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in-package search v0.2.0
tiny_libs.ai_learning
Net
type activation =
| Sigmoid
| Tanh
| Relu
| Linear
val squash : activation -> float -> float
val slope : activation -> z:float -> a:float -> float
type layer = {
w : Matrix.t;
b : Matrix.t;
f : activation;
}
type t = layer list
val make : seed:int -> ?hidden:activation -> ?last:activation -> int list -> t
val forward : t -> float array -> float array
val sizes : t -> int list
val weights : t -> int
type pass = {
input : Matrix.t;
steps : (Matrix.t * Matrix.t) list;
val forward_pass : t -> float array -> pass
val output : pass -> float array