athena__recurrent

Module containing implementation of recurrent neural network layers

This module implements the simple recurrent neural network (RNN) layer, which is designed to handle sequential data by maintaining a hidden state.

Simple RNN layer (equivalent to RNNCell of PyTorch): $\begin{align} \mathbf{h}_t &= \sigma(\mathbf{W}_{ih}\mathbf{x}_t + \mathbf{W}_{hh}\mathbf{h}_{t-1} + \mathbf{b}_h) \\ \mathbf{y}_t &= \mathbf{W}_{ho}\mathbf{h}_t + \mathbf{b}_o \end{align}$

where: - $\mathbf{x}_t$ is input at time t - $\mathbf{h}_t$ is hidden state at time t - $\sigma$ is the activation function (e.g., tanh, relu) - $\mathbf{W}$ matrices are learnable weights - $\mathbf{b}$ vectors are learnable biases

Properties: - Processes sequential data with temporal dependencies - Maintains hidden state across time steps

Uses

- athena__misc_types
- coreutils
- athena__base_layer
- diffstruc

Interfaces

public interface recurrent_layer_type

private module function layer_setup(hidden_size, input_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)

Arguments

Type	Intent	Optional		Name
integer,	intent(in)		::	hidden_size
integer,	intent(in),	optional	::	input_size
logical,	intent(in),	optional	::	use_bias
class(*),	intent(in),	optional	::	activation
class(*),	intent(in),	optional	::	kernel_initialiser
class(*),	intent(in),	optional	::	bias_initialiser
integer,	intent(in),	optional	::	verbose

Return Value type(recurrent_layer_type)

Derived Types

type, public, extends(learnable_layer_type) :: recurrent_layer_type

Type for simple RNN layer

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		::	hidden_size			Size of hidden state
type(array_type),	public,	pointer	::	hidden_state	=>	null()	Hidden state
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
integer,	public		::	input_size			Size of input
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_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)
integer,	public		::	time_step			Current time step
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

Constructor

private module function layer_setup (hidden_size, input_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose)

Type-Bound Procedures

procedure, public :: add_t_t => add_learnable	Add two layers
procedure, public, pass(this) :: build_from_onnx => build_from_onnx_base	Build 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_recurrent
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_recurrent
procedure, public, pass(this) :: get_params	Get learnable parameters of layer
procedure, public, pass(this) :: init => init_recurrent
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_recurrent
procedure, public, pass(this) :: read => read_recurrent
procedure, public, pass(this) :: reduce => reduce_learnable	Merge another learnable layer into this one
procedure, public, pass(this) :: reset_state => reset_state_recurrent
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_recurrent
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 read_recurrent_layer(unit, verbose) result(layer)

Read recurrent 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_recurrent(this) result(num_params)

Arguments

Type	Intent	Optional	Attributes		Name
class(recurrent_layer_type),	intent(in)			::	this

Return Value integer

private module function layer_setup(hidden_size, input_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)

Setup a recurrent layer

Arguments

Type	Intent	Optional		Name
integer,	intent(in)		::	hidden_size	Size of hidden state
integer,	intent(in),	optional	::	input_size	Size of input
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(recurrent_layer_type)

Instance of the recurrent layer

Subroutines

private subroutine forward_recurrent(this, input)

Forward propagation

Arguments

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

private subroutine init_recurrent(this, input_shape, verbose)

Initialise the recurrent layer

Arguments

Type	Intent	Optional	Attributes		Name
class(recurrent_layer_type),	intent(inout)			::	this	Instance of the recurrent layer
integer,	intent(in),		dimension(:)	::	input_shape	Shape of the input
integer,	intent(in),	optional		::	verbose	Verbosity level

private subroutine print_to_unit_recurrent(this, unit)

Print recurrent layer to unit

Arguments

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

private subroutine read_recurrent(this, unit, verbose)

Read recurrent layer from file

Arguments

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

private subroutine reset_state_recurrent(this)

Reset the hidden state of the recurrent layer

Arguments

Type	Intent	Optional	Attributes		Name
class(recurrent_layer_type),	intent(inout)			::	this	Instance of the recurrent layer

private subroutine set_hyperparams_recurrent(this, hidden_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose)

Set the hyperparameters for fully connected layer

Arguments

Type	Intent	Optional	Attributes		Name
class(recurrent_layer_type),	intent(inout)			::	this	Instance of the recurrent layer
integer,	intent(in)			::	hidden_size	Number of hidden units
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__recurrent_layer Module

Contents

Interfaces

Derived Types

Functions

Subroutines

Uses

Interfaces

public interface recurrent_layer_type

private module function layer_setup(hidden_size, input_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)

Arguments

Return Value type(recurrent_layer_type)

Derived Types

type, public, extends(learnable_layer_type) :: recurrent_layer_type

Components

Constructor

Type-Bound Procedures

Functions

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

Arguments

Return Value class(base_layer_type), allocatable

private pure function get_num_params_recurrent(this) result(num_params)

Arguments

Return Value integer

private module function layer_setup(hidden_size, input_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)

Arguments

Return Value type(recurrent_layer_type)

Subroutines

private subroutine forward_recurrent(this, input)

Arguments

private subroutine init_recurrent(this, input_shape, verbose)

Arguments

private subroutine print_to_unit_recurrent(this, unit)

Arguments

private subroutine read_recurrent(this, unit, verbose)

Arguments

private subroutine reset_state_recurrent(this)

Arguments

private subroutine set_hyperparams_recurrent(this, hidden_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose)

Arguments

athena__recurrent_layer Module

Contents

Interfaces

Derived Types

Functions

Subroutines

Uses

Interfaces

public interface recurrent_layer_type

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

Arguments

Return Value type(recurrent_layer_type)

Derived Types

type, public, extends(learnable_layer_type) :: recurrent_layer_type

Components

Constructor

Type-Bound Procedures

Functions

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

Arguments

Return Value class(base_layer_type), allocatable

private pure function get_num_params_recurrent(this) result(num_params)

Arguments

Return Value integer

private module function layer_setup(hidden_size, input_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)

Arguments

Return Value type(recurrent_layer_type)

Subroutines

private subroutine forward_recurrent(this, input)

Arguments

private subroutine init_recurrent(this, input_shape, verbose)

Arguments

private subroutine print_to_unit_recurrent(this, unit)

Arguments

private subroutine read_recurrent(this, unit, verbose)

Arguments

private subroutine reset_state_recurrent(this)

Arguments

private subroutine set_hyperparams_recurrent(this, hidden_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose)

Arguments

private module function layer_setup(hidden_size, input_size, use_bias, activation, kernel_initialiser, bias_initialiser, verbose) result(layer)