This class represents the weighted squared error term. More...

#include <weighted_squared_error.h>

Inheritance diagram for WeightedSquaredError:

Public Member Functions
	WeightedSquaredError ()

	WeightedSquaredError (NeuralNetwork , DataSet )

virtual	~WeightedSquaredError ()
	Destructor. More...

type	get_positives_weight () const
	Returns the weight of the positives. More...

type	get_negatives_weight () const
	Returns the weight of the negatives. More...

type	get_normalizaton_coefficient () const

void	set_default ()
	Set the default values for the object. More...

void	set_positives_weight (const type &)

void	set_negatives_weight (const type &)

void	set_weights (const type &, const type &)

void	set_weights ()
	Calculates of the weights for the positives and negatives values with the data of the data set. More...

void	set_normalization_coefficient ()
	Calculates of the normalization coefficient with the data of the data set. More...

void	set_data_set_pointer (DataSet *)
	set_data_set_pointer More...

type	weighted_sum_squared_error (const Tensor< type, 2 > &x, const Tensor< type, 2 > &y) const

string	get_error_type () const
	Returns a string with the name of the weighted squared error loss type, "WEIGHTED_SQUARED_ERROR". More...

string	get_error_type_text () const
	Returns a string with the name of the weighted squared error loss type in text format. More...

void	calculate_error (const DataSetBatch &, const NeuralNetworkForwardPropagation &, LossIndexBackPropagation &) const

void	calculate_output_delta (const DataSetBatch &, NeuralNetworkForwardPropagation &, LossIndexBackPropagation &) const

void	calculate_squared_errors_lm (const DataSetBatch &, const NeuralNetworkForwardPropagation &, LossIndexBackPropagationLM &) const

void	calculate_error_lm (const DataSetBatch &, const NeuralNetworkForwardPropagation &, LossIndexBackPropagationLM &) const

void	calculate_error_gradient_lm (const DataSetBatch &, LossIndexBackPropagationLM &) const

void	calculate_error_hessian_lm (const DataSetBatch &, LossIndexBackPropagationLM &) const

void	from_XML (const tinyxml2::XMLDocument &)

void	write_XML (tinyxml2::XMLPrinter &) const

Public Member Functions inherited from LossIndex
	LossIndex ()

	LossIndex (NeuralNetwork , DataSet )

virtual	~LossIndex ()
	Destructor. More...

NeuralNetwork *	get_neural_network_pointer () const
	Returns a pointer to the neural network object associated to the error term. More...

DataSet *	get_data_set_pointer () const
	Returns a pointer to the data set object associated to the error term. More...

const type &	get_regularization_weight () const
	Returns regularization weight. More...

const bool &	get_display () const

bool	has_neural_network () const

bool	has_data_set () const

RegularizationMethod	get_regularization_method () const
	Returns the regularization method. More...

void	set ()

void	set (NeuralNetwork *)

void	set (DataSet *)

void	set (NeuralNetwork , DataSet )

void	set (const LossIndex &)

void	set_threads_number (const int &)

void	set_neural_network_pointer (NeuralNetwork *)

void	set_default ()
	Sets the members of the error term to their default values: More...

void	set_regularization_method (const RegularizationMethod &)

void	set_regularization_method (const string &)

void	set_regularization_weight (const type &)

void	set_display (const bool &)

bool	has_selection () const
	Returns true if there are selection samples and false otherwise. More...

type	calculate_eta () const

type	calculate_h (const type &) const

Tensor< type, 1 >	calculate_gradient_numerical_differentiation ()

Tensor< type, 2 >	calculate_jacobian_numerical_differentiation ()

void	calculate_errors (const DataSetBatch &, const NeuralNetworkForwardPropagation &, LossIndexBackPropagation &) const

void	calculate_layers_delta (const DataSetBatch &, NeuralNetworkForwardPropagation &, LossIndexBackPropagation &) const

void	calculate_error_gradient (const DataSetBatch &, const NeuralNetworkForwardPropagation &, LossIndexBackPropagation &) const

void	back_propagate (const DataSetBatch &, NeuralNetworkForwardPropagation &, LossIndexBackPropagation &) const

void	calculate_errors_lm (const DataSetBatch &, const NeuralNetworkForwardPropagation &, LossIndexBackPropagationLM &) const

virtual void	calculate_output_delta_lm (const DataSetBatch &, NeuralNetworkForwardPropagation &, LossIndexBackPropagationLM &) const

void	calculate_layers_delta_lm (const DataSetBatch &, NeuralNetworkForwardPropagation &, LossIndexBackPropagationLM &) const

void	calculate_squared_errors_jacobian_lm (const DataSetBatch &, NeuralNetworkForwardPropagation &, LossIndexBackPropagationLM &) const

void	back_propagate_lm (const DataSetBatch &, NeuralNetworkForwardPropagation &, LossIndexBackPropagationLM &) const

type	calculate_regularization (const Tensor< type, 1 > &) const

void	calculate_regularization_gradient (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	calculate_regularization_hessian (const Tensor< type, 1 > &, Tensor< type, 2 > &) const

void	from_XML (const tinyxml2::XMLDocument &)

void	regularization_from_XML (const tinyxml2::XMLDocument &)

void	write_regularization_XML (tinyxml2::XMLPrinter &) const

string	write_regularization_method () const

void	check () const

Private Attributes
type	positives_weight = type(NAN)
	Weight for the positives for the calculation of the error. More...

type	negatives_weight = type(NAN)
	Weight for the negatives for the calculation of the error. More...

type	normalization_coefficient
	Coefficient of normalization. More...

Additional Inherited Members
Public Types inherited from LossIndex
enum class	RegularizationMethod { L1 , L2 , NoRegularization }
	Enumeration of available regularization methods. More...

Protected Attributes inherited from LossIndex
NonBlockingThreadPool *	non_blocking_thread_pool = nullptr

ThreadPoolDevice *	thread_pool_device = nullptr

NeuralNetwork *	neural_network_pointer = nullptr
	Pointer to a neural network object. More...

DataSet *	data_set_pointer = nullptr
	Pointer to a data set object. More...

RegularizationMethod	regularization_method = RegularizationMethod::L2
	Pointer to a regularization method object. More...

type	regularization_weight = static_cast<type>(0.01)
	Regularization weight value. 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 >	A_B = {IndexPair<Index>(1, 0)}

const Eigen::array< IndexPair< Index >, 2 >	SSE = {IndexPair<Index>(0, 0), IndexPair<Index>(1, 1)}

const Eigen::array< int, 1 >	rows_sum = {Eigen::array<int, 1>({1})}

Detailed Description

This class represents the weighted squared error term.

The weighted squared error measures the difference between the outputs from a neural network and the targets in a data set. This functional is used in data modeling problems, such as function regression, classification and time series prediction.

Definition at line 37 of file weighted_squared_error.h.

Constructor & Destructor Documentation

◆ WeightedSquaredError() [1/2]

WeightedSquaredError ( )

explicit

Default constructor. It creates a weighted squared error term not associated to any neural network and not measured on any data set. It also initializes all the rest of class members to their default values.

Definition at line 19 of file weighted_squared_error.cpp.

◆ WeightedSquaredError() [2/2]

WeightedSquaredError	(	NeuralNetwork *	new_neural_network_pointer,
		DataSet *	new_data_set_pointer
	)

explicit

Neural network and data set constructor. It creates a weighted squared error term object associated to a neural network and measured on a data set. It also initializes all the rest of class members to their default values.

Parameters

new_neural_network_pointer	Pointer to a neural network object.
new_data_set_pointer	Pointer to a data set object.

Definition at line 32 of file weighted_squared_error.cpp.

◆ ~WeightedSquaredError()

~WeightedSquaredError ( )

virtual

Destructor.

Definition at line 41 of file weighted_squared_error.cpp.

Member Function Documentation

◆ calculate_error()

void calculate_error	(	const DataSetBatch &	batch,
		const NeuralNetworkForwardPropagation &	forward_propagation,
		LossIndexBackPropagation &	back_propagation
	)		const

virtual

Implements LossIndex.

Definition at line 205 of file weighted_squared_error.cpp.

◆ calculate_error_gradient_lm()

void calculate_error_gradient_lm	(	const DataSetBatch &	batch,
		LossIndexBackPropagationLM &	loss_index_back_propagation_lm
	)		const

virtual

Todo:: Add gradient and hessian weighted squared error code (insted of normalized squared error)

Reimplemented from LossIndex.

Definition at line 300 of file weighted_squared_error.cpp.

◆ calculate_error_hessian_lm()

void calculate_error_hessian_lm	(	const DataSetBatch &	batch,
		LossIndexBackPropagationLM &	loss_index_back_propagation_lm
	)		const

virtual

Reimplemented from LossIndex.

Definition at line 321 of file weighted_squared_error.cpp.

◆ calculate_error_lm()

void calculate_error_lm	(	const DataSetBatch &	batch,
		const NeuralNetworkForwardPropagation &	,
		LossIndexBackPropagationLM &	back_propagation
	)		const

virtual

Reimplemented from LossIndex.

Definition at line 242 of file weighted_squared_error.cpp.

◆ calculate_output_delta()

void calculate_output_delta	(	const DataSetBatch &	batch,
		NeuralNetworkForwardPropagation &	,
		LossIndexBackPropagation &	back_propagation
	)		const

virtual

Implements LossIndex.

Definition at line 258 of file weighted_squared_error.cpp.

◆ calculate_squared_errors_lm()

void calculate_squared_errors_lm	(	const DataSetBatch &	batch,
		const NeuralNetworkForwardPropagation &	forward_propagation,
		LossIndexBackPropagationLM &	loss_index_back_propagation_lm
	)		const

virtual

Reimplemented from LossIndex.

Definition at line 510 of file weighted_squared_error.cpp.

◆ from_XML()

void from_XML ( const tinyxml2::XMLDocument & document )

Loads a weighted squared error object from a XML document.

Parameters

document Pointer to a TinyXML document with the object data.

Definition at line 401 of file weighted_squared_error.cpp.

◆ get_error_type()

string get_error_type ( ) const

virtual

Returns a string with the name of the weighted squared error loss type, "WEIGHTED_SQUARED_ERROR".

Reimplemented from LossIndex.

Definition at line 344 of file weighted_squared_error.cpp.

◆ get_error_type_text()

string get_error_type_text ( ) const

virtual

Returns a string with the name of the weighted squared error loss type in text format.

Reimplemented from LossIndex.

Definition at line 352 of file weighted_squared_error.cpp.

◆ get_negatives_weight()

type get_negatives_weight ( ) const

Returns the weight of the negatives.

Definition at line 56 of file weighted_squared_error.cpp.

◆ get_normalizaton_coefficient()

type get_normalizaton_coefficient ( ) const

Definition at line 62 of file weighted_squared_error.cpp.

◆ get_positives_weight()

type get_positives_weight ( ) const

Returns the weight of the positives.

Definition at line 48 of file weighted_squared_error.cpp.

◆ set_data_set_pointer()

void set_data_set_pointer ( DataSet * new_data_set_pointer )

virtual

set_data_set_pointer

Parameters

new_data_set_pointer

Reimplemented from LossIndex.

Definition at line 195 of file weighted_squared_error.cpp.

◆ set_default()

void set_default ( )

Set the default values for the object.

Definition at line 70 of file weighted_squared_error.cpp.

◆ set_negatives_weight()

void set_negatives_weight ( const type & new_negatives_weight )

Set a new weight for the negatives values.

Parameters

new_negatives_weight New weight for the negatives.

Definition at line 100 of file weighted_squared_error.cpp.

◆ set_normalization_coefficient()

void set_normalization_coefficient ( )

virtual

Calculates of the normalization coefficient with the data of the data set.

Reimplemented from LossIndex.

Definition at line 162 of file weighted_squared_error.cpp.

◆ set_positives_weight()

void set_positives_weight ( const type & new_positives_weight )

Set a new weight for the positives values.

Parameters

new_positives_weight New weight for the positives.

Definition at line 91 of file weighted_squared_error.cpp.

◆ set_weights() [1/2]

void set_weights ( )

Calculates of the weights for the positives and negatives values with the data of the data set.

Definition at line 119 of file weighted_squared_error.cpp.

◆ set_weights() [2/2]

void set_weights	(	const type &	new_positives_weight,
		const type &	new_negatives_weight
	)

Set new weights for the positives and negatives values.

Parameters

new_positives_weight	New weight for the positives.
new_negatives_weight	New weight for the negatives.

Definition at line 110 of file weighted_squared_error.cpp.

◆ weighted_sum_squared_error()

type weighted_sum_squared_error	(	const Tensor< type, 2 > &	x,
		const Tensor< type, 2 > &	y
	)		const

Definition at line 454 of file weighted_squared_error.cpp.

◆ write_XML()

void write_XML ( tinyxml2::XMLPrinter & file_stream ) const

virtual

Serializes the cross entropy error 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.

Parameters

file_stream

Reimplemented from LossIndex.

Definition at line 362 of file weighted_squared_error.cpp.

Member Data Documentation

◆ negatives_weight

type negatives_weight = type(NAN)

private

Weight for the negatives for the calculation of the error.

Definition at line 120 of file weighted_squared_error.h.

◆ normalization_coefficient

type normalization_coefficient

private

Coefficient of normalization.

Definition at line 124 of file weighted_squared_error.h.

◆ positives_weight

type positives_weight = type(NAN)

private

Weight for the positives for the calculation of the error.

Definition at line 116 of file weighted_squared_error.h.

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

Public Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ WeightedSquaredError() [1/2]

◆ WeightedSquaredError() [2/2]

◆ ~WeightedSquaredError()

Member Function Documentation

◆ calculate_error()

◆ calculate_error_gradient_lm()

◆ calculate_error_hessian_lm()

◆ calculate_error_lm()

◆ calculate_output_delta()

◆ calculate_squared_errors_lm()

◆ from_XML()

◆ get_error_type()

◆ get_error_type_text()

◆ get_negatives_weight()

◆ get_normalizaton_coefficient()

◆ get_positives_weight()

◆ set_data_set_pointer()

◆ set_default()

◆ set_negatives_weight()

◆ set_normalization_coefficient()

◆ set_positives_weight()

◆ set_weights() [1/2]

◆ set_weights() [2/2]

◆ weighted_sum_squared_error()

◆ write_XML()

Member Data Documentation

◆ negatives_weight

◆ normalization_coefficient

◆ positives_weight