package owl

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package owl
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type node = {
  1. mutable id : int;
  2. mutable prev : node array;
  3. mutable next : node array;
  4. mutable neuron : Owl_neural_neuron.neuron;
  5. mutable output : Owl_neural_neuron.t option;
  6. mutable network : network;
}
and network = {
  1. mutable size : int;
  2. mutable root : node option;
  3. mutable topo : node array;
}
val topological_sort : 'a -> 'a option array
val bfs_iter : (node -> 'a) -> node list -> unit
val bfs_map : (node -> 'a) -> node list -> 'a array
val bfs_array : node list -> node array
val make_network : int -> node option -> node array -> network
val make_node : int -> node array -> node array -> Owl_neural_neuron.neuron -> Owl_neural_neuron.t option -> network -> node
val get_root : network -> node
val get_network : node -> network
val collect_output : node array -> Owl_neural_neuron.t array
val connect_pair : node -> node -> unit
val connect_to_parents : node array -> node -> unit
val add_node : ?act_typ:Owl_neural_neuron.Activation.typ -> network -> node array -> node -> node
val init : network -> unit
val reset : network -> unit
val mktag : int -> network -> unit
val mkpar : network -> Owl_neural_neuron.t array array
val mkpri : network -> Owl_algodiff.S.t array array
val mkadj : network -> Owl_algodiff.S.t array array
val update : network -> Owl_algodiff.S.t array array -> unit
val backward : network -> Owl_algodiff.S.t -> Owl_algodiff.S.t array array * Owl_algodiff.S.t array array
val input : int array -> node
val linear : ?init_typ:Owl_neural_neuron.Init.typ -> ?act_typ:Owl_neural_neuron.Activation.typ -> int -> node -> node
val linear_nobias : ?init_typ:Owl_neural_neuron.Init.typ -> ?act_typ:Owl_neural_neuron.Activation.typ -> int -> node -> node
val recurrent : ?init_typ:Owl_neural_neuron.Init.typ -> act_typ:Owl_neural_neuron.Activation.typ -> int -> int -> node -> node
val lstm : int -> node -> node
val gru : int -> node -> node
val conv2d : ?padding:Owl_algodiff.S.padding -> ?act_typ:Owl_neural_neuron.Activation.typ -> int array -> int array -> node -> node
val conv3d : ?padding:Owl_algodiff.S.padding -> ?act_typ:Owl_neural_neuron.Activation.typ -> 'a -> int array -> int array -> node -> node
val fully_connected : ?init_typ:Owl_neural_neuron.Init.typ -> ?act_typ:Owl_neural_neuron.Activation.typ -> int -> node -> node
val max_pool2d : ?padding:Owl_algodiff.S.padding -> ?act_typ:Owl_neural_neuron.Activation.typ -> int array -> int array -> node -> node
val avg_pool2d : ?padding:Owl_algodiff.S.padding -> ?act_typ:Owl_neural_neuron.Activation.typ -> int array -> int array -> node -> node
val dropout : float -> node -> node
val reshape : ?convert:bool -> int array -> node -> node
val flatten : ?convert:bool -> node -> node
val add : ?act_typ:Owl_neural_neuron.Activation.typ -> node array -> node
val mul : ?act_typ:Owl_neural_neuron.Activation.typ -> node array -> node
val max : ?act_typ:Owl_neural_neuron.Activation.typ -> node array -> node
val average : ?act_typ:Owl_neural_neuron.Activation.typ -> node array -> node
val to_string : network -> string
val print : network -> unit
val save : 'a -> string -> unit
val load : string -> network
val train_generic : ?params:Owl_neural_optimise.Params.typ -> ?init_model:bool -> network -> Owl_algodiff.S.t -> Owl_algodiff.S.t -> Owl_algodiff.S.elt array
val train : ?params:Owl_neural_optimise.Params.typ -> ?init_model:bool -> network -> Owl_algodiff.S.mat -> Owl_algodiff.S.mat -> Owl_algodiff.S.elt array
val train_cnn : ?params:Owl_neural_optimise.Params.typ -> ?init_model:bool -> network -> Owl_algodiff.S.arr -> Owl_algodiff.S.mat -> Owl_algodiff.S.elt array