opennn::Layer Class Reference

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