingData<T>>(it->second);
     if (!data)
       mooseError("Reporter value ", rname, " already exists but is of different type.");
     return data->get();
   }
   else
   {
     const std::vector<T> & rval = getReporterValueByName<std::vector<T>>(rname);
     _training_data[rname] = std::make_shared<TrainingData<T>>(rval);
     return std::dynamic_pointer_cast<TrainingData<T>>(_training_data[rname])->get();
   }
 }

◆ hasModelData()

bool RestartableModelInterface::hasModelData ( ) const

inherited

Check if we need to load model data (if the filename parameter is used)

Definition at line 39 of file RestartableModelInterface.C.

 {
   return _model_object.isParamValid("filename");
 }

◆ initialize()

void SurrogateTrainer::initialize ( )

finalvirtualinherited

Reimplemented from SurrogateTrainerBase.

Definition at line 153 of file SurrogateTrainer.C.

 {
   // Figure out if data is distributed
   for (auto & pair : _training_data)
   {
     const ReporterName & name = pair.first;
     TrainingDataBase & data = *pair.second;
 
     const auto & mode = _fe_problem.getReporterData().getReporterMode(name);
     if (mode == REPORTER_MODE_DISTRIBUTED || (mode == REPORTER_MODE_ROOT && processor_id() != 0))
       data.isDistributed() = true;
     else if (mode == REPORTER_MODE_REPLICATED ||
              (mode == REPORTER_MODE_ROOT && processor_id() == 0))
       data.isDistributed() = false;
     else
       mooseError("Predictor reporter value ", name, " is not of supported mode.");
   }
 
   if (_doing_cv)
     _cv_surrogate = &getSurrogateModel("cv_surrogate");
 }

◆ modelMetaDataName()

const std::string& RestartableModelInterface::modelMetaDataName ( ) const

inlineinherited

Accessor for the name of the model meta data.

Definition at line 47 of file RestartableModelInterface.h.

Referenced by SurrogateTrainerOutput::output(), and MappingOutput::output().

47 { return _model_meta_data_name; }

RestartableModelInterface::_model_meta_data_name

const std::string _model_meta_data_name

The model meta data name.

Definition: RestartableModelInterface.h:61

◆ postTrain()

void LibtorchANNTrainer::postTrain ( )

overridevirtual

Contains processes which are executed after the training loop.

Reimplemented from SurrogateTrainer.

Definition at line 115 of file LibtorchANNTrainer.C.

 {
   _communicator.allgather(_flattened_data);
   _communicator.allgather(_flattened_response);
 
   // Then, we create and load our Tensors
   unsigned int num_samples = _flattened_response.size();
   unsigned int num_inputs = _n_dims;
 
   // We create a neural net (for the definition of the net see the header file)
   _nn = std::make_shared<Moose::LibtorchArtificialNeuralNet>(
       _nn_filename, num_inputs, 1, _num_neurons_per_layer, _activation_function);
 
   if (_read_from_file)
     try
     {
       torch::load(_nn, _nn_filename);
       _console << "Loaded requested .pt file." << std::endl;
     }
     catch (const c10::Error & e)
     {
       mooseError("The requested pytorch file could not be loaded.\n", e.msg());
     }
 
   // The default data type in pytorch is float, while we use double in MOOSE.
   // Therefore, in some cases we have to convert Tensors to double.
   auto options = torch::TensorOptions().dtype(at::kDouble);
   torch::Tensor data_tensor =
       torch::from_blob(_flattened_data.data(), {num_samples, num_inputs}, options).to(at::kDouble);
   torch::Tensor response_tensor =
       torch::from_blob(_flattened_response.data(), {num_samples, 1}, options).to(at::kDouble);
 
   // We standardize the input/output pairs if the user requested it
   if (_standardize_input)
   {
     auto data_std_mean = torch::std_mean(data_tensor, 0);
     auto & data_std = std::get<0>(data_std_mean);
     auto & data_mean = std::get<1>(data_std_mean);
 
     data_tensor = (data_tensor - data_mean) / data_std;
 
     std::vector<Real> converted_data_mean;
     LibtorchUtils::tensorToVector(data_mean, converted_data_mean);
     std::vector<Real> converted_data_std;
     LibtorchUtils::tensorToVector(data_std, converted_data_std);
     _input_standardizer.set(converted_data_mean, converted_data_std);
   }
   else
     _input_standardizer.set(_n_dims);
 
   if (_standardize_output)
   {
     auto response_std_mean = torch::std_mean(response_tensor, 0);
     auto & response_std = std::get<0>(response_std_mean);
     auto & response_mean = std::get<1>(response_std_mean);
 
     response_tensor = (response_tensor - response_mean) / response_std;
 
     std::vector<Real> converted_response_mean;
     LibtorchUtils::tensorToVector(response_mean, converted_response_mean);
     std::vector<Real> converted_response_std;
     LibtorchUtils::tensorToVector(response_std, converted_response_std);
     _output_standardizer.set(converted_response_mean, converted_response_std);
   }
   else
     _output_standardizer.set(1);
 
   // We create a custom data set from our converted data
   Moose::LibtorchDataset my_data(data_tensor, response_tensor);
 
   // We create atrainer for our neral net and train it with the dataset
   Moose::LibtorchArtificialNeuralNetTrainer<> trainer(*_nn, comm());
   trainer.train(my_data, _optim_options);
 }

◆ preTrain()

void LibtorchANNTrainer::preTrain ( )

overridevirtual

Contains processes which are executed before the training loop.

Reimplemented from SurrogateTrainer.

Definition at line 96 of file LibtorchANNTrainer.C.

 {
   // Resize to number of sample points
   _flattened_data.clear();
   _flattened_response.clear();
   _flattened_data.reserve(getLocalSampleSize() * _n_dims);
   _flattened_response.reserve(getLocalSampleSize());
 }

◆ threadJoin()

virtual void SurrogateTrainerBase::threadJoin ( const UserObject & )

inlinefinalvirtualinherited

Reimplemented from GeneralUserObject.

Definition at line 40 of file SurrogateTrainer.h.

40 {} // GeneralUserObjects are not threaded

◆ train()

void LibtorchANNTrainer::train ( )

overridevirtual

Contains processes which are executed for every sample in the training loop.

Reimplemented from SurrogateTrainer.

Definition at line 106 of file LibtorchANNTrainer.C.

 {
   for (auto & p : _predictor_row)
     _flattened_data.push_back(p);
 
   _flattened_response.push_back(*_rval);
 }

◆ validParams()

InputParameters LibtorchANNTrainer::validParams ( )

static

Definition at line 19 of file LibtorchANNTrainer.C.

 {
   InputParameters params = SurrogateTrainer::validParams();
 
   params.addClassDescription("Trains a simple neural network using libtorch.");
 
   params.addRangeCheckedParam<unsigned int>(
       "num_batches", 1, "1<=num_batches", "Number of batches.");
   params.addRangeCheckedParam<unsigned int>(
       "num_epochs", 1, "0<num_epochs", "Number of training epochs.");
   params.addRangeCheckedParam<Real>(
       "rel_loss_tol",
       0,
       "0<=rel_loss_tol<=1",
       "The relative loss where we stop the training of the neural net.");
   params.addParam<std::vector<unsigned int>>(
       "num_neurons_per_layer", std::vector<unsigned int>(), "Number of neurons per layer.");
   params.addParam<std::vector<std::string>>(
       "activation_function",
       std::vector<std::string>({"relu"}),
       "The type of activation functions to use. It is either one value "
       "or one value per hidden layer.");
   params.addParam<std::string>(
       "nn_filename", "net.pt", "Filename used to output the neural net parameters.");
   params.addParam<bool>("read_from_file",
                         false,
                         "Switch to allow reading old trained neural nets for further training.");
   params.addParam<Real>("learning_rate", 0.001, "Learning rate (relaxation).");
   params.addRangeCheckedParam<unsigned int>(
       "print_epoch_loss",
       0,
       "0<=print_epoch_loss",
       "Epoch training loss printing. 0 - no printing, 1 - every epoch, 10 - every 10th epoch.");
   params.addParam<unsigned int>(
       "seed", 11, "Random number generator seed for stochastic optimizers.");
   params.addParam<unsigned int>(
       "max_processes", 1, "The maximum number of parallel processes that the trainer will use.");
 
   params.addParam<bool>(
       "standardize_input", true, "Standardize (center and scale) training inputs (x values)");
   params.addParam<bool>(
       "standardize_output", true, "Standardize (center and scale) training outputs (y values)");
 
   params.suppressParameter<MooseEnum>("response_type");
   return params;
 }

Member Data Documentation

◆ _activation_function

std::vector<std::string>& LibtorchANNTrainer::_activation_function

private

Activation functions for each hidden layer.

Definition at line 55 of file LibtorchANNTrainer.h.

Referenced by postTrain().

◆ _flattened_data

std::vector<Real> LibtorchANNTrainer::_flattened_data

private

The gathered data in a flattened form to be able to convert easily to torch::Tensor.

Definition at line 45 of file LibtorchANNTrainer.h.

Referenced by postTrain(), preTrain(), and train().

◆ _flattened_response

std::vector<Real> LibtorchANNTrainer::_flattened_response

private

The gathered response in a flattened form to be able to convert easily to torch::Tensor.

Definition at line 48 of file LibtorchANNTrainer.h.

Referenced by postTrain(), preTrain(), and train().

◆ _input_standardizer

StochasticTools::Standardizer& LibtorchANNTrainer::_input_standardizer

private

Standardizer for use with input (x)

Definition at line 79 of file LibtorchANNTrainer.h.

Referenced by postTrain().

◆ _local_row

dof_id_type SurrogateTrainer::_local_row

protectedinherited

During training loop, this is the local row index of the data.

Definition at line 123 of file SurrogateTrainer.h.

Referenced by SurrogateTrainer::executeTraining().

◆ _n_dims

unsigned int SurrogateTrainer::_n_dims

protectedinherited

Dimension of predictor data - either _sampler.getNumberOfCols() or _pvals.size() + _pcols.size().

Definition at line 133 of file SurrogateTrainer.h.

Referenced by NearestPointTrainer::NearestPointTrainer(), GaussianProcessTrainer::postTrain(), postTrain(), preTrain(), SurrogateTrainer::SurrogateTrainer(), NearestPointTrainer::train(), and PolynomialRegressionTrainer::train().

◆ _n_outputs

unsigned int& SurrogateTrainer::_n_outputs

protectedinherited

The number of outputs.

Definition at line 135 of file SurrogateTrainer.h.

Referenced by GaussianProcessTrainer::GaussianProcessTrainer(), GaussianProcessTrainer::postTrain(), and GaussianProcessTrainer::train().

◆ _nn

std::shared_ptr<Moose::LibtorchArtificialNeuralNet>& LibtorchANNTrainer::_nn

private

Pointer to the neural net object (initialized as null)

Definition at line 70 of file LibtorchANNTrainer.h.

Referenced by postTrain().

◆ _nn_filename

const std::string LibtorchANNTrainer::_nn_filename

private

Name of the pytorch output file.

This is used for loading and storing already existing data.

Definition at line 59 of file LibtorchANNTrainer.h.

Referenced by postTrain().

◆ _num_neurons_per_layer

std::vector<unsigned int>& LibtorchANNTrainer::_num_neurons_per_layer

private

Number of neurons within the hidden layers (the length of this vector should be the same as _num_hidden_layers)

Definition at line 52 of file LibtorchANNTrainer.h.

Referenced by postTrain().

◆ _optim_options

Moose::LibtorchTrainingOptions LibtorchANNTrainer::_optim_options

private

The struct which contains the information for the training of the neural net.

Definition at line 67 of file LibtorchANNTrainer.h.

Referenced by LibtorchANNTrainer(), and postTrain().

◆ _output_standardizer

StochasticTools::Standardizer& LibtorchANNTrainer::_output_standardizer

private

Standardizer for use with output response (y)

Definition at line 82 of file LibtorchANNTrainer.h.

Referenced by postTrain().

◆ _pcols

std::vector<unsigned int> SurrogateTrainer::_pcols

protectedinherited

Columns from sampler for predictors.

Definition at line 131 of file SurrogateTrainer.h.

Referenced by PolynomialChaosTrainer::PolynomialChaosTrainer(), SurrogateTrainer::SurrogateTrainer(), and SurrogateTrainer::updatePredictorRow().

◆ _predictor_row

const std::vector<Real>& LibtorchANNTrainer::_predictor_row

private

Data from the current predictor row.

Definition at line 42 of file LibtorchANNTrainer.h.

Referenced by train().

◆ _pvals

std::vector<const Real *> SurrogateTrainer::_pvals

protectedinherited

Predictor values from reporters.

Definition at line 129 of file SurrogateTrainer.h.

Referenced by PolynomialChaosTrainer::PolynomialChaosTrainer(), SurrogateTrainer::SurrogateTrainer(), and SurrogateTrainer::updatePredictorRow().

◆ _read_from_file

const bool LibtorchANNTrainer::_read_from_file

private

Switch indicating if an already existing neural net should be read from a file or not.

This can be used to load existing torch files (from previous MOOSE or python runs for retraining and further manipulation)

Definition at line 64 of file LibtorchANNTrainer.h.

Referenced by postTrain().

◆ _row

dof_id_type SurrogateTrainer::_row

protectedinherited

During training loop, this is the row index of the data.

Definition at line 121 of file SurrogateTrainer.h.

Referenced by SurrogateTrainer::executeTraining(), and PolynomialChaosTrainer::train().

◆ _rval

const Real* SurrogateTrainer::_rval

protectedinherited

Response value.

Definition at line 125 of file SurrogateTrainer.h.

Referenced by SurrogateTrainer::evaluateModelError(), SurrogateTrainer::SurrogateTrainer(), NearestPointTrainer::train(), PolynomialRegressionTrainer::train(), GaussianProcessTrainer::train(), and train().

◆ _rvecval

const std::vector<Real>* SurrogateTrainer::_rvecval

protectedinherited

Vector response value.

Definition at line 127 of file SurrogateTrainer.h.

Referenced by SurrogateTrainer::evaluateModelError(), SurrogateTrainer::SurrogateTrainer(), NearestPointTrainer::train(), PolynomialRegressionTrainer::train(), and GaussianProcessTrainer::train().

◆ _sampler

Sampler& SurrogateTrainer::_sampler

protectedinherited

Sampler being used for training

Definition at line 119 of file SurrogateTrainer.h.

Referenced by SurrogateTrainer::checkIntegrity(), SurrogateTrainer::crossValidate(), SurrogateTrainer::execute(), SurrogateTrainer::executeTraining(), GaussianProcessTrainer::GaussianProcessTrainer(), PolynomialChaosTrainer::PolynomialChaosTrainer(), PolynomialRegressionTrainer::PolynomialRegressionTrainer(), and SurrogateTrainer::SurrogateTrainer().

◆ _standardize_input

const bool LibtorchANNTrainer::_standardize_input

private

If the training output should be standardized (scaled and shifted)

Definition at line 73 of file LibtorchANNTrainer.h.

Referenced by postTrain().

◆ _standardize_output

const bool LibtorchANNTrainer::_standardize_output

private

If the training output should be standardized (scaled and shifted)

Definition at line 76 of file LibtorchANNTrainer.h.

Referenced by postTrain().

The documentation for this class was generated from the following files:

stochastic_tools/include/libtorch/surrogates/LibtorchANNTrainer.h
stochastic_tools/src/libtorch/trainers/LibtorchANNTrainer.C

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Static Public Attributes

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Static Protected Attributes

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ LibtorchANNTrainer()

Member Function Documentation

◆ declareModelData()

◆ evaluateModelError()

◆ execute()

◆ finalize()

◆ getCurrentSampleSize()

◆ getLocalSampleSize()

◆ getModelData()

◆ getModelDataFileName()

◆ getPredictorData()

◆ getSamplerData()

◆ getSurrogateModel() [1/2]

◆ getSurrogateModel() [2/2]

◆ getSurrogateModelByName() [1/2]

◆ getSurrogateModelByName() [2/2]

◆ getSurrogateTrainer() [1/2]

◆ getSurrogateTrainer() [2/2]

◆ getSurrogateTrainerByName() [1/2]

◆ getSurrogateTrainerByName() [2/2]

◆ getTrainingData()

◆ hasModelData()

◆ initialize()

◆ modelMetaDataName()

◆ postTrain()

◆ preTrain()

◆ threadJoin()

◆ train()

◆ validParams()

Member Data Documentation

◆ _activation_function

◆ _flattened_data

◆ _flattened_response

◆ _input_standardizer

◆ _local_row

◆ _n_dims

◆ _n_outputs

◆ _nn

◆ _nn_filename

◆ _num_neurons_per_layer

◆ _optim_options

◆ _output_standardizer

◆ _pcols

◆ _predictor_row

◆ _pvals

◆ _read_from_file

◆ _row

◆ _rval

◆ _rvecval

◆ _sampler

◆ _standardize_input

◆ _standardize_output