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

#include <mean_squared_error.h>

Inheritance diagram for MeanSquaredError:

Public Member Functions
	MeanSquaredError ()

	MeanSquaredError (NeuralNetwork , DataSet )

virtual	~MeanSquaredError ()
	Destructor. More...

void	calculate_error (const DataSetBatch &, const NeuralNetworkForwardPropagation &, LossIndexBackPropagation &) const
	MeanSquaredError::calculate_error. More...

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

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

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

void	calculate_error_gradient_lm (const DataSetBatch &, LossIndexBackPropagationLM &) const

void	calculate_error_hessian_lm (const DataSetBatch &, LossIndexBackPropagationLM &) const

void	write_XML (tinyxml2::XMLPrinter &) const

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

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

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 *)

virtual void	set_data_set_pointer (DataSet *)
	Sets a new data set on which the error term is to be measured. More...

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 &)

virtual void	set_normalization_coefficient ()

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_squared_errors_lm (const DataSetBatch &, const 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

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 mean squared error term.

The mean 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 mean_squared_error.h.

Constructor & Destructor Documentation

◆ MeanSquaredError() [1/2]

MeanSquaredError ( )

explicit

Default constructor. It creates a mean 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 mean_squared_error.cpp.

◆ MeanSquaredError() [2/2]

MeanSquaredError	(	NeuralNetwork *	new_neural_network_pointer,
		DataSet *	new_data_set_pointer
	)

explicit

Neural network and data set constructor. It creates a mean 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 31 of file mean_squared_error.cpp.

◆ ~MeanSquaredError()

~MeanSquaredError ( )

virtual

Destructor.

Definition at line 39 of file mean_squared_error.cpp.

Member Function Documentation

◆ calculate_error()

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

virtual

MeanSquaredError::calculate_error.

Parameters

batch
forward_propagation
back_propagation

Implements LossIndex.

Definition at line 49 of file mean_squared_error.cpp.

◆ calculate_error_gradient_lm()

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

virtual

Reimplemented from LossIndex.

Definition at line 195 of file mean_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 216 of file mean_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 65 of file mean_squared_error.cpp.

◆ calculate_output_delta()

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

virtual

Implements LossIndex.

Definition at line 81 of file mean_squared_error.cpp.

◆ calculate_output_delta_lm()

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

virtual

Reimplemented from LossIndex.

Definition at line 140 of file mean_squared_error.cpp.

◆ get_error_type()

string get_error_type ( ) const

virtual

Returns a string with the name of the mean squared error loss type, "MEAN_SQUARED_ERROR".

Reimplemented from LossIndex.

Definition at line 237 of file mean_squared_error.cpp.

◆ get_error_type_text()

string get_error_type_text ( ) const

virtual

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

Reimplemented from LossIndex.

Definition at line 245 of file mean_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

Reimplemented from LossIndex.

Definition at line 254 of file mean_squared_error.cpp.

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

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ MeanSquaredError() [1/2]

◆ MeanSquaredError() [2/2]

◆ ~MeanSquaredError()

Member Function Documentation

◆ calculate_error()

◆ calculate_error_gradient_lm()

◆ calculate_error_hessian_lm()

◆ calculate_error_lm()

◆ calculate_output_delta()

◆ calculate_output_delta_lm()

◆ get_error_type()

◆ get_error_type_text()

◆ write_XML()