Source file owl_neural_neuron_sig.ml

# 1 "src/base/neural/owl_neural_neuron_sig.ml"
(*
 * OWL - OCaml Scientific and Engineering Computing
 * Copyright (c) 2016-2020 Liang Wang <liang.wang@cl.cam.ac.uk>
 *)

module type Sig = sig
  module Optimise : Owl_optimise_generic_sig.Sig

  open Optimise.Algodiff

  (** {6 Init neuron} *)

  module Init : sig
    type typ =
      | Uniform       of float * float
      | Gaussian      of float * float
      | Standard
      | Tanh
      | GlorotNormal
      | GlorotUniform
      | LecunNormal
      | Custom        of (int array -> t) (** Initialisation types *)

    val calc_fans : int array -> float * float
    (** Calculate fan-in and fan-out of weights. *)

    val run : typ -> int array -> t -> t
    (** Execute the computation in this neuron. *)

    val to_string : typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Input neuron} *)

  module Input : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : int array -> neuron_typ
    (** Create the neuron. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Activation neuron} *)

  module Activation : sig
    type typ =
      | Elu
      | Relu
      | Sigmoid
      | HardSigmoid
      | Softmax     of int
      | Softplus
      | Softsign
      | Tanh
      | Relu6
      | LeakyRelu   of float
      | TRelu       of float
      | Custom      of (t -> t)
      | None (** Types of activation functions. *)

    type neuron_typ =
      { mutable activation : typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : typ -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val run_activation : t -> typ -> t
    (** Run one specific activation function. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val activation_to_string : typ -> string
    (** Return the name of a specific activation function. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Linear neuron} *)

  module Linear : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : ?inputs:int -> int -> Init.typ -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 LinearNoBias neuron} *)

  module LinearNoBias : sig
    type neuron_typ =
      { mutable w : t
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : ?inputs:int -> int -> Init.typ -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Recurrent neuron} *)

  module Recurrent : sig
    type neuron_typ =
      { mutable whh : t
      ; mutable wxh : t
      ; mutable why : t
      ; mutable bh : t
      ; mutable by : t
      ; mutable h : t
      ; mutable hiddens : int
      ; mutable act : Activation.typ
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?time_steps:int
      -> ?inputs:int
      -> int
      -> int
      -> Activation.typ
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 LSTM neuron} *)

  module LSTM : sig
    type neuron_typ =
      { mutable wxi : t
      ; mutable whi : t
      ; mutable wxc : t
      ; mutable whc : t
      ; mutable wxf : t
      ; mutable whf : t
      ; mutable wxo : t
      ; mutable who : t
      ; mutable bi : t
      ; mutable bc : t
      ; mutable bf : t
      ; mutable bo : t
      ; mutable c : t
      ; mutable h : t
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 GRU neuron} *)

  module GRU : sig
    type neuron_typ =
      { mutable wxz : t
      ; mutable whz : t
      ; mutable wxr : t
      ; mutable whr : t
      ; mutable wxh : t
      ; mutable whh : t
      ; mutable bz : t
      ; mutable br : t
      ; mutable bh : t
      ; mutable h : t
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : ?time_steps:int -> ?inputs:int -> int -> Init.typ -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Conv1D neuron} *)

  module Conv1D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Conv2D neuron} *)

  module Conv2D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Conv3D neuron} *)

  module Conv3D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 DilatedConv1D neuron} *)

  module DilatedConv1D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable rate : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 DilatedConv2D neuron} *)

  module DilatedConv2D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable rate : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 DilatedConv3D neuron} *)

  module DilatedConv3D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable rate : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 TransposeConv1D neuron} *)

  module TransposeConv1D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 TransposeConv2D neuron} *)

  module TransposeConv2D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 TransposeConv3D neuron} *)

  module TransposeConv3D : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable padding : Owl_types.padding
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?inputs:int array
      -> Owl_types.padding
      -> int array
      -> int array
      -> Init.typ
      -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 FullyConnected neuron} *)

  module FullyConnected : sig
    type neuron_typ =
      { mutable w : t
      ; mutable b : t
      ; mutable init_typ : Init.typ
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : ?inputs:int -> int -> Init.typ -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 MaxPool1D neuron} *)

  module MaxPool1D : sig
    type neuron_typ =
      { mutable padding : Owl_types.padding
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : Owl_types.padding -> int array -> int array -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 MaxPool2D neuron} *)

  module MaxPool2D : sig
    type neuron_typ =
      { mutable padding : Owl_types.padding
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : Owl_types.padding -> int array -> int array -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 AvgPool1D neuron} *)

  module AvgPool1D : sig
    type neuron_typ =
      { mutable padding : Owl_types.padding
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : Owl_types.padding -> int array -> int array -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 AvgPool2D neuron} *)

  module AvgPool2D : sig
    type neuron_typ =
      { mutable padding : Owl_types.padding
      ; mutable kernel : int array
      ; mutable stride : int array
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : Owl_types.padding -> int array -> int array -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 GlobalMaxPool1D neuron} *)

  module GlobalMaxPool1D : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 GlobalMaxPool2D neuron} *)

  module GlobalMaxPool2D : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 GlobalAvgPool1D neuron} *)

  module GlobalAvgPool1D : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 GlobalAvgPool2D neuron} *)

  module GlobalAvgPool2D : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 UpSampling1D neuron} *)

  module UpSampling1D : sig end

  (** {6 UpSampling2D neuron} *)

  module UpSampling2D : sig
    type neuron_typ =
      { mutable size : int array
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : int array -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 UpSampling3D neuron} *)

  module UpSampling3D : sig end

  (** {6 Padding1D neuron} *)

  module Padding1D : sig end

  (** {6 Padding2D neuron} *)

  module Padding2D : sig
    type neuron_typ =
      { (* array of 2 arrays of 2 ints *)
        mutable padding : int array array
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : int array array -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Padding3D neuron} *)

  module Padding3D : sig end

  (** {6 Lambda neuron} *)

  module Lambda : sig
    type neuron_typ =
      { mutable lambda : t -> t
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : ?out_shape:int array -> (t -> t) -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 LambdaArray neuron} *)

  module LambdaArray : sig
    type neuron_typ =
      { mutable lambda : t array -> t
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : int array -> (t array -> t) -> neuron_typ
    (** Create the neuron. *)

    val connect : int array array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t array -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Dropout neuron} *)

  module Dropout : sig
    type neuron_typ =
      { mutable rate : float
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : float -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Reshape neuron} *)

  module Reshape : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : ?inputs:int array -> int array -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Flatten neuron} *)

  module Flatten : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Add neuron} *)

  module Add : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t array -> 'a -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Mul neuron} *)

  module Mul : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t array -> 'a -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Dot neuron} *)

  module Dot : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t array -> 'a -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Max neuron} *)

  module Max : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t array -> 'a -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Average neuron} *)

  module Average : sig
    type neuron_typ =
      { mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : unit -> neuron_typ
    (** Create the neuron. *)

    val connect : int array array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : 'a -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t array -> 'a -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Concatenate neuron} *)

  module Concatenate : sig
    type neuron_typ =
      { mutable axis : int
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : int -> neuron_typ
    (** Create the neuron. *)

    val connect : int array array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t array -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Normalisation neuron} *)

  module Normalisation : sig
    type neuron_typ =
      { mutable axis : int
      ; mutable beta : t
      ; mutable gamma : t
      ; mutable mu : t
      ; mutable var : t
      ; mutable decay : t
      ; mutable training : bool
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create
      :  ?training:bool
      -> ?decay:float
      -> ?mu:A.arr
      -> ?var:A.arr
      -> int
      -> neuron_typ
    (** Create the neuron. Note that axis 0 is the batch axis. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the trainable parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update trainable parameters of the neuron, used by ``Optimise`` module. *)

    val load_weights : neuron_typ -> t array -> unit
    (** Load both trainable and non-trainable parameters into the neuron. *)

    val save_weights : neuron_typ -> t array
    (** Assemble both trainable and non-trainable parameters of the neuron. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 GaussianNoise neuron} *)

  module GaussianNoise : sig
    type neuron_typ =
      { mutable sigma : float
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : float -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 GaussianDropout neuron} *)

  module GaussianDropout : sig
    type neuron_typ =
      { mutable rate : float
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : float -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 AlphaDropout neuron} *)

  module AlphaDropout : sig
    type neuron_typ =
      { mutable rate : float
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : float -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Embedding neuron} *)

  module Embedding : sig
    type neuron_typ =
      { mutable w : t
      ; mutable init_typ : Init.typ
      ; mutable in_dim : int
      ; mutable in_shape : int array
      ; mutable out_shape : int array
      }
    (** Neuron type definition. *)

    val create : ?inputs:int -> int -> int -> Init.typ -> neuron_typ
    (** Create the neuron. *)

    val connect : int array -> neuron_typ -> unit
    (** Connect this neuron to others in a neural network. *)

    val init : neuron_typ -> unit
    (** Initialise the neuron and its parameters. *)

    val reset : neuron_typ -> unit
    (** Reset the parameters in a neuron. *)

    val mktag : int -> neuron_typ -> unit
    (** Tag the neuron, used by ``Algodiff`` module. *)

    val mkpar : neuron_typ -> t array
    (** Assemble all the parameters in an array, used by ``Optimise`` module. *)

    val mkpri : neuron_typ -> t array
    (** Assemble all the primial values in an array, used by ``Optimise`` module. *)

    val mkadj : neuron_typ -> t array
    (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

    val update : neuron_typ -> t array -> unit
    (** Update parameters in a neuron, used by ``Optimise`` module. *)

    val copy : neuron_typ -> neuron_typ
    (** Make a deep copy of the neuron and its parameters. *)

    val run : t -> neuron_typ -> t
    (** Execute the computation in this neuron. *)

    val to_string : neuron_typ -> string
    (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

    val to_name : unit -> string
    (** Return the name of the neuron. *)
  end

  (** {6 Masking neuron} *)

  module Masking : sig end

  (** {6 Core functions} *)

  type neuron =
    | Input           of Input.neuron_typ
    | Linear          of Linear.neuron_typ
    | LinearNoBias    of LinearNoBias.neuron_typ
    | Embedding       of Embedding.neuron_typ
    | LSTM            of LSTM.neuron_typ
    | GRU             of GRU.neuron_typ
    | Recurrent       of Recurrent.neuron_typ
    | Conv1D          of Conv1D.neuron_typ
    | Conv2D          of Conv2D.neuron_typ
    | Conv3D          of Conv3D.neuron_typ
    | DilatedConv1D   of DilatedConv1D.neuron_typ
    | DilatedConv2D   of DilatedConv2D.neuron_typ
    | DilatedConv3D   of DilatedConv3D.neuron_typ
    | TransposeConv1D of TransposeConv1D.neuron_typ
    | TransposeConv2D of TransposeConv2D.neuron_typ
    | TransposeConv3D of TransposeConv3D.neuron_typ
    | FullyConnected  of FullyConnected.neuron_typ
    | MaxPool1D       of MaxPool1D.neuron_typ
    | MaxPool2D       of MaxPool2D.neuron_typ
    | AvgPool1D       of AvgPool1D.neuron_typ
    | AvgPool2D       of AvgPool2D.neuron_typ
    | GlobalMaxPool1D of GlobalMaxPool1D.neuron_typ
    | GlobalMaxPool2D of GlobalMaxPool2D.neuron_typ
    | GlobalAvgPool1D of GlobalAvgPool1D.neuron_typ
    | GlobalAvgPool2D of GlobalAvgPool2D.neuron_typ
    | UpSampling2D    of UpSampling2D.neuron_typ
    | Padding2D       of Padding2D.neuron_typ
    | Dropout         of Dropout.neuron_typ
    | Reshape         of Reshape.neuron_typ
    | Flatten         of Flatten.neuron_typ
    | Lambda          of Lambda.neuron_typ
    | LambdaArray     of LambdaArray.neuron_typ
    | Activation      of Activation.neuron_typ
    | GaussianNoise   of GaussianNoise.neuron_typ
    | GaussianDropout of GaussianDropout.neuron_typ
    | AlphaDropout    of AlphaDropout.neuron_typ
    | Normalisation   of Normalisation.neuron_typ
    | Add             of Add.neuron_typ
    | Mul             of Mul.neuron_typ
    | Dot             of Dot.neuron_typ
    | Max             of Max.neuron_typ
    | Average         of Average.neuron_typ
    | Concatenate     of Concatenate.neuron_typ (** Types of neuron. *)

  val get_in_out_shape : neuron -> int array * int array
  (** Get both input and output shapes of a neuron. *)

  val get_in_shape : neuron -> int array
  (** Get the input shape of a neuron. *)

  val get_out_shape : neuron -> int array
  (** Get the output shape of a neuron. *)

  val connect : int array array -> neuron -> unit
  (** Connect this neuron to others in a neural network. *)

  val init : neuron -> unit
  (** Initialise the neuron and its parameters. *)

  val reset : neuron -> unit
  (** Reset the parameters in a neuron. *)

  val mktag : int -> neuron -> unit
  (** Tag the neuron, used by ``Algodiff`` module. *)

  val mkpar : neuron -> t array
  (** Assemble all the trainable parameters in an array, used by ``Optimise`` module. *)

  val mkpri : neuron -> t array
  (** Assemble all the primal values in an array, used by ``Optimise`` module. *)

  val mkadj : neuron -> t array
  (** Assemble all the adjacent values in an array, used by ``Optimise`` module. *)

  val update : neuron -> t array -> unit
  (** Update trainable parameters in a neuron, used by ``Optimise`` module. *)

  val load_weights : neuron -> t array -> unit
  (** Load both trainable and non-trainable parameters into the neuron. *)

  val save_weights : neuron -> t array
  (** Assemble both trainable and non-trainable parameters of the neuron. *)

  val copy : neuron -> neuron
  (** Make a deep copy of the neuron and its parameters. *)

  val run : t array -> neuron -> t
  (** Execute the computation in this neuron. *)

  val to_string : neuron -> string
  (** Convert the neuron to its string representation. The string is often a summary of the parameters defined in the neuron. *)

  val to_name : neuron -> string
  (** Return the name of the neuron. *)
end