opennn::NeuronSelection Class Reference

Abstract base class for algorithms that select the optimal number of hidden neurons. More...

#include <neuron_selection.h>

Inheritance diagram for opennn::NeuronSelection:

Public Types
enum class	StoppingCondition {   MaximumTime , SelectionErrorGoal , MaximumEpochs , MaximumSelectionFailures ,   MaximumNeurons }
	Reasons the neurons selection loop may terminate. More...

Public Member Functions
	NeuronSelection (TrainingStrategy *=nullptr)
	Constructs the algorithm bound to an optional training strategy.
virtual	~NeuronSelection ()=default
const TrainingStrategy *	get_training_strategy () const
bool	has_training_strategy () const
bool	get_display () const
void	set (TrainingStrategy *)
	Binds the algorithm to the given training strategy.
void	set_training_strategy (TrainingStrategy *new_training_strategy)
void	set_default ()
	Restores default search bounds and stopping criteria.
void	set_maximum_neurons (const Index new_maximum_neurons)
void	set_minimum_neurons (const Index new_minimum_neurons)
void	set_trials_number (const Index new_trials_number)
void	set_display (bool new_display)
void	set_validation_error_goal (const float new_validation_error_goal)
void	set_maximum_epochs (const Index new_maximum_epochs)
void	set_maximum_validation_failures (const Index new_maximum_validation_failures)
void	set_maximum_time (const float new_maximum_time)
void	check () const
	Verifies that the training strategy and its dependencies are valid for neurons selection.
virtual NeuronsSelectionResults	perform_neurons_selection ()=0
	Runs the neurons selection algorithm until a stopping criterion is met.
string	get_name () const
virtual void	from_JSON (const JsonDocument &)=0
	Loads algorithm configuration from a JSON document.
virtual void	to_JSON (JsonWriter &) const =0
	Writes algorithm configuration to a JSON writer.
void	save (const filesystem::path &) const
	Saves the algorithm configuration to disk.
void	load (const filesystem::path &)
	Loads the algorithm configuration from disk.
virtual void	print () const
	Prints a human-readable description of the algorithm to stdout.

Protected Attributes
TrainingStrategy *	training_strategy = nullptr
VectorR	validation_error_history
VectorR	training_error_history
Index	minimum_neurons = 0
Index	maximum_neurons = 0
Index	trials_number = 1
float	validation_error_goal = 0
Index	maximum_epochs = 10
Index	maximum_validation_failures = 100
float	maximum_time = 0
bool	display = true
string	name

Detailed Description

Abstract base class for algorithms that select the optimal number of hidden neurons.

Member Enumeration Documentation

StoppingCondition

enum class opennn::NeuronSelection::StoppingCondition

strong

Reasons the neurons selection loop may terminate.

Enumerator
MaximumTime
SelectionErrorGoal
MaximumEpochs
MaximumSelectionFailures
MaximumNeurons

Constructor & Destructor Documentation

NeuronSelection()

opennn::NeuronSelection::NeuronSelection

(

TrainingStrategy *

= nullptr

)

Constructs the algorithm bound to an optional training strategy.

~NeuronSelection()

virtual opennn::NeuronSelection::~NeuronSelection ( )

virtualdefault

Member Function Documentation

check()

void opennn::NeuronSelection::check

(

)

const

Verifies that the training strategy and its dependencies are valid for neurons selection.

from_JSON()

virtual void opennn::NeuronSelection::from_JSON ( const JsonDocument & )

pure virtual

Loads algorithm configuration from a JSON document.

Implemented in opennn::GrowingNeurons.

get_display()

bool opennn::NeuronSelection::get_display ( ) const

inline

get_name()

string opennn::NeuronSelection::get_name ( ) const

inline

get_training_strategy()

const TrainingStrategy * opennn::NeuronSelection::get_training_strategy ( ) const

inline

has_training_strategy()

bool opennn::NeuronSelection::has_training_strategy ( ) const

inline

load()

void opennn::NeuronSelection::load

(

const filesystem::path &

)

Loads the algorithm configuration from disk.

perform_neurons_selection()

virtual NeuronsSelectionResults opennn::NeuronSelection::perform_neurons_selection ( )

pure virtual

Runs the neurons selection algorithm until a stopping criterion is met.

Returns

Results including the optimal neuron count, optimal parameters and error histories.

Implemented in opennn::GrowingNeurons.

print()

virtual void opennn::NeuronSelection::print ( ) const

inlinevirtual

Prints a human-readable description of the algorithm to stdout.

save()

void opennn::NeuronSelection::save

(

const filesystem::path &

)

const

Saves the algorithm configuration to disk.

set()

void opennn::NeuronSelection::set

(

TrainingStrategy *

)

Binds the algorithm to the given training strategy.

set_default()

void opennn::NeuronSelection::set_default

(

)

Restores default search bounds and stopping criteria.

set_display()

void opennn::NeuronSelection::set_display ( bool new_display )

inline

set_maximum_epochs()

void opennn::NeuronSelection::set_maximum_epochs ( const Index new_maximum_epochs )

inline

set_maximum_neurons()

void opennn::NeuronSelection::set_maximum_neurons ( const Index new_maximum_neurons )

inline

set_maximum_time()

void opennn::NeuronSelection::set_maximum_time ( const float new_maximum_time )

inline

set_maximum_validation_failures()

void opennn::NeuronSelection::set_maximum_validation_failures ( const Index new_maximum_validation_failures )

inline

set_minimum_neurons()

void opennn::NeuronSelection::set_minimum_neurons ( const Index new_minimum_neurons )

inline

set_training_strategy()

void opennn::NeuronSelection::set_training_strategy ( TrainingStrategy * new_training_strategy )

inline

set_trials_number()

void opennn::NeuronSelection::set_trials_number ( const Index new_trials_number )

inline

set_validation_error_goal()

void opennn::NeuronSelection::set_validation_error_goal ( const float new_validation_error_goal )

inline

to_JSON()

virtual void opennn::NeuronSelection::to_JSON ( JsonWriter & ) const

pure virtual

Writes algorithm configuration to a JSON writer.

Implemented in opennn::GrowingNeurons.

Member Data Documentation

display

bool opennn::NeuronSelection::display = true

protected

maximum_epochs

Index opennn::NeuronSelection::maximum_epochs = 10

protected

maximum_neurons

Index opennn::NeuronSelection::maximum_neurons = 0

protected

maximum_time

float opennn::NeuronSelection::maximum_time = 0

protected

maximum_validation_failures

Index opennn::NeuronSelection::maximum_validation_failures = 100

protected

minimum_neurons

Index opennn::NeuronSelection::minimum_neurons = 0

protected

name

string opennn::NeuronSelection::name

protected

training_error_history

VectorR opennn::NeuronSelection::training_error_history

protected

training_strategy

TrainingStrategy* opennn::NeuronSelection::training_strategy = nullptr

protected

trials_number

Index opennn::NeuronSelection::trials_number = 1

protected

validation_error_goal

float opennn::NeuronSelection::validation_error_goal = 0

protected

validation_error_history

VectorR opennn::NeuronSelection::validation_error_history

protected