This concrete class represents an incremental algorithm for the NeuronsSelection as part of the ModelSelection[1] class. More...

#include <incremental_neurons.h>

Inheritance diagram for IncrementalNeurons:

Classes
struct	IncrementalNeuronsResults
	This structure contains the training results for the incremental order method. More...

Public Member Functions
	IncrementalNeurons ()
	Default constructor.

	IncrementalNeurons (TrainingStrategy *)

	IncrementalNeurons (const tinyxml2::XMLDocument &)

	IncrementalNeurons (const string &)

virtual	~IncrementalNeurons ()
	Destructor.

const size_t &	get_step () const
	Returns the number of the hidden perceptrons pointed in each iteration of the Incremental algorithm.

const size_t &	get_maximum_selection_failures () const
	Returns the maximum number of selection failures in the model order selection algorithm.

void	set_default ()
	Sets the members of the model selection object to their default values:

void	set_step (const size_t &)

void	set_maximum_selection_failures (const size_t &)

IncrementalNeuronsResults *	perform_neurons_selection ()
	Perform the neurons selection with the Incremental method.

Matrix< string >	to_string_matrix () const
	Writes as matrix of strings the most representative atributes.

tinyxml2::XMLDocument *	to_XML () const

void	from_XML (const tinyxml2::XMLDocument &)

void	write_XML (tinyxml2::XMLPrinter &) const

void	save (const string &) const

void	load (const string &)

Public Member Functions inherited from NeuronsSelection
	NeuronsSelection ()
	Default constructor.

	NeuronsSelection (TrainingStrategy *)

	NeuronsSelection (const string &)

	NeuronsSelection (const tinyxml2::XMLDocument &)

virtual	~NeuronsSelection ()
	Destructor.

TrainingStrategy *	get_training_strategy_pointer () const
	Returns a pointer to the training strategy object.

bool	has_training_strategy () const
	Returns true if this order selection algorithm has a training strategy associated, and false otherwise.

const size_t &	get_maximum_order () const
	Returns the maximum of the hidden perceptrons number used in the order order selection.

const size_t &	get_minimum_order () const
	Returns the minimum of the hidden perceptrons number used in the order selection.

const size_t &	get_trials_number () const
	Returns the number of trials for each network architecture.

const bool &	get_reserve_error_data () const
	Returns true if the loss index losses are to be reserved, and false otherwise.

const bool &	get_reserve_selection_error_data () const
	Returns true if the loss index selection losses are to be reserved, and false otherwise.

const bool &	get_reserve_minimal_parameters () const
	Returns true if the parameters vector of the neural network with minimum selection error is to be reserved, and false otherwise.

const bool &	get_display () const

const double &	get_selection_error_goal () const
	Returns the goal for the selection error in the order selection algorithm.

const size_t &	get_maximum_iterations_number () const
	Returns the maximum number of iterations in the order selection algorithm.

const double &	get_maximum_time () const
	Returns the maximum time in the order selection algorithm.

const double &	get_tolerance () const
	Return the tolerance of error for the order selection algorithm.

void	set_training_strategy_pointer (TrainingStrategy *)

void	set_default ()
	Sets the members of the order selection object to their default values.

void	set_maximum_order (const size_t &)

void	set_minimum_order (const size_t &)

void	set_trials_number (const size_t &)

void	set_reserve_error_data (const bool &)

void	set_reserve_selection_error_data (const bool &)

void	set_reserve_minimal_parameters (const bool &)

void	set_display (const bool &)

void	set_selection_error_goal (const double &)

void	set_maximum_iterations_number (const size_t &)

void	set_maximum_time (const double &)

void	set_tolerance (const double &)

Vector< double >	calculate_losses (const size_t &, NeuralNetwork &)

string	write_stopping_condition (const OptimizationAlgorithm::Results &) const

void	delete_selection_history ()
	Delete the history of the selection error values.

void	delete_training_loss_history ()
	Delete the history of the loss values.

void	check () const
	Checks that the different pointers needed for performing the order selection are not nullptr.

Private Attributes
size_t	step
	Number of neurons added at each iteration.

size_t	maximum_selection_failures
	Maximum number of iterations at which the selection error increases.

Additional Inherited Members
Public Types inherited from NeuronsSelection
enum	StoppingCondition { MaximumTime, SelectionErrorGoal, MaximumIterations, MaximumSelectionFailures, AlgorithmFinished }
	Enumeration of all possibles condition of stop for the algorithms.

Protected Attributes inherited from NeuronsSelection
TrainingStrategy *	training_strategy_pointer = nullptr
	Pointer to a training strategy object.

Vector< size_t >	order_history
	Order of all the neural networks trained.

Vector< double >	selection_error_history
	Selection loss of all the neural networks trained.

Vector< double >	training_loss_history
	Performance of all the neural networks trained.

Vector< Vector< double > >	parameters_history

size_t	minimum_order
	Minimum number of hidden neurons.

size_t	maximum_order
	Maximum number of hidden neurons.

size_t	trials_number
	Number of trials for each neural network.

bool	reserve_error_data
	True if the loss of all neural networks are to be reserved.

bool	reserve_selection_error_data
	True if the selection error of all neural networks are to be reserved.

bool	reserve_minimal_parameters
	True if the vector parameters of the neural network presenting minimum selection error is to be reserved.

bool	display
	Display messages to screen.

double	selection_error_goal
	Goal value for the selection error. It is used as a stopping criterion.

size_t	maximum_iterations_number
	Maximum number of iterations to perform_neurons_selection. It is used as a stopping criterion.

double	maximum_time
	Maximum selection algorithm time. It is used as a stopping criterion.

double	tolerance
	Tolerance for the error in the trainings of the algorithm.

Detailed Description

This concrete class represents an incremental algorithm for the NeuronsSelection as part of the ModelSelection[1] class.

[1] Neural Designer "Model Selection Algorithms in Predictive Analytics." https://www.neuraldesigner.com/blog/model-selection

Definition at line 37 of file incremental_neurons.h.

Constructor & Destructor Documentation

◆ IncrementalNeurons() [1/3]

IncrementalNeurons ( TrainingStrategy * new_training_strategy_pointer )

explicit

Training strategy constructor.

Parameters

new_training_strategy_pointer Pointer to a gradient descent object.

Definition at line 26 of file incremental_neurons.cpp.

◆ IncrementalNeurons() [2/3]

IncrementalNeurons ( const tinyxml2::XMLDocument & incremental_order_document )

explicit

XML constructor.

Parameters

incremental_order_document Pointer to a TinyXML document containing the incremental order data.

Definition at line 36 of file incremental_neurons.cpp.

◆ IncrementalNeurons() [3/3]

IncrementalNeurons ( const string & file_name )

explicit

File constructor.

Parameters

file_name Name of XML incremental order file.

Definition at line 46 of file incremental_neurons.cpp.

Member Function Documentation

◆ from_XML()

void from_XML ( const tinyxml2::XMLDocument & document )

Deserializes a TinyXML document into this incremental order object.

Parameters

document TinyXML document containing the member data.

Definition at line 805 of file incremental_neurons.cpp.

◆ load()

void load ( const string & file_name )

Loads a incremental order object from a XML-type file.

Parameters

file_name Name of incremental order XML-type file.

Definition at line 1104 of file incremental_neurons.cpp.

◆ save()

void save ( const string & file_name ) const

Saves to a XML-type file the members of the incremental order object.

Parameters

file_name Name of incremental order XML-type file.

Definition at line 1091 of file incremental_neurons.cpp.

◆ set_maximum_selection_failures()

void set_maximum_selection_failures ( const size_t & new_maximum_loss_failures )

Sets the maximum selection failures for the Incremental order selection algorithm.

Parameters

new_maximum_loss_failures Maximum number of selection failures in the Incremental order selection algorithm.

Definition at line 124 of file incremental_neurons.cpp.

◆ set_step()

void set_step ( const size_t & new_step )

Sets the number of the hidden perceptrons pointed in each iteration of the Incremental algorithm in the model order selection process.

Parameters

new_step number of hidden perceptrons pointed.

Definition at line 89 of file incremental_neurons.cpp.

◆ to_XML()

tinyxml2::XMLDocument * to_XML ( ) const

Prints to the screen the incremental order parameters, the stopping criteria and other user stuff concerning the incremental order object.

Definition at line 503 of file incremental_neurons.cpp.

◆ write_XML()

void write_XML ( tinyxml2::XMLPrinter & file_stream ) const

Serializes the incremental order 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.

Definition at line 681 of file incremental_neurons.cpp.

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

Classes

Public Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ IncrementalNeurons() [1/3]

◆ IncrementalNeurons() [2/3]

◆ IncrementalNeurons() [3/3]

Member Function Documentation

◆ from_XML()

◆ load()

◆ save()

◆ set_maximum_selection_failures()

◆ set_step()

◆ to_XML()

◆ write_XML()