package owl-base

  1. Overview
  2. Docs
package owl-base
Legend:
Library
Module
Module type
Parameter
Class
Class type
module Neuron : sig ... end
type node = Owl_neural_generic.Make_Embedded(Engine).node = {
  1. mutable name : string;
  2. mutable prev : node array;
  3. mutable next : node array;
  4. mutable neuron : Neuron.neuron;
  5. mutable output : Neuron.Optimise.Algodiff.t option;
  6. mutable network : network;
  7. mutable train : bool;
}
and network = Owl_neural_generic.Make_Embedded(Engine).network = {
  1. mutable nnid : string;
  2. mutable size : int;
  3. mutable roots : node array;
  4. mutable outputs : node array;
  5. mutable topo : node array;
}
val make_network : ?nnid:string -> int -> node array -> node array -> network
val make_node : ?name:string -> ?train:bool -> node array -> node array -> Neuron.neuron -> Neuron.Optimise.Algodiff.t option -> network -> node
val get_roots : network -> node array
val get_outputs : network -> node array
val get_node : network -> string -> node
val get_network : ?name:string -> node -> network
val outputs : ?name:string -> node array -> network
val get_network_name : network -> string
val set_network_name : network -> string -> unit
val collect_output : node array -> Neuron.Optimise.Algodiff.t array
val connect_pair : node -> node -> unit
val connect_to_parents : node array -> node -> unit
val add_node : ?act_typ:Neuron.Activation.typ -> network -> node array -> node -> node
val input_shape : network -> int array
val input_shapes : network -> int array array
val init : network -> unit
val reset : network -> unit
val mktag : int -> network -> unit
val mkpar : network -> Neuron.Optimise.Algodiff.t array array
val mkpri : network -> Neuron.Optimise.Algodiff.t array array
val mkadj : network -> Neuron.Optimise.Algodiff.t array array
val update : network -> Neuron.Optimise.Algodiff.t array array -> unit
val copy : network -> network
val input : ?name:string -> int array -> node
val inputs : ?names:string array -> int array array -> node array
val activation : ?name:string -> Neuron.Activation.typ -> node -> node
val linear : ?name:string -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int -> node -> node
val linear_nobias : ?name:string -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int -> node -> node
val embedding : ?name:string -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int -> int -> node -> node
val recurrent : ?name:string -> ?init_typ:Neuron.Init.typ -> act_typ:Neuron.Activation.typ -> int -> int -> node -> node
val lstm : ?name:string -> ?init_typ:Neuron.Init.typ -> int -> node -> node
val gru : ?name:string -> ?init_typ:Neuron.Init.typ -> int -> node -> node
val conv1d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val conv2d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val conv3d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val dilated_conv1d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> int array -> node -> node
val dilated_conv2d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> int array -> node -> node
val dilated_conv3d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> int array -> node -> node
val transpose_conv1d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val transpose_conv2d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val transpose_conv3d : ?name:string -> ?padding:Owl_types.padding -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val fully_connected : ?name:string -> ?init_typ:Neuron.Init.typ -> ?act_typ:Neuron.Activation.typ -> int -> node -> node
val max_pool1d : ?name:string -> ?padding:Owl_types.padding -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val max_pool2d : ?name:string -> ?padding:Owl_types.padding -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val avg_pool1d : ?name:string -> ?padding:Owl_types.padding -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val avg_pool2d : ?name:string -> ?padding:Owl_types.padding -> ?act_typ:Neuron.Activation.typ -> int array -> int array -> node -> node
val global_max_pool1d : ?name:string -> ?act_typ:Neuron.Activation.typ -> node -> node
val global_max_pool2d : ?name:string -> ?act_typ:Neuron.Activation.typ -> node -> node
val global_avg_pool1d : ?name:string -> ?act_typ:Neuron.Activation.typ -> node -> node
val global_avg_pool2d : ?name:string -> ?act_typ:Neuron.Activation.typ -> node -> node
val upsampling2d : ?name:string -> ?act_typ:Neuron.Activation.typ -> int array -> node -> node
val padding2d : ?name:string -> ?act_typ:Neuron.Activation.typ -> int array array -> node -> node
val dropout : ?name:string -> float -> node -> node
val gaussian_noise : ?name:string -> float -> node -> node
val gaussian_dropout : ?name:string -> float -> node -> node
val alpha_dropout : ?name:string -> float -> node -> node
val normalisation : ?name:string -> ?axis:int -> ?training:bool -> ?decay:float -> ?mu:Neuron.Optimise.Algodiff.A.arr -> ?var:Neuron.Optimise.Algodiff.A.arr -> node -> node
val reshape : ?name:string -> int array -> node -> node
val flatten : ?name:string -> node -> node
val slice : ?name:string -> int list list -> node -> node
val lambda : ?name:string -> ?act_typ:Neuron.Activation.typ -> ?out_shape:int array -> (Neuron.Optimise.Algodiff.t -> Neuron.Optimise.Algodiff.t) -> node -> node
val lambda_array : ?name:string -> ?act_typ:Neuron.Activation.typ -> int array -> (Neuron.Optimise.Algodiff.t array -> Neuron.Optimise.Algodiff.t) -> node array -> node
val add : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node
val mul : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node
val dot : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node
val max : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node
val average : ?name:string -> ?act_typ:Neuron.Activation.typ -> node array -> node
val concatenate : ?name:string -> ?act_typ:Neuron.Activation.typ -> int -> node array -> node
val to_string : network -> string
val pp_network : Stdlib.Format.formatter -> network -> unit
val print : network -> unit
val save : ?unsafe:bool -> network -> string -> unit
val load : string -> network
val save_weights : network -> string -> unit
val load_weights : network -> string -> unit
val make_subnetwork : ?copy:bool -> ?make_inputs:string array -> network -> string array -> network
OCaml

Innovation. Community. Security.

GitHub Discord Twitter Peertube RSS
About
Changelog Releases Industrial Users Academic Users Why OCaml
Ecosystem
Packages Community Events OCaml Planet Jobs
Resources
Install OCaml Get Started Platform Tools Language Manual Standard Library API Books Exercises Papers OCaml Playground Logo
Policies
Carbon Footprint Governance Privacy Code of Conduct