Objective function for supervised learning. More...

#include <shark/ObjectiveFunctions/ErrorFunction.h>

Inheritance diagram for shark::ErrorFunction:

Public Member Functions
template<class InputType , class LabelType , class OutputType >
	ErrorFunction (LabeledData< InputType, LabelType > const &dataset, AbstractModel< InputType, OutputType > model, AbstractLoss< LabelType, OutputType > loss, bool useMiniBatches=false)

template<class InputType , class LabelType , class OutputType >
	ErrorFunction (WeightedLabeledData< InputType, LabelType > const &dataset, AbstractModel< InputType, OutputType > model, AbstractLoss< LabelType, OutputType > loss)

	ErrorFunction (const ErrorFunction &op)

ErrorFunction &	operator= (const ErrorFunction &op)

std::string	name () const
	returns the name of the object More...

void	setRegularizer (double factor, SingleObjectiveFunction *regularizer)

SearchPointType	proposeStartingPoint () const
	Proposes a starting point in the feasible search space of the function. More...

std::size_t	numberOfVariables () const
	Accesses the number of variables. More...

void	init ()

double	eval (RealVector const &input) const

ResultType	evalDerivative (const SearchPointType &input, FirstOrderDerivative &derivative) const
	Evaluates the objective function and calculates its gradient. More...

Public Member Functions inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
const Features &	features () const

virtual void	updateFeatures ()

bool	hasValue () const
	returns whether this function can calculate it's function value More...

bool	hasFirstDerivative () const
	returns whether this function can calculate the first derivative More...

bool	hasSecondDerivative () const
	returns whether this function can calculate the second derivative More...

bool	canProposeStartingPoint () const
	returns whether this function can propose a starting point. More...

bool	isConstrained () const
	returns whether this function can return More...

bool	hasConstraintHandler () const
	returns whether this function can return More...

bool	canProvideClosestFeasible () const
	Returns whether this function can calculate thee closest feasible to an infeasible point. More...

bool	isThreadSafe () const
	Returns true, when the function can be usd in parallel threads. More...

bool	isNoisy () const
	Returns true, when the function can be usd in parallel threads. More...

	AbstractObjectiveFunction ()
	Default ctor. More...

virtual	~AbstractObjectiveFunction ()
	Virtual destructor. More...

void	setRng (random::rng_type *rng)
	Sets the Rng used by the objective function. More...

virtual bool	hasScalableDimensionality () const

virtual void	setNumberOfVariables (std::size_t numberOfVariables)
	Adjusts the number of variables if the function is scalable. More...

virtual std::size_t	numberOfObjectives () const

virtual bool	hasScalableObjectives () const

virtual void	setNumberOfObjectives (std::size_t numberOfObjectives)
	Adjusts the number of objectives if the function is scalable. More...

std::size_t	evaluationCounter () const
	Accesses the evaluation counter of the function. More...

AbstractConstraintHandler< SearchPointType > const &	getConstraintHandler () const
	Returns the constraint handler of the function if it has one. More...

virtual bool	isFeasible (const SearchPointType &input) const
	Tests whether a point in SearchSpace is feasible, e.g., whether the constraints are fulfilled. More...

virtual void	closestFeasible (SearchPointType &input) const
	If supported, the supplied point is repaired such that it satisfies all of the function's constraints. More...

virtual ResultType	eval (SearchPointType const &input) const
	Evaluates the objective function for the supplied argument. More...

ResultType	operator() (SearchPointType const &input) const
	Evaluates the function. Useful together with STL-Algorithms like std::transform. More...

virtual ResultType	evalDerivative (SearchPointType const &input, SecondOrderDerivative &derivative) const
	Evaluates the objective function and calculates its gradient. More...

Public Member Functions inherited from shark::INameable
virtual	~INameable ()

Friends
void	swap (ErrorFunction &op1, ErrorFunction &op2)

Additional Inherited Members
Public Types inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
enum	Feature { HAS_VALUE = 1, HAS_FIRST_DERIVATIVE = 2, HAS_SECOND_DERIVATIVE = 4, CAN_PROPOSE_STARTING_POINT = 8, IS_CONSTRAINED_FEATURE = 16, HAS_CONSTRAINT_HANDLER = 32, CAN_PROVIDE_CLOSEST_FEASIBLE = 64, IS_THREAD_SAFE = 128, IS_NOISY = 256 }
	List of features that are supported by an implementation. More...

typedef PointType	SearchPointType

typedef ResultT	ResultType

typedef boost::mpl::if_< std::is_arithmetic< ResultT >, SearchPointType, RealMatrix >::type	FirstOrderDerivative

typedef TypedFlags< Feature >	Features
	This statement declares the member m_features. See Core/Flags.h for details. More...

typedef TypedFeatureNotAvailableException< Feature >	FeatureNotAvailableException

Protected Member Functions inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
void	announceConstraintHandler (AbstractConstraintHandler< SearchPointType > const *handler)
	helper function which is called to announce the presence of an constraint handler. More...

Protected Attributes inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
Features	m_features

std::size_t	m_evaluationCounter
	Evaluation counter, default value: 0. More...

AbstractConstraintHandler< SearchPointType > const *	m_constraintHandler

random::rng_type *	mep_rng

Detailed Description

Objective function for supervised learning.

: An ErrorFunction object is an objective function for learning the parameters of a model from data by means of minimization of a cost function. The value of the objective function is the cost of the model predictions on the training data, given the targets.

: It supports mini-batch learning using an optional fourth argument to The constructor. With mini-batch learning enabled, each iteration a random batch is taken from the dataset. Thus the size of the minibatch is the size of the batches in the datasets. Normalization ensures that batches of different sizes have approximately the same magnitude of error and derivative.

: It automatically infers the input und label type from the given dataset and the output type of the model in the constructor and ensures that Model and loss match. Thus the user does not need to provide the types as template parameters.

Definition at line 67 of file ErrorFunction.h.

Constructor & Destructor Documentation

◆ ErrorFunction() [1/3]

template<class InputType , class LabelType , class OutputType >

shark::ErrorFunction::ErrorFunction	(	LabeledData< InputType, LabelType > const &	dataset,
		AbstractModel< InputType, OutputType > *	model,
		AbstractLoss< LabelType, OutputType > *	loss,
		bool	useMiniBatches = `false`
	)

◆ ErrorFunction() [2/3]

template<class InputType , class LabelType , class OutputType >

shark::ErrorFunction::ErrorFunction	(	WeightedLabeledData< InputType, LabelType > const &	dataset,
		AbstractModel< InputType, OutputType > *	model,
		AbstractLoss< LabelType, OutputType > *	loss
	)

◆ ErrorFunction() [3/3]

shark::ErrorFunction::ErrorFunction ( const ErrorFunction & op )

Member Function Documentation

◆ eval()

double shark::ErrorFunction::eval ( RealVector const & input ) const

Referenced by init().

◆ evalDerivative()

ResultType shark::ErrorFunction::evalDerivative	(	const SearchPointType &	input,
		FirstOrderDerivative &	derivative
	)		const

virtual

Evaluates the objective function and calculates its gradient.

Parameters

[in]	input	The argument to eval the function for.
[out]	derivative	The derivate is placed here.

Returns: The result of evaluating the function for the supplied argument.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Referenced by init().

◆ init()

void shark::ErrorFunction::init ( )

inlinevirtual

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 101 of file ErrorFunction.h.

References eval(), evalDerivative(), shark::AbstractObjectiveFunction< PointType, ResultT >::mep_rng, and swap.

Referenced by main(), shark::OptimizationTrainer< Model, LabelTypeT >::train(), trainAutoencoderModel(), and trainProblem().

◆ name()

std::string shark::ErrorFunction::name ( ) const

inlinevirtual

returns the name of the object

Reimplemented from shark::INameable.

Definition at line 86 of file ErrorFunction.h.

◆ numberOfVariables()

std::size_t shark::ErrorFunction::numberOfVariables ( ) const

inlinevirtual

Accesses the number of variables.

Implements shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 97 of file ErrorFunction.h.

Referenced by main(), and trainAutoencoderModel().

◆ operator=()

ErrorFunction& shark::ErrorFunction::operator= ( const ErrorFunction & op )

◆ proposeStartingPoint()

SearchPointType shark::ErrorFunction::proposeStartingPoint ( ) const

inlinevirtual

Proposes a starting point in the feasible search space of the function.

Returns: The generated starting point.

Exceptions

FeatureNotAvailableException in the default implementation and if a function does not support this feature.

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 94 of file ErrorFunction.h.

◆ setRegularizer()

void shark::ErrorFunction::setRegularizer	(	double	factor,
		SingleObjectiveFunction *	regularizer
	)

inline

Definition at line 89 of file ErrorFunction.h.

Referenced by main(), trainAutoencoderModel(), and trainProblem().

Friends And Related Function Documentation

◆ swap

void swap	(	ErrorFunction &	op1,
		ErrorFunction &	op2
	)

friend

Referenced by init().

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

include/shark/ObjectiveFunctions/ErrorFunction.h

Public Member Functions

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ ErrorFunction() [1/3]

◆ ErrorFunction() [2/3]

◆ ErrorFunction() [3/3]

Member Function Documentation

◆ eval()

◆ evalDerivative()

◆ init()

◆ name()

◆ numberOfVariables()

◆ operator=()

◆ proposeStartingPoint()

◆ setRegularizer()

Friends And Related Function Documentation

◆ swap