athena__full_layer

Module containing implementation of a fully connected layer

This module implements a fully connected (dense) layer, the fundamental building block of neural networks that connects every input to every output.

Mathematical operation: $\mathbf{y} = \sigma(\mathbf{W}\mathbf{x} + \mathbf{b})$

where: - $\mathbf{x} \in \mathbb{R}^{n_{in}}$ is the input vector - $\mathbf{W} \in \mathbb{R}^{n_{out} \times n_{in}}$ is the weight matrix - $\mathbf{b} \in \mathbb{R}^{n_{out}}$ is the bias vector - $\sigma$ is the activation function - $\mathbf{y} \in \mathbb{R}^{n_{out}}$ is the output vector

Number of parameters: $n_{out} \times n_{in} + n_{out}$ (if bias used)

Properties: Universal function approximator (with sufficient width/depth) Learns arbitrary non-linear mappings between input and output spaces

Attribution statement: The get_num_params procedure is based on code from the neural-fortran library https://github.com/modern-fortran/neural-fortran

Uses

- diffstruc
- coreutils
- athena__initialiser_data
- athena__misc_types
- athena__base_layer

Interfaces

public interface full_layer_type

Interface for setting up the fully connected layer

private module function layer_setup(num_outputs, num_inputs, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)

Setup a fully connected layer

Arguments

Type	Intent	Optional		Name
integer,	intent(in)		::	num_outputs	Number of outputs
integer,	intent(in),	optional	::	num_inputs	Number of inputs
logical,	intent(in),	optional	::	use_bias	Whether to use bias
class(*),	intent(in),	optional	::	activation	Activation function
class(*),	intent(in),	optional	::	kernel_initialiser	Kernel and bias initialisers
class(*),	intent(in),	optional	::	bias_initialiser	Kernel and bias initialisers
integer,	intent(in),	optional	::	verbose	Verbosity level

Return Value type(full_layer_type)

Instance of the fully connected layer

Derived Types

type, public, extends(learnable_layer_type) :: full_layer_type

Type for fully connected (aka dense) layer with overloaded procedures

Components

Type	Visibility	Attributes		Name		Initial
class(base_actv_type),	public,	allocatable	::	activation			Activation function
class(base_init_type),	public,	allocatable	::	bias_init			Initialisers for kernel and bias
character(len=14),	public		::	bias_initialiser	=	''	Initialisers for kernel and bias
integer,	public,	allocatable, dimension(:)	::	bias_shape			Shape of biases
type(graph_type),	public,	allocatable, dimension(:)	::	graph			Graph structure of input data
integer,	public		::	id			Unique identifier
logical,	public		::	inference	=	.false.	Inference mode
integer,	public		::	input_rank	=	0	Rank of input data
integer,	public,	allocatable, dimension(:)	::	input_shape			Input shape
class(base_init_type),	public,	allocatable	::	kernel_init			Initialisers for kernel and bias
character(len=14),	public		::	kernel_initialiser	=	''	Initialisers for kernel and bias
character(len=:),	public,	allocatable	::	name			Layer name
integer,	public		::	num_inputs			Number of inputs
integer,	public		::	num_outputs			Number of outputs
integer,	public		::	num_params	=	0	Number of learnable parameters
class(array_type),	public,	allocatable, dimension(:,:)	::	output			Output
integer,	public		::	output_rank	=	0	Rank of output data
integer,	public,	allocatable, dimension(:)	::	output_shape			Output shape
type(array_type),	public,	allocatable, dimension(:)	::	params			Learnable parameters
character(len=20),	public		::	subtype	=	repeat(" ", 20)
character(len=4),	public		::	type	=	'base'	Layer type
logical,	public		::	use_bias	=	.false.	Layer has bias
logical,	public		::	use_graph_input	=	.false.	Use graph input
logical,	public		::	use_graph_output	=	.false.	Use graph output
integer,	public,	allocatable, dimension(:,:)	::	weight_shape			Shape of weights
type(array_type),	public,	dimension(1)	::	z			Temporary arrays for forward propagation

Constructor

Interface for setting up the fully connected layer

private module function layer_setup (num_outputs, num_inputs, use_bias, activation, kernel_initialiser, bias_initialiser, verbose)	Setup a fully connected layer

Finalizations Procedures

final :: finalise_full	Finalise fully connected layer

Type-Bound Procedures

procedure, public :: add_t_t => add_learnable	Add two layers
procedure, public, pass(this) :: build_from_onnx => build_from_onnx_full	Build fully connected layer from ONNX node and initialiser
procedure, public, pass(this) :: emit_onnx_graph_inputs => emit_onnx_graph_inputs_base	Emit graph input tensor declarations for this layer
procedure, public, pass(this) :: emit_onnx_nodes => emit_onnx_nodes_base	Emit ONNX JSON nodes for this layer (format-aware and polymorphic)
procedure, public, pass(this) :: extract_output => extract_output_base	Extract the output of the layer as a standard real array
procedure, public, pass(this) :: forward => forward_full	Forward propagation derived type handler
procedure, public, pass(this) :: forward_eval => forward_eval_base	Forward pass of layer and return output for evaluation
procedure, public, pass(this) :: get_attributes => get_attributes_base	Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_gradients	Get parameter gradients of layer
procedure, public, pass(this) :: get_num_params => get_num_params_full	Get the number of parameters for fully connected layer
procedure, public, pass(this) :: get_params	Get learnable parameters of layer
procedure, public, pass(this) :: init => init_full	Initialise fully connected layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base	Nullify the forward pass data of the layer to free memory Read more…
generic, public :: operator(+) => add_t_t	Operator overloading for addition
procedure, public, pass(this) :: print => print_base	Print the layer to a file with additional information
procedure, public, pass(this) :: print_to_unit => print_to_unit_full	Print the layer to a file
procedure, public, pass(this) :: read => read_full	Read the layer from a file
procedure, public, pass(this) :: reduce => reduce_learnable	Merge another learnable layer into this one
procedure, public, pass(this) :: set_gradients	Set learnable parameters of layer
procedure, public, pass(this) :: set_graph => set_graph_base	Set the graph structure of the input data !! this is adjacency and edge weighting
procedure, public, pass(this) :: set_hyperparams => set_hyperparams_full	Set the hyperparameters for fully connected layer
procedure, public, pass(this) :: set_params	Set learnable parameters of layer
procedure, public, pass(this) :: set_rank => set_rank_base	Set the input and output ranks of the layer
procedure, public, pass(this) :: set_shape => set_shape_base	Set the input shape of the layer

Functions

public function create_from_onnx_full_layer(node, initialisers, value_info, verbose) result(layer)

Build fully connected layer from attributes and return layer

Arguments

Type	Intent	Optional	Attributes		Name
type(onnx_node_type),	intent(in)			::	node	Instance of ONNX node information
type(onnx_initialiser_type),	intent(in),		dimension(:)	::	initialisers	Instance of ONNX initialiser information
type(onnx_tensor_type),	intent(in),		dimension(:)	::	value_info	Instance of ONNX value info information
integer,	intent(in),	optional		::	verbose	Verbosity level

Return Value class(base_layer_type), allocatable

Instance of the 2D convolutional layer

public function read_full_layer(unit, verbose) result(layer)

Read fully connected layer from file and return layer

Arguments

Type	Intent	Optional	Attributes		Name
integer,	intent(in)			::	unit	Unit number
integer,	intent(in),	optional		::	verbose	Verbosity level

Return Value class(base_layer_type), allocatable

Instance of the fully connected layer

private pure function get_num_params_full(this) result(num_params)

Get the number of parameters for fully connected layer

Arguments

Type	Intent	Optional	Attributes		Name
class(full_layer_type),	intent(in)			::	this	Instance of the fully connected layer

Return Value integer

Number of parameters

private module function layer_setup(num_outputs, num_inputs, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)

Setup a fully connected layer

Arguments

Type	Intent	Optional		Name
integer,	intent(in)		::	num_outputs	Number of outputs
integer,	intent(in),	optional	::	num_inputs	Number of inputs
logical,	intent(in),	optional	::	use_bias	Whether to use bias
class(*),	intent(in),	optional	::	activation	Activation function
class(*),	intent(in),	optional	::	kernel_initialiser	Activation function, kernel initialiser, and bias initialiser
class(*),	intent(in),	optional	::	bias_initialiser	Activation function, kernel initialiser, and bias initialiser
integer,	intent(in),	optional	::	verbose	Verbosity level

Return Value type(full_layer_type)

Instance of the fully connected layer

Subroutines

private subroutine build_from_onnx_full(this, node, initialisers, value_info, verbose)

Read ONNX attributes for fully connected layer

Arguments

Type	Intent	Attributes		Name
class(full_layer_type),	intent(inout)		::	this	Instance of the fully connected layer
type(onnx_node_type),	intent(in)		::	node	Instance of ONNX node information
type(onnx_initialiser_type),	intent(in),	dimension(:)	::	initialisers	Instance of ONNX initialiser information
type(onnx_tensor_type),	intent(in),	dimension(:)	::	value_info	Instance of ONNX value info information
integer,	intent(in)		::	verbose	Verbosity level

private subroutine finalise_full(this)

Finalise fully connected layer

Arguments

Type	Intent	Optional	Attributes		Name
type(full_layer_type),	intent(inout)			::	this	Instance of the fully connected layer

private subroutine forward_full(this, input)

Forward propagation

Arguments

Type	Intent	Optional	Attributes		Name
class(full_layer_type),	intent(inout)			::	this	Instance of the fully connected layer
class(array_type),	intent(in),		dimension(:,:)	::	input	Input values

private subroutine init_full(this, input_shape, verbose)

Initialise fully connected layer

Arguments

Type	Intent	Optional	Attributes		Name
class(full_layer_type),	intent(inout)			::	this	Instance of the fully connected layer
integer,	intent(in),		dimension(:)	::	input_shape	Input shape
integer,	intent(in),	optional		::	verbose	Verbosity level

private subroutine print_to_unit_full(this, unit)

Print fully connected layer to unit

Arguments

Type	Intent	Optional	Attributes		Name
class(full_layer_type),	intent(in)			::	this	Instance of the fully connected layer
integer,	intent(in)			::	unit	File unit

private subroutine read_full(this, unit, verbose)

Read fully connected layer from file

Arguments

Type	Intent	Optional		Name
class(full_layer_type),	intent(inout)		::	this	Instance of the fully connected layer
integer,	intent(in)		::	unit	Unit number
integer,	intent(in),	optional	::	verbose	Verbosity level

private subroutine set_hyperparams_full(this, num_outputs, use_bias, activation, kernel_initialiser, bias_initialiser, verbose)

Set the hyperparameters for fully connected layer

Arguments

Type	Intent	Optional	Attributes		Name
class(full_layer_type),	intent(inout)			::	this	Instance of the fully connected layer
integer,	intent(in)			::	num_outputs	Number of outputs
logical,	intent(in)			::	use_bias	Whether to use bias
class(base_actv_type),	intent(in),		allocatable	::	activation	Activation function
class(base_init_type),	intent(in),		allocatable	::	kernel_initialiser	Kernel and bias initialisers
class(base_init_type),	intent(in),		allocatable	::	bias_initialiser	Kernel and bias initialisers
integer,	intent(in),	optional		::	verbose	Verbosity level

athena__full_layer Module

Contents

Interfaces

Derived Types

Functions

Subroutines

Uses

Interfaces

public interface full_layer_type

private module function layer_setup(num_outputs, num_inputs, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer) Implementation →

Arguments

Return Value type(full_layer_type)

Derived Types

type, public, extends(learnable_layer_type) :: full_layer_type

Components

Constructor

Finalizations Procedures

Type-Bound Procedures

Functions

public function create_from_onnx_full_layer(node, initialisers, value_info, verbose) result(layer)

Arguments

Return Value class(base_layer_type), allocatable

public function read_full_layer(unit, verbose) result(layer)

Arguments

Return Value class(base_layer_type), allocatable

private pure function get_num_params_full(this) result(num_params)

Arguments

Return Value integer

private module function layer_setup(num_outputs, num_inputs, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)

Arguments

Return Value type(full_layer_type)

Subroutines

private subroutine build_from_onnx_full(this, node, initialisers, value_info, verbose)

Arguments

private subroutine finalise_full(this)

Arguments

private subroutine forward_full(this, input)

Arguments

private subroutine init_full(this, input_shape, verbose)

Arguments

private subroutine print_to_unit_full(this, unit)

Arguments

private subroutine read_full(this, unit, verbose)

Arguments

private subroutine set_hyperparams_full(this, num_outputs, use_bias, activation, kernel_initialiser, bias_initialiser, verbose)

Arguments

private module function layer_setup(num_outputs, num_inputs, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)