package owl-base

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package owl-base
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Module
Module type
Parameter
Class
Class type

Parameters

Signature

include sig ... end
module Neuron : sig ... end
type node = Owl_neural_graph.Make(Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)))).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_graph.Make(Owl_neural_neuron.Make(Owl_optimise_generic.Make(Owl_algodiff_generic.Make(A)))).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 input_shape : network -> int array
val input_shapes : network -> int array array
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 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 _remove_training_nodes : network -> unit
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 : 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
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