Per-feature input normalization layer. More...

#include <scaling_layer.h>

Inheritance diagram for opennn::Scaling:

Public Member Functions
	Scaling (const Shape &input_shape={})
	Constructs a Scaling layer.

Shape	get_input_shape () const override
	Returns the per-sample input shape.

Shape	get_output_shape () const override
	Returns the per-sample output shape (same as input).

VectorR	get_minimums () const
	Returns the per-feature minimum statistics.

VectorR	get_maximums () const
	Returns the per-feature maximum statistics.

VectorR	get_means () const
	Returns the per-feature mean statistics.

VectorR	get_standard_deviations () const
	Returns the per-feature standard-deviation statistics.

const vector< ScalerMethod > &	get_scalers () const
	Read-only access to the per-feature scaler method list.

float	get_min_range () const
	Lower bound of the target range used by min-max scalers.

float	get_max_range () const
	Upper bound of the target range used by min-max scalers.

vector< Operator * >	get_operators () override
	Returns the single Scale operator that implements this layer.

void	set (const Shape &input_shape={})
	Re-initializes the layer.

void	set_input_shape (const Shape &) override
	Updates the input shape and resizes the scaler vector.

void	set_output_shape (const Shape &) override
	Updates the output shape (kept equal to the input shape).

void	set_descriptives (const vector< Descriptives > &)
	Sets the per-feature descriptive statistics used by the scalers.

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

void	set_scalers (const vector< string > &)
	Sets the scaler method per feature from a vector of names.

void	set_scalers (const string &)
	Sets the same scaler method for every feature.

void	forward_propagate (ForwardPropagation &, size_t, bool) noexcept override
	Forward pass: applies the configured scaler to each feature.

string	write_no_scaling_expression (const vector< string > &inputs_names, const vector< string > &outputs_names) const
	Generates a symbolic expression that performs no scaling.

string	write_minimum_maximum_expression (const vector< string > &inputs_names, const vector< string > &outputs_names) const
	Generates a symbolic expression for min-max scaling.

string	write_mean_standard_deviation_expression (const vector< string > &inputs_names, const vector< string > &outputs_names) const
	Generates a symbolic expression for mean / standard-deviation scaling.

string	write_standard_deviation_expression (const vector< string > &inputs_names, const vector< string > &outputs_names) const
	Generates a symbolic expression for standard-deviation scaling.

void	read_JSON_body (const Json *) override
	Reads the layer-specific JSON body (input shape, descriptives, scaler methods, target range).

void	write_JSON_body (JsonWriter &) const override
	Writes the layer-specific JSON body (input shape, descriptives, scaler methods, target range).

Public Member Functions inherited from opennn::Layer
virtual	~Layer ()=default
	Virtual destructor; subclasses are owned via unique_ptr<Layer>.

const string &	get_label () const
	Returns the user-assigned label of this layer.

const string &	get_name () const
	Returns the canonical type name of this layer.

LayerType	get_type () const
	Returns the LayerType enumerator for this layer.

void	set_label (string new_label)
	Sets the human-readable label of this layer.

Index	get_parameters_number () const
	Total number of trainable parameters in this layer.

virtual vector< pair< Shape, Type > >	get_parameter_specs () const
	Specifications of the trainable parameter tensors owned by this layer.

virtual vector< pair< Shape, Type > >	get_state_specs () const
	Specifications of the persistent state tensors of this layer.

virtual vector< pair< Shape, Type > >	get_forward_specs (Index batch_size) const
	Specifications of the forward intermediate buffers for one batch.

virtual vector< pair< Shape, Type > >	get_backward_specs (Index batch_size) const
	Specifications of the backward intermediate buffers for one batch.

vector< Shape >	get_parameter_shapes () const
	Shape-only view of get_parameter_specs().

vector< Shape >	get_state_shapes () const
	Shape-only view of get_state_specs().

vector< Shape >	get_forward_shapes (Index b) const
	Shape-only view of get_forward_specs() for batch size `b`.

vector< Shape >	get_backward_shapes (Index b) const
	Shape-only view of get_backward_specs() for batch size `b`.

vector< Type >	get_parameter_dtypes () const
	Dtype-only view of get_parameter_specs().

vector< Type >	get_forward_dtypes (Index b) const
	Dtype-only view of get_forward_specs() for batch size `b`.

vector< Type >	get_backward_dtypes (Index b) const
	Dtype-only view of get_backward_specs() for batch size `b`.

virtual Activation::Function	get_output_activation () const
	Activation function fused at the end of this layer, if any.

Index	get_inputs_number () const
	Total number of scalar inputs per sample (product of input dims).

Index	get_outputs_number () const
	Total number of scalar outputs per sample (product of output dims).

virtual void	back_propagate (ForwardPropagation &, BackPropagation &, size_t) const noexcept
	Backward pass: propagates gradients through this layer.

virtual void	from_JSON (const JsonDocument &document)
	Loads the layer configuration (hyperparameters) from JSON.

virtual void	load_state_from_JSON (const JsonDocument &document)
	Loads parameter and state tensors from a JSON document.

virtual void	to_JSON (JsonWriter &writer) const
	Writes the layer configuration to JSON.

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

bool	get_is_trainable () const
	Whether this layer has trainable parameters.

Type	get_compute_dtype () const
	Numerical type used for forward/backward computation.

void	set_compute_dtype (Type new_compute_dtype)
	Sets the compute dtype and triggers on_compute_dtype_changed().

virtual void	on_compute_dtype_changed ()
	Hook invoked after set_compute_dtype() mutates the dtype.

virtual float *	link_parameters (float *pointer)
	Wires this layer's parameter TensorViews onto an external buffer.

virtual float *	link_states (float *pointer)
	Wires this layer's state TensorViews onto an external buffer.

vector< TensorView > &	get_parameter_views ()
	Mutable access to this layer's parameter TensorViews.

const vector< TensorView > &	get_parameter_views () const
	Read-only access to this layer's parameter TensorViews.

vector< TensorView > &	get_state_views ()
	Mutable access to this layer's state TensorViews.

const vector< TensorView > &	get_state_views () const
	Read-only access to this layer's state TensorViews.

void	redistribute_parameters_to_operators ()
	Forwards the current parameter views down to each composing Operator.

void	redistribute_parameter_gradients_to_operators (vector< TensorView > &gradient_views)
	Forwards externally provided gradient views down to each Operator.

void	redistribute_states_to_operators ()
	Forwards the current state views down to each composing Operator.

Additional Inherited Members
Protected Member Functions inherited from opennn::Layer
	Layer ()=default
	Default constructor; only invoked by subclasses.

float *	link_views (float pointer, const vector< Shape > &shapes, vector< TensorView > &views, const char tag) const
	Builds `views` over a contiguous float buffer using `shapes`.

void	distribute_to_operators (vector< TensorView > &views, void(Operator::link)(const vector< TensorView > &), vector< pair< Shape, Type > >(Operator::specs)() const)
	Generic helper used by the redistribute_*_to_operators() routines.

Protected Attributes inherited from opennn::Layer
string	label = "my_layer"
	User-visible label for this layer instance (default "my_layer").

string	name = "layer"
	Canonical type name set by the subclass (e.g. "dense").

LayerType	layer_type = LayerType::Dense
	Layer type tag set by the subclass.

bool	is_trainable = true
	True if the layer has parameters that participate in training.

bool	is_first_layer = false
	True if this layer is the network's input layer.

Type	compute_dtype = Type::FP32
	Numerical type used for forward and backward computation.

vector< TensorView >	parameters
	Parameter TensorViews bound to the network's parameter arena.

vector< TensorView >	states
	State TensorViews bound to the network's state arena.

vector< unique_ptr< Layer > >	layers
	Sub-layers, when this layer is itself a composite.

Detailed Description

Per-feature input normalization layer.

Holds a vector of ScalerMethod values (one per feature) and the corresponding descriptive statistics (minimum, maximum, mean, standard deviation). The forward pass applies the configured scaler to each feature so that downstream layers see normalized inputs.

The layer has no trainable parameters; the scaler statistics are stored as state and are typically derived from the training dataset.

Constructor & Destructor Documentation

◆ Scaling()

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

Constructs a Scaling layer.

Parameters

input_shape Per-sample input shape.

Member Function Documentation

◆ forward_propagate()

void opennn::Scaling::forward_propagate	(	ForwardPropagation &	,
		size_t	,
		bool	)

overridevirtualnoexcept

Forward pass: applies the configured scaler to each feature.

Receives the ForwardPropagation buffer slice, this layer's index and the training flag.

Reimplemented from opennn::Layer.

◆ get_input_shape()

Shape opennn::Scaling::get_input_shape ( ) const

inlineoverridevirtual

Returns the per-sample input shape.

Implements opennn::Layer.

◆ get_max_range()

float opennn::Scaling::get_max_range ( ) const

inline

Upper bound of the target range used by min-max scalers.

◆ get_maximums()

VectorR opennn::Scaling::get_maximums ( ) const

Returns the per-feature maximum statistics.

◆ get_means()

VectorR opennn::Scaling::get_means ( ) const

Returns the per-feature mean statistics.

◆ get_min_range()

float opennn::Scaling::get_min_range ( ) const

inline

Lower bound of the target range used by min-max scalers.

◆ get_minimums()

VectorR opennn::Scaling::get_minimums ( ) const

Returns the per-feature minimum statistics.

◆ get_operators()

vector< Operator * > opennn::Scaling::get_operators ( )

inlineoverridevirtual

Returns the single Scale operator that implements this layer.

Reimplemented from opennn::Layer.

◆ get_output_shape()

Shape opennn::Scaling::get_output_shape ( ) const

inlineoverridevirtual

Returns the per-sample output shape (same as input).

Implements opennn::Layer.

◆ get_scalers()

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

inline

Read-only access to the per-feature scaler method list.

◆ get_standard_deviations()

VectorR opennn::Scaling::get_standard_deviations ( ) const

Returns the per-feature standard-deviation statistics.

◆ read_JSON_body()

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

overridevirtual

Reads the layer-specific JSON body (input shape, descriptives, scaler methods, target range).

Reimplemented from opennn::Layer.

◆ set()

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

Re-initializes the layer.

Parameters

input_shape Per-sample input shape.

◆ set_descriptives()

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

Sets the per-feature descriptive statistics used by the scalers.

Receives a vector of Descriptives (minimum, maximum, mean, standard deviation) with one entry per input feature.

◆ set_input_shape()

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

overridevirtual

Updates the input shape and resizes the scaler vector.

Reimplemented from opennn::Layer.

◆ set_min_max_range()

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

inline

Sets the target range used by min-max scalers.

Parameters

min	Lower bound of the target range.
max	Upper bound of the target range.

◆ set_output_shape()

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

overridevirtual

Updates the output shape (kept equal to the input shape).

Reimplemented from opennn::Layer.

◆ set_scalers() [1/2]

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

Sets the same scaler method for every feature.

Receives a single method name applied to all features.

◆ set_scalers() [2/2]

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

Sets the scaler method per feature from a vector of names.

Receives one method name per feature; see ScalerMethod for the supported set.

◆ write_JSON_body()

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

overridevirtual

Writes the layer-specific JSON body (input shape, descriptives, scaler methods, target range).

Reimplemented from opennn::Layer.

◆ write_mean_standard_deviation_expression()

string opennn::Scaling::write_mean_standard_deviation_expression	(	const vector< string > &	inputs_names,
		const vector< string > &	outputs_names ) const

Generates a symbolic expression for mean / standard-deviation scaling.

Parameters

inputs_names	Variable names used in the expression for each input.
outputs_names	Variable names used for each output.

Returns: Multi-line expression string suitable for code export.

◆ write_minimum_maximum_expression()

string opennn::Scaling::write_minimum_maximum_expression	(	const vector< string > &	inputs_names,
		const vector< string > &	outputs_names ) const

Generates a symbolic expression for min-max scaling.

Parameters

inputs_names	Variable names used in the expression for each input.
outputs_names	Variable names used for each output.

Returns: Multi-line expression string suitable for code export.

◆ write_no_scaling_expression()

string opennn::Scaling::write_no_scaling_expression	(	const vector< string > &	inputs_names,
		const vector< string > &	outputs_names ) const

Generates a symbolic expression that performs no scaling.

Parameters

inputs_names	Variable names used in the expression for each input.
outputs_names	Variable names used for each output.

Returns: Multi-line expression string suitable for code export.

◆ write_standard_deviation_expression()

string opennn::Scaling::write_standard_deviation_expression	(	const vector< string > &	inputs_names,
		const vector< string > &	outputs_names ) const

Generates a symbolic expression for standard-deviation scaling.

Parameters

inputs_names	Variable names used in the expression for each input.
outputs_names	Variable names used for each output.

Returns: Multi-line expression string suitable for code export.

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Scaling()

Member Function Documentation

◆ forward_propagate()

◆ get_input_shape()

◆ get_max_range()

◆ get_maximums()

◆ get_means()

◆ get_min_range()

◆ get_minimums()

◆ get_operators()

◆ get_output_shape()

◆ get_scalers()

◆ get_standard_deviations()

◆ read_JSON_body()

◆ set()

◆ set_descriptives()

◆ set_input_shape()

◆ set_min_max_range()

◆ set_output_shape()

◆ set_scalers() [1/2]

◆ set_scalers() [2/2]

◆ write_JSON_body()

◆ write_mean_standard_deviation_expression()

◆ write_minimum_maximum_expression()

◆ write_no_scaling_expression()

◆ write_standard_deviation_expression()