Input scaling layer that normalizes features using per-variable descriptive statistics. More...

#include <scaling_layer.h>

Inheritance diagram for opennn::Scaling:

Public Member Functions
	Scaling (const Shape &={})
	Constructs a scaling layer for the given input shape.

Shape	get_output_shape () const override
	Returns the output shape; subclasses must implement this to expose their geometry.

const vector< Descriptives > &	get_descriptives () const

const vector< ScalerMethod > &	get_scalers () const

VectorR	get_minimums () const
	Returns the per-variable minimum values from the stored descriptive statistics.

VectorR	get_maximums () const
	Returns the per-variable maximum values from the stored descriptive statistics.

VectorR	get_means () const
	Returns the per-variable means from the stored descriptive statistics.

VectorR	get_standard_deviations () const
	Returns the per-variable standard deviations from the stored descriptive statistics.

float	get_min_range () const

float	get_max_range () const

void	set (const Shape &={})
	Reconfigures the layer with a new input shape, resetting descriptives and scalers.

void	set_input_shape (const Shape &) override
	Sets the input shape; subclasses override to derive dependent dimensions.

void	set_descriptives (const vector< Descriptives > &)
	Sets the descriptive statistics (min, max, mean, stddev) used for scaling each variable.

void	set_min_max_range (float min, float max)
	Sets the target output range used by min-max scaling methods.

void	set_scalers (const vector< string > &)
	Sets the scaling method for each input variable from a vector of method names.

void	set_scalers (const string &)
	Sets the same scaling method for all input variables from its name.

float *	link_states (float *) override
	Binds the persistent-state region of the shared buffer to operator views.

void	read_JSON_body (const Json *) override
	Subclass hook reading the body section of the layer's JSON node.

void	write_JSON_body (JsonWriter &) const override
	Subclass hook writing the body section of the layer's JSON node.

string	write_expression (const vector< string > &input_names, const vector< string > &output_names) const override
	Returns a human-readable mathematical expression for this layer (empty by default).

Public Member Functions inherited from opennn::Layer
virtual	~Layer ()=default

const string &	get_label () const

const string &	get_name () const

LayerType	get_type () const

virtual void	set_output_shape (const Shape &)
	Sets the output shape; subclasses override when the output is user-configurable.

void	set_label (string new_label)

Index	get_parameters_number () const
	Returns the total number of trainable parameters owned by this layer.

const vector< Operator * > &	get_operators () const

virtual vector< TensorSpec >	get_parameter_specs () const
	Returns the tensor specs of trainable parameters; subclasses override.

virtual vector< TensorSpec >	get_state_specs () const
	Returns the tensor specs of persistent state (e.g. running mean/variance).

virtual vector< TensorSpec >	get_forward_specs (Index batch_size) const
	Returns the tensor specs of the forward workspace; defaults to a single output tensor.

virtual vector< TensorSpec >	get_backward_specs (Index batch_size) const
	Returns the tensor specs of the backward workspace; empty for non-trainable layers.

virtual Shape	get_input_shape () const
	Returns the input shape stored by the layer.

virtual ActivationOp::Function	get_output_activation () const
	Returns the layer's output activation (Identity for most layers; overridden by Dense/Bounding).

Index	get_inputs_number () const

Index	get_outputs_number () const

virtual void	forward_propagate (ForwardPropagation &fp, size_t layer, bool is_training) noexcept
	Runs the forward pass by chaining the layer's operators in order.

virtual void	back_propagate (ForwardPropagation &fp, BackPropagation &bp, size_t i) const noexcept
	Runs the backward pass by chaining the layer's operators in reverse order.

virtual void	from_JSON (const JsonDocument &document)
	Restores layer configuration and parameters from a JSON document.

virtual void	load_state_from_JSON (const JsonDocument &document)
	Restores persistent state (e.g. running statistics) from a JSON document.

virtual void	to_JSON (JsonWriter &writer) const
	Serializes layer configuration and parameters to a JSON writer.

virtual void	print () const
	Prints a human-readable summary of the layer to standard output.

bool	get_is_trainable () const

Type	get_compute_dtype () const

void	set_compute_dtype (Type new_compute_dtype)
	Sets the compute dtype and notifies subclasses via on_compute_dtype_changed().

virtual void	on_compute_dtype_changed ()
	Subclass hook invoked when the compute dtype changes; default is no-op.

float *	link_gradients (float *pointer, vector< TensorView > &gradient_views)
	Binds the gradient slice of the shared buffer to operator gradient views.

vector< TensorView > &	get_parameter_views ()

const vector< TensorView > &	get_parameter_views () const

void	redistribute_parameters_to_operators ()
	Re-binds operator parameter views after the parameter buffer has been resized or moved.

Additional Inherited Members
Protected Types inherited from opennn::Layer
enum	Forward { Input , Output }

enum	Backward { OutputDelta , InputDelta }

Protected Member Functions inherited from opennn::Layer
	Layer ()=default

	Layer (LayerType t, bool trainable=true)

float *	link_views_to_operators (vector< TensorView > &views, float pointer, vector< TensorSpec >(Operator::specs_fn)() const, void(Operator::*link_fn)(span< const TensorView >))

Protected Attributes inherited from opennn::Layer
string	label = "my_layer"

LayerType	layer_type = LayerType::Dense

bool	is_trainable = true

Shape	input_shape

Type	compute_dtype = Type::FP32

vector< TensorView >	parameters

vector< TensorView >	states

vector< Operator * >	operators

Detailed Description

Input scaling layer that normalizes features using per-variable descriptive statistics.

Constructor & Destructor Documentation

◆ Scaling()

opennn::Scaling::Scaling ( const Shape & = {} )

Constructs a scaling layer for the given input shape.

Member Function Documentation

◆ get_descriptives()

const vector< Descriptives > & opennn::Scaling::get_descriptives ( ) const

inline

◆ get_max_range()

float opennn::Scaling::get_max_range ( ) const

inline

◆ get_maximums()

VectorR opennn::Scaling::get_maximums ( ) const

Returns the per-variable maximum values from the stored descriptive statistics.

◆ get_means()

VectorR opennn::Scaling::get_means ( ) const

Returns the per-variable means from the stored descriptive statistics.

◆ get_min_range()

float opennn::Scaling::get_min_range ( ) const

inline

◆ get_minimums()

VectorR opennn::Scaling::get_minimums ( ) const

Returns the per-variable minimum values from the stored descriptive statistics.

◆ get_output_shape()

Shape opennn::Scaling::get_output_shape ( ) const

inlineoverridevirtual

Returns the output shape; subclasses must implement this to expose their geometry.

Implements opennn::Layer.

◆ get_scalers()

const vector< ScalerMethod > & opennn::Scaling::get_scalers ( ) const

inline

◆ get_standard_deviations()

VectorR opennn::Scaling::get_standard_deviations ( ) const

Returns the per-variable standard deviations from the stored descriptive statistics.

◆ link_states()

float * opennn::Scaling::link_states ( float * )

overridevirtual

Binds the persistent-state region of the shared buffer to operator views.

Parameters

pointer Start of the layer's slice in the shared state buffer.

Returns: Pointer advanced past this layer's state region.

Reimplemented from opennn::Layer.

◆ read_JSON_body()

void opennn::Scaling::read_JSON_body ( const Json * )

overridevirtual

Subclass hook reading the body section of the layer's JSON node.

Reimplemented from opennn::Layer.

◆ set()

void opennn::Scaling::set ( const Shape & = {} )

Reconfigures the layer with a new input shape, resetting descriptives and scalers.

◆ set_descriptives()

void opennn::Scaling::set_descriptives ( const vector< Descriptives > & )

Sets the descriptive statistics (min, max, mean, stddev) used for scaling each variable.

◆ set_input_shape()

void opennn::Scaling::set_input_shape ( const Shape & )

overridevirtual

Sets the input shape; subclasses override to derive dependent dimensions.

Reimplemented from opennn::Layer.

◆ set_min_max_range()

void opennn::Scaling::set_min_max_range	(	float	min,
		float	max )

Sets the target output range used by min-max scaling methods.

◆ set_scalers() [1/2]

void opennn::Scaling::set_scalers ( const string & )

Sets the same scaling method for all input variables from its name.

◆ set_scalers() [2/2]

void opennn::Scaling::set_scalers ( const vector< string > & )

Sets the scaling method for each input variable from a vector of method names.

◆ write_expression()

string opennn::Scaling::write_expression	(	const vector< string > &	input_names,
		const vector< string > &	output_names ) const

overridevirtual

Returns a human-readable mathematical expression for this layer (empty by default).

Parameters

input_names	Names assigned to the inputs.
output_names	Names assigned to the outputs.

Reimplemented from opennn::Layer.

◆ write_JSON_body()

void opennn::Scaling::write_JSON_body ( JsonWriter & ) const

overridevirtual

Subclass hook writing the body section of the layer's JSON node.

Reimplemented from opennn::Layer.

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Scaling()

Member Function Documentation

◆ get_descriptives()

◆ get_max_range()

◆ get_maximums()

◆ get_means()

◆ get_min_range()

◆ get_minimums()

◆ get_output_shape()

◆ get_scalers()

◆ get_standard_deviations()

◆ link_states()

◆ read_JSON_body()

◆ set()

◆ set_descriptives()

◆ set_input_shape()

◆ set_min_max_range()

◆ set_scalers() [1/2]

◆ set_scalers() [2/2]

◆ write_expression()

◆ write_JSON_body()