This class represents a layer of unscaling neurons. More...

#include <unscaling_layer.h>

Inheritance diagram for UnscalingLayer:

Public Member Functions
	UnscalingLayer ()
	Default constructor. More...

	UnscalingLayer (const Index &)
	Outputs number constructor. More...

	UnscalingLayer (const Tensor< Descriptives, 1 > &)
	Outputs descriptives constructor. More...

virtual	~UnscalingLayer ()
	Destructor. More...

Index	get_inputs_number () const
	Returns the number of inputs. More...

Index	get_neurons_number () const
	Returns the number of unscaling neurons in this layer. More...

Tensor< Descriptives, 1 >	get_descriptives () const

Tensor< type, 1 >	get_minimums () const

Tensor< type, 1 >	get_maximums () const

const Tensor< Scaler, 1 >	get_unscaling_method () const

Tensor< string, 1 >	write_unscaling_methods () const
	Returns a vector of strings with the name of the method used for each unscaling neuron. More...

Tensor< string, 1 >	write_unscaling_method_text () const

const bool &	get_display () const

void	set ()
	Sets the unscaling layer to be empty. More...

void	set (const Index &)

void	set (const Tensor< Descriptives, 1 > &)

void	set (const Tensor< Descriptives, 1 > &, const Tensor< Scaler, 1 > &)

void	set (const tinyxml2::XMLDocument &)

void	set (const UnscalingLayer &)
	Sets the members of this object to be the members of another object of the same class. More...

void	set_inputs_number (const Index &)

void	set_neurons_number (const Index &)

virtual void	set_default ()

void	set_descriptives (const Tensor< Descriptives, 1 > &)

void	set_item_descriptives (const Index &, const Descriptives &)

void	set_minimum (const Index &, const type &)

void	set_maximum (const Index &, const type &)

void	set_mean (const Index &, const type &)

void	set_standard_deviation (const Index &, const type &)

void	set_min_max_range (const type min, const type max)

void	set_scalers (const Tensor< Scaler, 1 > &)

void	set_scalers (const string &)

void	set_scalers (const Tensor< string, 1 > &)

void	set_scalers (const Scaler &)

void	set_display (const bool &)

bool	is_empty () const
	Returns true if the number of unscaling neurons is zero, and false otherwise. More...

Tensor< type, 2 >	calculate_outputs (const Tensor< type, 2 > &)

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

void	from_XML (const tinyxml2::XMLDocument &)

void	write_XML (tinyxml2::XMLPrinter &) const

string	write_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const
	Returns a string with the expression of the inputs scaling process. More...

string	write_expression_c () const

string	write_expression_python () const

Public Member Functions inherited from Layer
string	get_name () const

virtual void	set_parameters_constant (const type &)

virtual void	set_parameters_random ()

virtual Tensor< type, 1 >	get_parameters () const

virtual Index	get_parameters_number () const

virtual void	set_parameters (const Tensor< type, 1 > &, const Index &)

void	set_threads_number (const int &)

virtual void	insert_gradient (LayerBackPropagation *, const Index &, Tensor< type, 1 > &) const

virtual Tensor< type, 2 >	calculate_outputs_from4D (const Tensor< type, 4 > &)

virtual Tensor< type, 4 >	calculate_outputs_4D (const Tensor< type, 4 > &)

virtual void	forward_propagate (const Tensor< type, 2 > &, LayerForwardPropagation *)

virtual void	forward_propagate (const Tensor< type, 4 > &, LayerForwardPropagation *)

virtual void	forward_propagate (const Tensor< type, 4 > &, Tensor< type, 1 >, LayerForwardPropagation *)

virtual void	forward_propagate (const Tensor< type, 2 > &, Tensor< type, 1 >, LayerForwardPropagation *)

virtual void	calculate_hidden_delta (LayerForwardPropagation , LayerBackPropagation , LayerBackPropagation *) const

virtual void	calculate_hidden_delta_lm (LayerForwardPropagation , LayerBackPropagationLM , LayerBackPropagationLM *) const

virtual void	calculate_error_gradient (const Tensor< type, 2 > &, LayerForwardPropagation , LayerBackPropagation ) const

virtual void	calculate_error_gradient (const Tensor< type, 4 > &, LayerForwardPropagation , LayerBackPropagation ) const

virtual void	calculate_squared_errors_Jacobian_lm (const Tensor< type, 2 > &, LayerForwardPropagation , LayerBackPropagationLM )

virtual void	insert_squared_errors_Jacobian_lm (LayerBackPropagationLM *, const Index &, Tensor< type, 2 > &) const

virtual Index	get_synaptic_weights_number () const
	Returns the number of layer's synaptic weights. More...

Type	get_type () const

string	get_type_string () const
	Takes the type of layer used by the model. More...

Protected Attributes
Tensor< Descriptives, 1 >	descriptives
	Descriptives of output variables. More...

Tensor< Scaler, 1 >	scalers
	Unscaling method for the output variables. More...

type	min_range
	min and max range for unscaling More...

type	max_range

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

Protected Attributes inherited from Layer
NonBlockingThreadPool *	non_blocking_thread_pool = nullptr

ThreadPoolDevice *	thread_pool_device = nullptr

string	layer_name = "layer"
	Layer name. More...

Type	layer_type = Type::Perceptron
	Layer type. More...

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

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

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

Additional Inherited Members
Public Types inherited from Layer
enum class	Type { Scaling , Convolutional , Perceptron , Pooling , Probabilistic , LongShortTermMemory , Recurrent , Unscaling , Bounding }
	This enumeration represents the possible types of layers. More...

Protected Member Functions inherited from Layer
void	hard_sigmoid (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

void	hard_sigmoid (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hyperbolic_tangent (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	symmetric_threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	rectified_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	scaled_exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_plus (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_sign (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	softmax (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	binary (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	competitive (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hard_sigmoid_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hyperbolic_tangent_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	symmetric_threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	rectified_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	scaled_exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_plus_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_sign_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const

void	softmax_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const

void	linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	logistic (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hyperbolic_tangent (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	symmetric_threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	rectified_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	scaled_exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_plus (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_sign (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hard_sigmoid (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	logistic_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hyperbolic_tangent_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	symmetric_threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	rectified_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	scaled_exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_plus_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_sign_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hard_sigmoid_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

Detailed Description

This class represents a layer of unscaling neurons.

Unscaling layers are included in the definition of a neural network. They are used to unnormalize variables so they are in the original range after computer processing.

Definition at line 39 of file unscaling_layer.h.

Constructor & Destructor Documentation

◆ UnscalingLayer() [1/3]

UnscalingLayer ( )

explicit

Default constructor.

Definition at line 16 of file unscaling_layer.cpp.

◆ UnscalingLayer() [2/3]

UnscalingLayer ( const Index & new_neurons_number )

explicit

Outputs number constructor.

Definition at line 24 of file unscaling_layer.cpp.

◆ UnscalingLayer() [3/3]

UnscalingLayer ( const Tensor< Descriptives, 1 > & new_descriptives )

explicit

Outputs descriptives constructor.

Definition at line 32 of file unscaling_layer.cpp.

◆ ~UnscalingLayer()

~UnscalingLayer ( )

virtual

Destructor.

Definition at line 40 of file unscaling_layer.cpp.

Member Function Documentation

◆ calculate_outputs()

Tensor< type, 2 > calculate_outputs ( const Tensor< type, 2 > & inputs )

virtual

Calculates the outputs from the unscaling layer for a given set of inputs to that layer.

Parameters

inputs Set of inputs to the unscaling layer.

Reimplemented from Layer.

Definition at line 692 of file unscaling_layer.cpp.

◆ check_range()

void check_range ( const Tensor< type, 1 > & outputs ) const

Checks whether the outptus from the unscaling layer are inside the range defined by the minimums and maximum values. It displays a warning message if they are outside.

Parameters

outputs Set of outptus from the unscaling layer.

Definition at line 627 of file unscaling_layer.cpp.

◆ from_XML()

void from_XML ( const tinyxml2::XMLDocument & document )

virtual

Deserializes a TinyXML document into this unscaling layer object.

Parameters

document XML document containing the member data.

Reimplemented from Layer.

Definition at line 891 of file unscaling_layer.cpp.

◆ get_descriptives()

Tensor< Descriptives, 1 > get_descriptives ( ) const

Returns all the available descriptives of the inputs and output variables. The format is a vector of pointers to vectors of size ten:

Mean of variables.
Standard deviation of variables.
Minimum of variables.
Maximum of variables.

Definition at line 70 of file unscaling_layer.cpp.

◆ get_display()

const bool & get_display ( ) const

Returns true if messages from this class are to be displayed on the screen, or false if messages from this class are not to be displayed on the screen.

Definition at line 265 of file unscaling_layer.cpp.

◆ get_inputs_number()

Index get_inputs_number ( ) const

virtual

Returns the number of inputs.

Reimplemented from Layer.

Definition at line 47 of file unscaling_layer.cpp.

◆ get_maximums()

Tensor< type, 1 > get_maximums ( ) const

Returns a vector with the maximum values of all unscaling neurons. The size is the number of neurons in the layer.

Definition at line 97 of file unscaling_layer.cpp.

◆ get_minimums()

Tensor< type, 1 > get_minimums ( ) const

Returns a vector with the minimum values of all unscaling neurons. The size is the number of neurons in the layer.

Definition at line 79 of file unscaling_layer.cpp.

◆ get_neurons_number()

Index get_neurons_number ( ) const

virtual

Returns the number of unscaling neurons in this layer.

Reimplemented from Layer.

Definition at line 55 of file unscaling_layer.cpp.

◆ get_unscaling_method()

const Tensor< Scaler, 1 > get_unscaling_method ( ) const

Returns the method used for unscaling (no unscaling, minimum and maximum or mean and standard deviation).

Definition at line 115 of file unscaling_layer.cpp.

◆ is_empty()

bool is_empty ( ) const

Returns true if the number of unscaling neurons is zero, and false otherwise.

Definition at line 674 of file unscaling_layer.cpp.

◆ set() [1/6]

void set ( )

Sets the unscaling layer to be empty.

Definition at line 273 of file unscaling_layer.cpp.

◆ set() [2/6]

void set ( const Index & new_neurons_number )

Sets a new size in the unscaling layer. It also sets the members to their default values.

Definition at line 298 of file unscaling_layer.cpp.

◆ set() [3/6]

void set ( const Tensor< Descriptives, 1 > & new_descriptives )

Sets the size of the unscaling layer and the descriptives values.

Parameters

new_descriptives Vector of vectors containing the minimums, maximums, means and standard deviations for the unscaling layer. The size of this vector must be 4. The size of each subvector will be the size of the unscaling layer.

Definition at line 315 of file unscaling_layer.cpp.

◆ set() [4/6]

void set	(	const Tensor< Descriptives, 1 > &	new_descriptives,
		const Tensor< Scaler, 1 > &	new_scalers
	)

Definition at line 327 of file unscaling_layer.cpp.

◆ set() [5/6]

void set ( const tinyxml2::XMLDocument & new_unscaling_layer_document )

Sets the unscaling layer members from a XML document.

Parameters

new_unscaling_layer_document Pointer to a TinyXML document containing the member data.

Definition at line 338 of file unscaling_layer.cpp.

◆ set() [6/6]

void set ( const UnscalingLayer & new_unscaling_layer )

Sets the members of this object to be the members of another object of the same class.

Definition at line 348 of file unscaling_layer.cpp.

◆ set_default()

void set_default ( )

virtual

This member sets the default values for the unscaling layer:

Minimus: -1 for all unscaling neurons.
Maximums: 1 for al unscaling neurons.
Means: 0 for all unscaling neurons.
Standard deviations 1 for all unscaling neurons.
Unscaling method: Minimum and maximum.
Display: True.

Definition at line 368 of file unscaling_layer.cpp.

◆ set_descriptives()

void set_descriptives ( const Tensor< Descriptives, 1 > & new_descriptives )

Sets the descriptives for all the neurons in the unscaling layer from a vector. The size of this vector must be equal to the number of unscaling neurons.

Parameters

new_descriptives Unscaling neurons descriptives.

Definition at line 395 of file unscaling_layer.cpp.

◆ set_display()

void set_display ( const bool & new_display )

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.

Definition at line 617 of file unscaling_layer.cpp.

◆ set_inputs_number()

void set_inputs_number ( const Index & new_inputs_number )

virtual

Reimplemented from Layer.

Definition at line 283 of file unscaling_layer.cpp.

◆ set_item_descriptives()

void set_item_descriptives	(	const Index &	i,
		const Descriptives &	item_descriptives
	)

Sets the minimum, maximum, mean and standard deviation values of a single unscaling neuron.

Parameters

i	Index of unscaling neuron.
item_descriptives	Descriptives values for that neuron.

Definition at line 426 of file unscaling_layer.cpp.

◆ set_maximum()

void set_maximum	(	const Index &	i,
		const type &	new_maximum
	)

Sets the maximum value of a given unscaling neuron.

Parameters

i	Index of unscaling neuron.
new_maximum	Maximum value.

Definition at line 446 of file unscaling_layer.cpp.

◆ set_mean()

void set_mean	(	const Index &	i,
		const type &	new_mean
	)

Sets the mean value of a given unscaling neuron.

Parameters

i	Index of unscaling neuron.
new_mean	Mean value.

Definition at line 456 of file unscaling_layer.cpp.

◆ set_min_max_range()

void set_min_max_range	(	const type	min,
		const type	max
	)

Sets max and min scaling range for minmaxscaling.

Parameters

min	and max for scaling range.

Definition at line 384 of file unscaling_layer.cpp.

◆ set_minimum()

void set_minimum	(	const Index &	i,
		const type &	new_minimum
	)

Sets the minimum value of a given unscaling neuron.

Parameters

i	Index of unscaling neuron.
new_minimum	Minimum value.

Definition at line 436 of file unscaling_layer.cpp.

◆ set_neurons_number()

void set_neurons_number ( const Index & new_neurons_number )

virtual

Reimplemented from Layer.

Definition at line 289 of file unscaling_layer.cpp.

◆ set_scalers() [1/4]

void set_scalers ( const Scaler & new_unscaling_method )

Sets the method to be used for unscaling the variables.

Parameters

new_unscaling_method New unscaling method for the variables.

Definition at line 602 of file unscaling_layer.cpp.

◆ set_scalers() [2/4]

void set_scalers ( const string & new_scaling_methods_string )

Sets the method to be used for unscaling the outputs from the neural network The argument is a string containing the name of the method("NoScaling", "MeanStandardDeviation", "MinimumMaximum" or "Logarithm").

Parameters

new_unscaling_method New unscaling method for the output variables.

Definition at line 485 of file unscaling_layer.cpp.

◆ set_scalers() [3/4]

void set_scalers ( const Tensor< Scaler, 1 > & new_unscaling_method )

Sets the method to be used for unscaling the outputs from the neural network

Parameters

new_unscaling_method New unscaling method for the output variables.

Definition at line 475 of file unscaling_layer.cpp.

◆ set_scalers() [4/4]

void set_scalers ( const Tensor< string, 1 > & new_unscaling_methods_string )

Sets the methods to be used for unscaling each variable. The argument is a vector string containing the name of the methods("NoScaling", "MeanStandardDeviation" or "Logarithm").

Parameters

new_unscaling_methods_string New unscaling methods for the variables.

Definition at line 540 of file unscaling_layer.cpp.

◆ set_standard_deviation()

void set_standard_deviation	(	const Index &	i,
		const type &	new_standard_deviation
	)

Sets the standard deviation value of a given unscaling neuron.

Parameters

i	Index of unscaling neuron.
new_standard_deviation	Standard deviation value.

Definition at line 466 of file unscaling_layer.cpp.

◆ write_expression()

string write_expression	(	const Tensor< string, 1 > &	inputs_names,
		const Tensor< string, 1 > &	outputs_names
	)		const

virtual

Returns a string with the expression of the inputs scaling process.

Reimplemented from Layer.

Definition at line 123 of file unscaling_layer.cpp.

◆ write_expression_c()

string write_expression_c ( ) const

virtual

Returns a string with the expression of the unscaling process in this layer.

Parameters

inputs_names	Name of inputs to the unscaling layer. The size of this vector must be equal to the number of unscaling neurons.
outputs_names	Name of outputs from the unscaling layer. The size of this vector must be equal to the number of unscaling neurons.

Reimplemented from Layer.

Definition at line 1085 of file unscaling_layer.cpp.

◆ write_expression_python()

string write_expression_python ( ) const

virtual

Returns a string with the expression of the unscaling process in this layer.

Parameters

inputs_names	Name of inputs to the unscaling layer. The size of this vector must be equal to the number of unscaling neurons.
outputs_names	Name of outputs from the unscaling layer. The size of this vector must be equal to the number of unscaling neurons.

Reimplemented from Layer.

Definition at line 1158 of file unscaling_layer.cpp.

◆ write_unscaling_method_text()

Tensor< string, 1 > write_unscaling_method_text ( ) const

Returns a string with the name of the method used for unscaling, as paragraph text.

Definition at line 218 of file unscaling_layer.cpp.

◆ write_unscaling_methods()

Tensor< string, 1 > write_unscaling_methods ( ) const

Returns a vector of strings with the name of the method used for each unscaling neuron.

Definition at line 171 of file unscaling_layer.cpp.

◆ write_XML()

void write_XML ( tinyxml2::XMLPrinter & file_stream ) const

virtual

Serializes the unscaling layer 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 Layer.

Definition at line 789 of file unscaling_layer.cpp.

Member Data Documentation

◆ descriptives

Tensor<Descriptives, 1> descriptives

protected

Descriptives of output variables.

Definition at line 140 of file unscaling_layer.h.

◆ display

bool display = true

protected

Display warning messages to screen.

Definition at line 153 of file unscaling_layer.h.

◆ max_range

type max_range

protected

Definition at line 149 of file unscaling_layer.h.

◆ min_range

type min_range

protected

min and max range for unscaling

Definition at line 148 of file unscaling_layer.h.

◆ scalers

Tensor<Scaler, 1> scalers

protected

Unscaling method for the output variables.

Definition at line 144 of file unscaling_layer.h.

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

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ UnscalingLayer() [1/3]

◆ UnscalingLayer() [2/3]

◆ UnscalingLayer() [3/3]

◆ ~UnscalingLayer()

Member Function Documentation

◆ calculate_outputs()

◆ check_range()

◆ from_XML()

◆ get_descriptives()

◆ get_display()

◆ get_inputs_number()

◆ get_maximums()

◆ get_minimums()

◆ get_neurons_number()

◆ get_unscaling_method()

◆ is_empty()

◆ set() [1/6]

◆ set() [2/6]

◆ set() [3/6]

◆ set() [4/6]

◆ set() [5/6]

◆ set() [6/6]

◆ set_default()

◆ set_descriptives()

◆ set_display()

◆ set_inputs_number()

◆ set_item_descriptives()

◆ set_maximum()

◆ set_mean()

◆ set_min_max_range()

◆ set_minimum()

◆ set_neurons_number()

◆ set_scalers() [1/4]

◆ set_scalers() [2/4]

◆ set_scalers() [3/4]

◆ set_scalers() [4/4]

◆ set_standard_deviation()

◆ write_expression()

◆ write_expression_c()

◆ write_expression_python()

◆ write_unscaling_method_text()

◆ write_unscaling_methods()

◆ write_XML()

Member Data Documentation

◆ descriptives

◆ display

◆ max_range

◆ min_range

◆ scalers