package tensorflow

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type _1d
type _2d
type _3d
module Shape : sig ... end
module Id : sig ... end
module Input_id : sig ... end
type 'a t
type init = [
  1. | `const of Base.float
  2. | `normal of Base.float
  3. | `truncated_normal of Base.float
]
val shape : 'a t -> 'a Shape.t
val id : _ t -> Id.t
val input : shape:'a Shape.t -> 'a t * Input_id.t
val const : Base.float -> shape:'a Shape.t -> 'a t
val sigmoid : 'a t -> 'a t
val tanh : 'a t -> 'a t
val relu : 'a t -> 'a t
val softmax : 'a t -> 'a t
val reduce_sum : 'a t -> 'a t
val square : 'a t -> 'a t
val neg : 'a t -> 'a t
val (+) : 'a t -> 'a t -> 'a t
val (-) : 'a t -> 'a t -> 'a t
val (*) : 'a t -> 'a t -> 'a t
val dense : ?w_init:init -> ?b_init:init -> ?name:Base.string -> Base.int -> _1d t -> _1d t
val conv2d : ?w_init:init -> ?b_init:init -> ?name:Base.string -> filter:(Base.int * Base.int) -> out_channels:Base.int -> strides:(Base.int * Base.int) -> padding:[ `same | `valid ] -> Base.unit -> _3d t -> _3d t
val dense' : ?w_init:init -> ?b_init:init -> ?name:Base.string -> Base.int -> (_1d t -> _1d t) Base.Staged.t
val conv2d' : ?w_init:init -> ?b_init:init -> ?name:Base.string -> filter:(Base.int * Base.int) -> out_channels:Base.int -> strides:(Base.int * Base.int) -> padding:[ `same | `valid ] -> Base.unit -> (_3d t -> _3d t) Base.Staged.t
val avg_pool : _3d t -> filter:(Base.int * Base.int) -> strides:(Base.int * Base.int) -> padding:[ `same | `valid ] -> _3d t
val max_pool : _3d t -> filter:(Base.int * Base.int) -> strides:(Base.int * Base.int) -> padding:[ `same | `valid ] -> _3d t
val reshape : _ t -> shape:'a Shape.t -> 'a t
val flatten : _ t -> _1d t
val split : _2d t -> _1d t Base.list
val concat : _1d t Base.list -> _2d t
val var : 'a t -> 'a t
module Optimizer : sig ... end
module Loss : sig ... end
module Model : sig ... end
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