#include <optimization_algorithm.h>

Inheritance diagram for OptimizationAlgorithm:

Public Types
enum class	StoppingCondition { MinimumLossDecrease , LossGoal , MaximumSelectionErrorIncreases , MaximumEpochsNumber , MaximumTime }
	Enumeration of all possibles condition of stop for the algorithms. More...

Public Member Functions
	OptimizationAlgorithm ()

	OptimizationAlgorithm (LossIndex *)

virtual	~OptimizationAlgorithm ()
	Destructor. More...

LossIndex *	get_loss_index_pointer () const

string	get_hardware_use () const
	Hardware use. More...

void	set_hardware_use (const string &)
	Set hardware to use. Default: Multi-core. More...

bool	has_loss_index () const

const bool &	get_display () const

const Index &	get_display_period () const
	Returns the number of iterations between the training showing progress. More...

const Index &	get_save_period () const
	Returns the number of iterations between the training saving progress. More...

const string &	get_neural_network_file_name () const
	Returns the file name where the neural network will be saved. More...

const string	write_time (const type &) const
	Writes the time from seconds in format HH:mm:ss. More...

void	set ()

virtual void	set_default ()
	Sets the members of the optimization algorithm object to their default values. More...

virtual void	set_threads_number (const int &)

virtual void	set_loss_index_pointer (LossIndex *)

virtual void	set_display (const bool &)

void	set_display_period (const Index &)

void	set_save_period (const Index &)

void	set_neural_network_file_name (const string &)

virtual void	check () const

virtual TrainingResults	perform_training ()=0
	Trains a neural network which has a loss index associated. More...

virtual string	write_optimization_algorithm_type () const

virtual void	print () const
	Prints to the screen the XML-type representation of the optimization algorithm object. More...

virtual Tensor< string, 2 >	to_string_matrix () const

virtual void	from_XML (const tinyxml2::XMLDocument &)

virtual void	write_XML (tinyxml2::XMLPrinter &) const

void	save (const string &) const

void	load (const string &)

Protected Attributes
NonBlockingThreadPool *	non_blocking_thread_pool = nullptr

ThreadPoolDevice *	thread_pool_device

LossIndex *	loss_index_pointer = nullptr
	Pointer to a loss index for a neural network object. More...

Index	epochs_number = 10000
	Number of training epochs in the neural network. More...

string	hardware_use = "Multi-core"
	Hardware use. More...

Index	display_period = 10
	Number of iterations between the training showing progress. More...

Index	save_period = numeric_limits<Index>::max()
	Number of iterations between the training saving progress. More...

string	neural_network_file_name = "neural_network.xml"
	Path where the neural network is saved. More...

bool	display = true
	Display messages to screen. More...

const Eigen::array< IndexPair< Index >, 1 >	AT_B = {IndexPair<Index>(0, 0)}

const Eigen::array< IndexPair< Index >, 1 >	product_vector_matrix = {IndexPair<Index>(0, 1)}

const Eigen::array< IndexPair< Index >, 1 >	A_B = {IndexPair<Index>(1, 0)}

Detailed Description

This abstract class represents the concept of optimization algorithm for a neural network in OpenNN library. Any derived class must implement the perform_training() method.

Definition at line 41 of file optimization_algorithm.h.

Member Enumeration Documentation

◆ StoppingCondition

enum class StoppingCondition

strong

Enumeration of all possibles condition of stop for the algorithms.

Definition at line 54 of file optimization_algorithm.h.

Constructor & Destructor Documentation

◆ OptimizationAlgorithm() [1/2]

OptimizationAlgorithm ( )

explicit

Default constructor. It creates a optimization algorithm object not associated to any loss index object.

Definition at line 17 of file optimization_algorithm.cpp.

◆ OptimizationAlgorithm() [2/2]

OptimizationAlgorithm ( LossIndex * new_loss_index_pointer )

explicit

It creates a optimization algorithm object associated to a loss index object.

Parameters

new_loss_index_pointer Pointer to a loss index object.

Definition at line 31 of file optimization_algorithm.cpp.

◆ ~OptimizationAlgorithm()

~OptimizationAlgorithm ( )

virtual

Destructor.

Definition at line 44 of file optimization_algorithm.cpp.

Member Function Documentation

◆ check()

void check ( ) const

virtual

Performs a default checking for optimization algorithms. In particular, it checks that the loss index pointer associated to the optimization algorithm is not nullptr, and that the neural network associated to that loss index is neither nullptr. If that checkings are not hold, an exception is thrown.

Reimplemented in LevenbergMarquardtAlgorithm.

Definition at line 260 of file optimization_algorithm.cpp.

◆ from_XML()

void from_XML ( const tinyxml2::XMLDocument & document )

virtual

Loads a default optimization algorithm from a XML document.

Parameters

document TinyXML document containing the error term members.

Reimplemented in AdaptiveMomentEstimation, ConjugateGradient, GradientDescent, LevenbergMarquardtAlgorithm, QuasiNewtonMethod, and StochasticGradientDescent.

Definition at line 317 of file optimization_algorithm.cpp.

◆ get_display()

const bool & get_display ( ) const

Returns true if messages from this class can be displayed on the screen, or false if messages from this class can't be displayed on the screen.

Definition at line 110 of file optimization_algorithm.cpp.

◆ get_display_period()

const Index & get_display_period ( ) const

Returns the number of iterations between the training showing progress.

Definition at line 118 of file optimization_algorithm.cpp.

◆ get_hardware_use()

string get_hardware_use ( ) const

Hardware use.

Returns the hardware used. Default: Multi-core.

Definition at line 77 of file optimization_algorithm.cpp.

◆ get_loss_index_pointer()

LossIndex * get_loss_index_pointer ( ) const

Returns a pointer to the loss index object to which the optimization algorithm is associated.

Definition at line 54 of file optimization_algorithm.cpp.

◆ get_neural_network_file_name()

const string & get_neural_network_file_name ( ) const

Returns the file name where the neural network will be saved.

Definition at line 134 of file optimization_algorithm.cpp.

◆ get_save_period()

const Index & get_save_period ( ) const

Returns the number of iterations between the training saving progress.

Definition at line 126 of file optimization_algorithm.cpp.

◆ has_loss_index()

bool has_loss_index ( ) const

Returns true if this optimization algorithm object has an associated loss index object, and false otherwise.

Definition at line 94 of file optimization_algorithm.cpp.

◆ load()

void load ( const string & file_name )

Loads a gradient descent object from a XML-type file. Please mind about the file format, wich is specified in the User's Guide.

Parameters

file_name Name of optimization algorithm XML-type file.

Definition at line 388 of file optimization_algorithm.cpp.

◆ perform_training()

virtual TrainingResults perform_training ( )

pure virtual

Trains a neural network which has a loss index associated.

Implemented in AdaptiveMomentEstimation, ConjugateGradient, GradientDescent, LevenbergMarquardtAlgorithm, QuasiNewtonMethod, and StochasticGradientDescent.

◆ print()

void print ( ) const

virtual

Prints to the screen the XML-type representation of the optimization algorithm object.

Definition at line 364 of file optimization_algorithm.cpp.

◆ save()

void save ( const string & file_name ) const

Saves to a XML-type file the members of the optimization algorithm object.

Parameters

file_name Name of optimization algorithm XML-type file.

Definition at line 372 of file optimization_algorithm.cpp.

◆ set()

void set ( )

Sets the loss index pointer to nullptr. It also sets the rest of members to their default values.

Definition at line 143 of file optimization_algorithm.cpp.

◆ set_default()

void set_default ( )

virtual

Sets the members of the optimization algorithm object to their default values.

Reimplemented in AdaptiveMomentEstimation, ConjugateGradient, GradientDescent, LevenbergMarquardtAlgorithm, QuasiNewtonMethod, and StochasticGradientDescent.

Definition at line 243 of file optimization_algorithm.cpp.

◆ set_display()

void set_display ( const bool & new_display )

virtual

Sets a new display value. If it is set to true messages from this class are to be displayed on the screen; if it is set to false messages from this class are not to be displayed on the screen.

Parameters

new_display Display value.

Reimplemented in QuasiNewtonMethod.

Definition at line 175 of file optimization_algorithm.cpp.

◆ set_display_period()

void set_display_period ( const Index & new_display_period )

Sets a new number of iterations between the training showing progress.

Parameters

new_display_period Number of iterations between the training showing progress.

Definition at line 185 of file optimization_algorithm.cpp.

◆ set_hardware_use()

void set_hardware_use ( const string & new_hardware_use )

Set hardware to use. Default: Multi-core.

Definition at line 85 of file optimization_algorithm.cpp.

◆ set_loss_index_pointer()

void set_loss_index_pointer ( LossIndex * new_loss_index_pointer )

virtual

Sets a pointer to a loss index object to be associated to the optimization algorithm.

Parameters

new_loss_index_pointer Pointer to a loss index object.

Reimplemented in AdaptiveMomentEstimation, ConjugateGradient, GradientDescent, QuasiNewtonMethod, and StochasticGradientDescent.

Definition at line 164 of file optimization_algorithm.cpp.

◆ set_neural_network_file_name()

void set_neural_network_file_name ( const string & new_neural_network_file_name )

Sets a new file name where the neural network will be saved.

Parameters

new_neural_network_file_name File name for the neural network object.

Definition at line 235 of file optimization_algorithm.cpp.

◆ set_save_period()

void set_save_period ( const Index & new_save_period )

Sets a new number of iterations between the training saving progress.

Parameters

new_save_period Number of iterations between the training saving progress.

Definition at line 210 of file optimization_algorithm.cpp.

◆ set_threads_number()

void set_threads_number ( const int & new_threads_number )

virtual

Definition at line 151 of file optimization_algorithm.cpp.

◆ to_string_matrix()

Tensor< string, 2 > to_string_matrix ( ) const

virtual

Returns a default(empty) string matrix containing the members of the optimization algorithm object.

Reimplemented in AdaptiveMomentEstimation, ConjugateGradient, GradientDescent, LevenbergMarquardtAlgorithm, QuasiNewtonMethod, and StochasticGradientDescent.

Definition at line 356 of file optimization_algorithm.cpp.

◆ write_optimization_algorithm_type()

virtual string write_optimization_algorithm_type ( ) const

inlinevirtual

Reimplemented in AdaptiveMomentEstimation, ConjugateGradient, and LevenbergMarquardtAlgorithm.

Definition at line 106 of file optimization_algorithm.h.

◆ write_time()

const string write_time ( const type & time ) const

Writes the time from seconds in format HH:mm:ss.

Definition at line 483 of file optimization_algorithm.cpp.

◆ write_XML()

void write_XML ( tinyxml2::XMLPrinter & file_stream ) const

virtual

Serializes the optimization algorithm object into a XML document of the TinyXML library without keep the DOM tree in memory. See the OpenNN manual for more information about the format of this document.

Reimplemented in AdaptiveMomentEstimation, ConjugateGradient, GradientDescent, LevenbergMarquardtAlgorithm, QuasiNewtonMethod, and StochasticGradientDescent.

Definition at line 293 of file optimization_algorithm.cpp.

Member Data Documentation

◆ A_B

const Eigen::array<IndexPair<Index>, 1> A_B = {IndexPair<Index>(1, 0)}

protected

Definition at line 158 of file optimization_algorithm.h.

◆ AT_B

const Eigen::array<IndexPair<Index>, 1> AT_B = {IndexPair<Index>(0, 0)}

protected

Definition at line 156 of file optimization_algorithm.h.

◆ display

bool display = true

protected

Display messages to screen.

Definition at line 154 of file optimization_algorithm.h.

◆ display_period

Index display_period = 10

protected

Number of iterations between the training showing progress.

Definition at line 142 of file optimization_algorithm.h.

◆ epochs_number

Index epochs_number = 10000

protected

Number of training epochs in the neural network.

Definition at line 132 of file optimization_algorithm.h.

◆ hardware_use

string hardware_use = "Multi-core"

protected

Hardware use.

Definition at line 138 of file optimization_algorithm.h.

◆ loss_index_pointer

LossIndex* loss_index_pointer = nullptr

protected

Pointer to a loss index for a neural network object.

Definition at line 128 of file optimization_algorithm.h.

◆ neural_network_file_name

string neural_network_file_name = "neural_network.xml"

protected

Path where the neural network is saved.

Definition at line 150 of file optimization_algorithm.h.

◆ non_blocking_thread_pool

NonBlockingThreadPool* non_blocking_thread_pool = nullptr

protected

Definition at line 123 of file optimization_algorithm.h.

◆ product_vector_matrix

const Eigen::array<IndexPair<Index>, 1> product_vector_matrix = {IndexPair<Index>(0, 1)}

protected

Definition at line 157 of file optimization_algorithm.h.

◆ save_period

Index save_period = numeric_limits<Index>::max()

protected

Number of iterations between the training saving progress.

Definition at line 146 of file optimization_algorithm.h.

◆ thread_pool_device

ThreadPoolDevice* thread_pool_device

protected

Definition at line 124 of file optimization_algorithm.h.

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

Public Types

Public Member Functions

Protected Attributes

Detailed Description

Member Enumeration Documentation

◆ StoppingCondition

Constructor & Destructor Documentation

◆ OptimizationAlgorithm() [1/2]

◆ OptimizationAlgorithm() [2/2]

◆ ~OptimizationAlgorithm()

Member Function Documentation

◆ check()

◆ from_XML()

◆ get_display()

◆ get_display_period()

◆ get_hardware_use()

◆ get_loss_index_pointer()

◆ get_neural_network_file_name()

◆ get_save_period()

◆ has_loss_index()

◆ load()

◆ perform_training()

◆ print()

◆ save()

◆ set()

◆ set_default()

◆ set_display()

◆ set_display_period()

◆ set_hardware_use()

◆ set_loss_index_pointer()

◆ set_neural_network_file_name()

◆ set_save_period()

◆ set_threads_number()

◆ to_string_matrix()

◆ write_optimization_algorithm_type()

◆ write_time()

◆ write_XML()

Member Data Documentation

◆ A_B

◆ AT_B

◆ display

◆ display_period

◆ epochs_number

◆ hardware_use

◆ loss_index_pointer

◆ neural_network_file_name

◆ non_blocking_thread_pool

◆ product_vector_matrix

◆ save_period

◆ thread_pool_device