Basic recurrent (RNN) layer that unrolls over time steps with a shared activation. More...

#include <recurrent_layer.h>

Inheritance diagram for opennn::Recurrent:

Public Member Functions
	Recurrent (const Shape &={0, 0}, const Shape &={0})
	Constructs a recurrent layer with input and output shapes.

Shape	get_input_shape () const override
	Returns the input tensor shape (time_steps, input_features).

Shape	get_output_shape () const override
	Returns the output tensor shape (hidden units).

const TensorView &	get_biases () const

const TensorView &	get_input_weights () const

const TensorView &	get_recurrent_weights () const

const string &	get_activation_function () const

vector< TensorSpec >	get_parameter_specs () const override
	Returns the tensor specifications for the trainable parameters.

vector< TensorSpec >	get_forward_specs (Index batch_size) const override
	Returns the tensor specifications used during forward propagation.

vector< TensorSpec >	get_backward_specs (Index batch_size) const override
	Returns the tensor specifications used during back propagation.

void	set (const Shape &={}, const Shape &={})
	Reconfigures the layer with new input and output shapes.

void	set_input_shape (const Shape &) override
	Updates the layer for a new input shape.

void	set_output_shape (const Shape &) override
	Updates the layer for a new output shape.

void	set_activation_function (const string &)
	Sets the activation function by name (e.g. "Tanh", "ReLU").

void	forward_propagate (ForwardPropagation &, size_t, bool) noexcept override
	Runs the forward pass, unrolling the recurrence across time steps.

void	back_propagate (ForwardPropagation &, BackPropagation &, size_t) const noexcept override
	Runs the backward pass (backpropagation through time).

void	read_JSON_body (const Json *) override
	Reads the layer configuration from a JSON node.

void	write_JSON_body (JsonWriter &) const override
	Writes the layer configuration to a JSON writer.

string	write_expression (const vector< string > &input_names, const vector< string > &output_names) const override
	Returns a symbolic expression of the layer for export.

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

const string &	get_label () const

const string &	get_name () const

LayerType	get_type () const

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_state_specs () const
	Returns the tensor specs of persistent state (e.g. running mean/variance).

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	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.

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

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

Basic recurrent (RNN) layer that unrolls over time steps with a shared activation.

Constructor & Destructor Documentation

◆ Recurrent()

opennn::Recurrent::Recurrent	(	const Shape &	= {0, 0},
		const Shape &	= {0} )

Constructs a recurrent layer with input and output shapes.

Parameters

input_shape	Input shape as (time_steps, input_features).
output_shape	Output shape (number of hidden units).

Member Function Documentation

◆ back_propagate()

void opennn::Recurrent::back_propagate	(	ForwardPropagation &	,
		BackPropagation &	,
		size_t	) const

overridevirtualnoexcept

Runs the backward pass (backpropagation through time).

Reimplemented from opennn::Layer.

◆ forward_propagate()

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

overridevirtualnoexcept

Runs the forward pass, unrolling the recurrence across time steps.

Reimplemented from opennn::Layer.

◆ get_activation_function()

const string & opennn::Recurrent::get_activation_function ( ) const

inline

◆ get_backward_specs()

vector< TensorSpec > opennn::Recurrent::get_backward_specs ( Index batch_size ) const

overridevirtual

Returns the tensor specifications used during back propagation.

Reimplemented from opennn::Layer.

◆ get_biases()

const TensorView & opennn::Recurrent::get_biases ( ) const

inline

◆ get_forward_specs()

vector< TensorSpec > opennn::Recurrent::get_forward_specs ( Index batch_size ) const

overridevirtual

Returns the tensor specifications used during forward propagation.

Reimplemented from opennn::Layer.

◆ get_input_shape()

Shape opennn::Recurrent::get_input_shape ( ) const

inlineoverridevirtual

Returns the input tensor shape (time_steps, input_features).

Reimplemented from opennn::Layer.

◆ get_input_weights()

const TensorView & opennn::Recurrent::get_input_weights ( ) const

inline

◆ get_output_shape()

Shape opennn::Recurrent::get_output_shape ( ) const

overridevirtual

Returns the output tensor shape (hidden units).

Implements opennn::Layer.

◆ get_parameter_specs()

vector< TensorSpec > opennn::Recurrent::get_parameter_specs ( ) const

overridevirtual

Returns the tensor specifications for the trainable parameters.

Reimplemented from opennn::Layer.

◆ get_recurrent_weights()

const TensorView & opennn::Recurrent::get_recurrent_weights ( ) const

inline

◆ read_JSON_body()

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

overridevirtual

Reads the layer configuration from a JSON node.

Reimplemented from opennn::Layer.

◆ set()

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

Reconfigures the layer with new input and output shapes.

◆ set_activation_function()

void opennn::Recurrent::set_activation_function ( const string & )

Sets the activation function by name (e.g. "Tanh", "ReLU").

◆ set_input_shape()

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

overridevirtual

Updates the layer for a new input shape.

Reimplemented from opennn::Layer.

◆ set_output_shape()

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

overridevirtual

Updates the layer for a new output shape.

Reimplemented from opennn::Layer.

◆ write_expression()

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

overridevirtual

Returns a symbolic expression of the layer for export.

Reimplemented from opennn::Layer.

◆ write_JSON_body()

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

overridevirtual

Writes the layer configuration to a JSON writer.

Reimplemented from opennn::Layer.

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Recurrent()

Member Function Documentation

◆ back_propagate()

◆ forward_propagate()

◆ get_activation_function()

◆ get_backward_specs()

◆ get_biases()

◆ get_forward_specs()

◆ get_input_shape()

◆ get_input_weights()

◆ get_output_shape()

◆ get_parameter_specs()

◆ get_recurrent_weights()

◆ read_JSON_body()

◆ set()

◆ set_activation_function()

◆ set_input_shape()

◆ set_output_shape()

◆ write_expression()

◆ write_JSON_body()