Abstract base class for all OpenNN layers; orchestrates operators and shape propagation. More...

#include <layer.h>

Inheritance diagram for opennn::Layer:

Public Member Functions
virtual	~Layer ()=default

const string &	get_label () const

const string &	get_name () const

LayerType	get_type () const

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

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 Shape	get_output_shape () const =0
	Returns the output shape; subclasses must implement this to expose their geometry.

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

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

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

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.

Protected Types
enum	Forward { Input , Output }

enum	Backward { OutputDelta , InputDelta }

Protected Member Functions
	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
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

Abstract base class for all OpenNN layers; orchestrates operators and shape propagation.

Member Enumeration Documentation

◆ Backward

enum opennn::Layer::Backward

protected

Enumerator
OutputDelta
InputDelta

◆ Forward

enum opennn::Layer::Forward

protected

Enumerator
Input
Output

Constructor & Destructor Documentation

◆ ~Layer()

virtual opennn::Layer::~Layer ( )

virtualdefault

◆ Layer() [1/2]

opennn::Layer::Layer ( )

protecteddefault

◆ Layer() [2/2]

opennn::Layer::Layer	(	LayerType	t,
		bool	trainable = true )

inlineprotected

Member Function Documentation

◆ back_propagate()

virtual void opennn::Layer::back_propagate	(	ForwardPropagation &	fp,
		BackPropagation &	bp,
		size_t	i ) const

inlinevirtualnoexcept

Runs the backward pass by chaining the layer's operators in reverse order.

Parameters

fp	Forward propagation workspace (read-only here).
bp	Back propagation workspace receiving gradients.
i	Index of this layer in the workspace.

Reimplemented in opennn::Recurrent.

◆ forward_propagate()

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

inlinevirtualnoexcept

Runs the forward pass by chaining the layer's operators in order.

Parameters

fp	Forward propagation workspace.
layer	Index of this layer in the workspace.
is_training	If true, enables training-only behavior in child operators.

Reimplemented in opennn::Recurrent.

◆ from_JSON()

virtual void opennn::Layer::from_JSON ( const JsonDocument & document )

virtual

Restores layer configuration and parameters from a JSON document.

◆ get_backward_specs()

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

inlinenodiscardvirtual

Returns the tensor specs of the backward workspace; empty for non-trainable layers.

Parameters

batch_size Batch size used to size each tensor.

Reimplemented in opennn::Addition, opennn::Embedding, opennn::MultiHeadAttention, and opennn::Recurrent.

◆ get_compute_dtype()

Type opennn::Layer::get_compute_dtype ( ) const

inlinenodiscard

◆ get_forward_specs()

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

inlinenodiscardvirtual

Returns the tensor specs of the forward workspace; defaults to a single output tensor.

Parameters

batch_size Batch size used to size each tensor.

Reimplemented in opennn::Convolutional, opennn::Dense, opennn::MultiHeadAttention, opennn::Normalization3d, opennn::Pooling3d, opennn::Pooling, and opennn::Recurrent.

◆ get_input_shape()

virtual Shape opennn::Layer::get_input_shape ( ) const

inlinenodiscardvirtual

Returns the input shape stored by the layer.

Reimplemented in opennn::Bounding, opennn::Convolutional, opennn::ConvolutionalRelu, opennn::Embedding, opennn::MultiHeadAttention, opennn::Normalization3d, opennn::Pooling3d, opennn::Pooling, opennn::Recurrent, and opennn::Unscaling.

◆ get_inputs_number()

Index opennn::Layer::get_inputs_number ( ) const

inlinenodiscard

◆ get_is_trainable()

bool opennn::Layer::get_is_trainable ( ) const

inlinenodiscard

◆ get_label()

const string & opennn::Layer::get_label ( ) const

inlinenodiscard

◆ get_name()

const string & opennn::Layer::get_name ( ) const

inlinenodiscard

◆ get_operators()

const vector< Operator * > & opennn::Layer::get_operators ( ) const

inlinenodiscard

◆ get_output_activation()

virtual ActivationOp::Function opennn::Layer::get_output_activation ( ) const

inlinenodiscardvirtual

Returns the layer's output activation (Identity for most layers; overridden by Dense/Bounding).

Reimplemented in opennn::Convolutional, opennn::ConvolutionalRelu, opennn::Dense, and opennn::DenseRelu.

◆ get_output_shape()

virtual Shape opennn::Layer::get_output_shape ( ) const

nodiscardpure virtual

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

Implemented in opennn::Addition, opennn::Bounding, opennn::Convolutional, opennn::ConvolutionalRelu, opennn::Dense, opennn::DenseRelu, opennn::Embedding, opennn::Flatten, opennn::MultiHeadAttention, opennn::Normalization3d, opennn::Pooling3d, opennn::Pooling, opennn::Recurrent, opennn::Scaling, and opennn::Unscaling.

◆ get_outputs_number()

Index opennn::Layer::get_outputs_number ( ) const

inlinenodiscard

◆ get_parameter_specs()

virtual vector< TensorSpec > opennn::Layer::get_parameter_specs ( ) const

nodiscardvirtual

Returns the tensor specs of trainable parameters; subclasses override.

Reimplemented in opennn::Recurrent.

◆ get_parameter_views() [1/2]

vector< TensorView > & opennn::Layer::get_parameter_views ( )

inline

◆ get_parameter_views() [2/2]

const vector< TensorView > & opennn::Layer::get_parameter_views ( ) const

inline

◆ get_parameters_number()

Index opennn::Layer::get_parameters_number ( ) const

nodiscard

Returns the total number of trainable parameters owned by this layer.

◆ get_state_specs()

virtual vector< TensorSpec > opennn::Layer::get_state_specs ( ) const

nodiscardvirtual

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

◆ get_type()

LayerType opennn::Layer::get_type ( ) const

inlinenodiscard

◆ link_gradients()

float * opennn::Layer::link_gradients	(	float *	pointer,
		vector< TensorView > &	gradient_views )

Binds the gradient slice of the shared buffer to operator gradient views.

Parameters

pointer	Start of the layer's slice in the shared gradient buffer.
gradient_views	Output list of views the optimizer iterates over.

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

◆ link_states()

virtual float * opennn::Layer::link_states ( float * pointer )

virtual

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 in opennn::Bounding, opennn::Scaling, and opennn::Unscaling.

◆ link_views_to_operators()

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

protected

◆ load_state_from_JSON()

virtual void opennn::Layer::load_state_from_JSON ( const JsonDocument & document )

virtual

Restores persistent state (e.g. running statistics) from a JSON document.

◆ on_compute_dtype_changed()

virtual void opennn::Layer::on_compute_dtype_changed ( )

inlinevirtual

Subclass hook invoked when the compute dtype changes; default is no-op.

Reimplemented in opennn::Convolutional, opennn::ConvolutionalRelu, opennn::Dense, opennn::DenseRelu, and opennn::MultiHeadAttention.

◆ print()

virtual void opennn::Layer::print ( ) const

inlinevirtual

Prints a human-readable summary of the layer to standard output.

◆ read_JSON_body()

virtual void opennn::Layer::read_JSON_body ( const Json * )

inlinevirtual

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

Reimplemented in opennn::Bounding, opennn::Convolutional, opennn::ConvolutionalRelu, opennn::Dense, opennn::Embedding, opennn::MultiHeadAttention, opennn::Normalization3d, opennn::Pooling3d, opennn::Pooling, opennn::Recurrent, opennn::Scaling, and opennn::Unscaling.

◆ redistribute_parameters_to_operators()

void opennn::Layer::redistribute_parameters_to_operators ( )

Re-binds operator parameter views after the parameter buffer has been resized or moved.

◆ set_compute_dtype()

void opennn::Layer::set_compute_dtype ( Type new_compute_dtype )

inline

Sets the compute dtype and notifies subclasses via on_compute_dtype_changed().

◆ set_input_shape()

virtual void opennn::Layer::set_input_shape ( const Shape & )

virtual

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

Reimplemented in opennn::Addition, opennn::Bounding, opennn::Convolutional, opennn::ConvolutionalRelu, opennn::Dense, opennn::DenseRelu, opennn::Flatten, opennn::MultiHeadAttention, opennn::Normalization3d, opennn::Pooling3d, opennn::Pooling, opennn::Recurrent, opennn::Scaling, and opennn::Unscaling.

◆ set_label()

void opennn::Layer::set_label ( string new_label )

inline

◆ set_output_shape()

virtual void opennn::Layer::set_output_shape ( const Shape & )

virtual

Sets the output shape; subclasses override when the output is user-configurable.

Reimplemented in opennn::Dense, opennn::DenseRelu, opennn::Pooling3d, opennn::Recurrent, and opennn::Unscaling.

◆ to_JSON()

virtual void opennn::Layer::to_JSON ( JsonWriter & writer ) const

virtual

Serializes layer configuration and parameters to a JSON writer.

◆ write_expression()

virtual string opennn::Layer::write_expression	(	const vector< string > &	,
		const vector< string > &	) const

inlinenodiscardvirtual

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 in opennn::Bounding, opennn::Dense, opennn::Recurrent, opennn::Scaling, and opennn::Unscaling.

◆ write_JSON_body()

virtual void opennn::Layer::write_JSON_body ( JsonWriter & ) const

inlinevirtual

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

Reimplemented in opennn::Bounding, opennn::Convolutional, opennn::ConvolutionalRelu, opennn::Embedding, opennn::MultiHeadAttention, opennn::Pooling3d, opennn::Pooling, opennn::Recurrent, opennn::Scaling, and opennn::Unscaling.

Member Data Documentation

◆ compute_dtype

Type opennn::Layer::compute_dtype = Type::FP32

protected

◆ input_shape

Shape opennn::Layer::input_shape

protected

◆ is_trainable

bool opennn::Layer::is_trainable = true

protected

◆ label

string opennn::Layer::label = "my_layer"

protected

◆ layer_type

LayerType opennn::Layer::layer_type = LayerType::Dense

protected

◆ operators

vector<Operator*> opennn::Layer::operators

protected

◆ parameters

vector<TensorView> opennn::Layer::parameters

protected

◆ states

vector<TensorView> opennn::Layer::states

protected

Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Detailed Description

Member Enumeration Documentation

◆ Backward

◆ Forward

Constructor & Destructor Documentation

◆ ~Layer()

◆ Layer() [1/2]

◆ Layer() [2/2]

Member Function Documentation

◆ back_propagate()

◆ forward_propagate()

◆ from_JSON()

◆ get_backward_specs()

◆ get_compute_dtype()

◆ get_forward_specs()

◆ get_input_shape()

◆ get_inputs_number()

◆ get_is_trainable()

◆ get_label()

◆ get_name()

◆ get_operators()

◆ get_output_activation()

◆ get_output_shape()

◆ get_outputs_number()

◆ get_parameter_specs()

◆ get_parameter_views() [1/2]

◆ get_parameter_views() [2/2]

◆ get_parameters_number()

◆ get_state_specs()

◆ get_type()

◆ link_gradients()

◆ link_states()

◆ link_views_to_operators()

◆ load_state_from_JSON()

◆ on_compute_dtype_changed()

◆ print()

◆ read_JSON_body()

◆ redistribute_parameters_to_operators()

◆ set_compute_dtype()

◆ set_input_shape()

◆ set_label()

◆ set_output_shape()

◆ to_JSON()

◆ write_expression()

◆ write_JSON_body()

Member Data Documentation

◆ compute_dtype

◆ input_shape

◆ is_trainable

◆ label

◆ layer_type

◆ operators

◆ parameters

◆ states