Inverse of ScaleOp: maps normalized outputs back to the original feature range. More...

#include <operators.h>

Inheritance diagram for opennn::UnscaleOp:

Public Member Functions
void	forward_propagate (ForwardPropagation &fp, size_t layer, bool is_training) noexcept override
	Runs the operator's forward computation.

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

virtual vector< TensorSpec >	parameter_specs () const
	Returns the tensor specs of trainable parameters owned by this operator.

virtual vector< TensorSpec >	state_specs () const
	Returns the tensor specs of persistent state owned by this operator.

virtual void	link_parameters (span< const TensorView >)
	Binds parameter views provided by the hosting layer.

virtual void	link_gradients (span< const TensorView >)
	Binds gradient views provided by the hosting layer.

virtual void	link_states (span< const TensorView >)
	Binds state views provided by the hosting layer.

virtual void	set_parameters_random ()
	Initializes parameters with random values.

virtual void	set_parameters_glorot ()
	Initializes parameters using Glorot (Xavier) initialization.

virtual void	back_propagate (ForwardPropagation &, BackPropagation &, size_t) const noexcept
	Runs the operator's backward computation, accumulating into gradient/delta buffers.

virtual void	to_JSON (JsonWriter &) const
	Serializes the operator configuration to a JSON writer.

virtual void	from_JSON (const Json *)
	Restores the operator configuration from a JSON node.

virtual void	load_state_from_JSON (const Json *)
	Restores persistent state (e.g. running statistics) from a JSON node.

virtual void	destroy_cuda ()
	Releases CUDA resources owned by the operator; called from destructors.

TensorView &	get_input (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept

vector< TensorView > &	get_inputs (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept

TensorView &	get_output (ForwardPropagation &fp, size_t layer, size_t i=0) const noexcept

TensorView &	get_output_delta (BackPropagation &bp, size_t layer, size_t i=0) const noexcept

TensorView &	get_input_delta (BackPropagation &bp, size_t layer, size_t i=0) const noexcept

Public Attributes
float	min_range = -1.0f

float	max_range = 1.0f

TensorView	minimums

TensorView	maximums

TensorView	means

TensorView	standard_deviations

TensorView	scalers

Public Attributes inherited from opennn::Operator
vector< size_t >	input_slots = {0}

vector< size_t >	output_slots = {1}

vector< size_t >	input_delta_slots = {1}

vector< size_t >	output_delta_slots = {0}

Detailed Description

Inverse of ScaleOp: maps normalized outputs back to the original feature range.

Member Function Documentation

◆ forward_propagate()

void opennn::UnscaleOp::forward_propagate	(	ForwardPropagation &	fp,
		size_t	layer,
		bool	is_training )

overridevirtualnoexcept

Runs the operator's forward computation.

Parameters

fp	Forward propagation workspace.
layer	Index of the hosting layer in the workspace.
is_training	If true, enables training-only behavior (e.g. dropout sampling).

Reimplemented from opennn::Operator.

Member Data Documentation

◆ max_range

float opennn::UnscaleOp::max_range = 1.0f

◆ maximums

TensorView opennn::UnscaleOp::maximums

◆ means

TensorView opennn::UnscaleOp::means

◆ min_range

float opennn::UnscaleOp::min_range = -1.0f

◆ minimums

TensorView opennn::UnscaleOp::minimums

◆ scalers

TensorView opennn::UnscaleOp::scalers

◆ standard_deviations

TensorView opennn::UnscaleOp::standard_deviations

Public Member Functions

Public Attributes