athena__dropout_layer Module

Module containing implementation of a dropout layer

This module implements dropout regularisation, randomly zeroing elements during training to prevent overfitting and co-adaptation of neurons.

Mathematical operation (training): where: - is the output - is the input - is the dropout probability (rate) - is a random variable uniformly distributed in [0,1]

Scaling by maintains expected value:

Inference: acts as identity (no dropout applied)

Benefits: Prevents overfitting, ensemble effect, forces redundancy Typical p values: 0.2-0.5 (higher dropout for larger networks) Reference: Srivastava et al. (2014), JMLR


Uses


Interfaces

public interface dropout_layer_type

Interface for setting up the dropout layer

  • private module function layer_setup(rate, num_masks, input_shape) result(layer)

    Set up the dropout layer

    Arguments

    Type IntentOptional Attributes Name
    real(kind=real32), intent(in) :: rate

    Drop rate
    integer, intent(in) :: num_masks

    Number of unique masks
    integer, intent(in), optional, dimension(:) :: input_shape

    Input shape

    Return Value type(dropout_layer_type)

    Instance of the dropout layer


Derived Types

type, public, extends(drop_layer_type) ::  dropout_layer_type

Type for dropout layer with overloaded procedures

Components

Type Visibility Attributes Name Initial
type(graph_type), public, allocatable, dimension(:) :: graph

Graph structure of input data
integer, public :: id

Unique identifier
integer, public :: idx = 0

Temporary index of sample (doesn't need to be accurate)
logical, public :: inference = .false.

Inference mode
integer, public :: input_rank = 0

Rank of input data
integer, public, allocatable, dimension(:) :: input_shape

Input shape
logical, public, allocatable, dimension(:,:) :: mask

Mask for dropout
character(len=:), public, allocatable :: name

Layer name
integer, public :: num_masks

Number of unique masks = number of samples in batch
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
real(kind=real32), public :: rate = 0.1_real32

Dropout rate, rate = 1 - keep_prob -- typical = 0.05-0.25
character(len=20), public :: subtype = repeat(" ", 20)
character(len=4), public :: type = 'base'

Layer type
logical, public :: use_graph_input = .false.

Use graph input
logical, public :: use_graph_output = .false.

Use graph output

Constructor

Interface for setting up the dropout layer

private module function layer_setup (rate, num_masks, input_shape)

Set up the dropout layer

Type-Bound Procedures

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_dropout

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) :: generate_mask => generate_dropout_mask

Generate dropout mask
procedure, public, pass(this) :: get_attributes => get_attributes_base

Get the attributes of the layer (for ONNX export)
procedure, public, pass(this) :: get_num_params => get_num_params_base

Get the number of parameters in the layer
procedure, public, pass(this) :: init => init_dropout

Initialise dropout layer
procedure, public, pass(this) :: nullify_graph => nullify_graph_base

Nullify the forward pass data of the layer to free memory

Read more…
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_dropout

Print dropout layer to unit
procedure, public, pass(this) :: read => read_dropout

Read dropout layer from file
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_dropout

Set hyperparameters for dropout 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_dropout_layer(node, initialisers, value_info, verbose) result(layer)

Build dropout layer from attributes and return layer

Arguments

Type IntentOptional Attributes Name
type(onnx_node_type), intent(in) :: node

ONNX node information
type(onnx_initialiser_type), intent(in), dimension(:) :: initialisers

ONNX initialiser information
type(onnx_tensor_type), intent(in), dimension(:) :: value_info

ONNX value info information
integer, intent(in), optional :: verbose

Verbosity level

Return Value class(base_layer_type), allocatable

Instance of the dropout layer

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

Read dropout layer from file and return layer

Arguments

Type IntentOptional Attributes Name
integer, intent(in) :: unit

File unit
integer, intent(in), optional :: verbose

Verbosity level

Return Value class(base_layer_type), allocatable

Instance of the base layer

private module function layer_setup(rate, num_masks, input_shape) result(layer)

Set up the dropout layer

Arguments

Type IntentOptional Attributes Name
real(kind=real32), intent(in) :: rate

Drop rate
integer, intent(in) :: num_masks

Number of unique masks
integer, intent(in), optional, dimension(:) :: input_shape

Input shape

Return Value type(dropout_layer_type)

Instance of the dropout layer


Subroutines

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

Read ONNX attributes for dropout layer

Arguments

Type IntentOptional Attributes Name
class(dropout_layer_type), intent(inout) :: this

Instance of the dropout layer
type(onnx_node_type), intent(in) :: node

ONNX node information
type(onnx_initialiser_type), intent(in), dimension(:) :: initialisers

ONNX initialiser information
type(onnx_tensor_type), intent(in), dimension(:) :: value_info

ONNX value info
integer, intent(in) :: verbose

Verbosity level

private subroutine forward_dropout(this, input)

Forward propagation

Arguments

Type IntentOptional Attributes Name
class(dropout_layer_type), intent(inout) :: this

Instance of the dropout layer
class(array_type), intent(in), dimension(:,:) :: input

Input values

private subroutine generate_dropout_mask(this)

Generate dropout mask

Arguments

Type IntentOptional Attributes Name
class(dropout_layer_type), intent(inout) :: this

Instance of the dropout layer

private subroutine init_dropout(this, input_shape, verbose)

Initialise dropout layer

Arguments

Type IntentOptional Attributes Name
class(dropout_layer_type), intent(inout) :: this

Instance of the dropout layer
integer, intent(in), dimension(:) :: input_shape

Input shape
integer, intent(in), optional :: verbose

Verbosity level

private subroutine print_to_unit_dropout(this, unit)

Print dropout layer to unit

Arguments

Type IntentOptional Attributes Name
class(dropout_layer_type), intent(in) :: this

Instance of the dropout layer
integer, intent(in) :: unit

File unit

private subroutine read_dropout(this, unit, verbose)

Read dropout layer from file

Arguments

Type IntentOptional Attributes Name
class(dropout_layer_type), intent(inout) :: this

Instance of the dropout layer
integer, intent(in) :: unit

File unit
integer, intent(in), optional :: verbose

Verbosity level

private pure subroutine set_hyperparams_dropout(this, rate, num_masks)

Set hyperparameters for dropout layer

Arguments

Type IntentOptional Attributes Name
class(dropout_layer_type), intent(inout) :: this

Instance of the dropout layer
real(kind=real32), intent(in) :: rate

Drop rate
integer, intent(in) :: num_masks

Number of unique masks