package owl

  1. Overview
  2. Docs
package owl
Legend:
Library
Module
Module type
Parameter
Class
Class type
Utils module
module Utils : sig ... end
Learning_Rate module
module Learning_Rate : sig ... end
Batch module
module Batch : sig ... end
Loss module
module Loss : sig ... end
Gradient module
module Gradient : sig ... end
Momentum module
module Momentum : sig ... end
Regularisation module
module Regularisation : sig ... end
Clipping module
module Clipping : sig ... end
Stopping module
module Stopping : sig ... end
Checkpoint module
module Checkpoint : sig ... end
Params module
module Params : sig ... end
Core functions

This function minimises the weight ``w`` of passed-in function ``f``.

* ``f`` is a function ``f : w -> x -> y``. * ``w`` is a row vector but ``y`` can have any shape.

val minimise_network : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t * Algodiff.t array array) -> (Algodiff.t -> Algodiff.t array array * Algodiff.t array array) -> (Algodiff.t array array -> unit) -> (string -> unit) -> Algodiff.t -> Algodiff.t -> Checkpoint.state

This function is specifically designed for minimising the weights in a neural network of graph structure. In Owl's earlier versions, the functions in the regression module were actually implemented using this function.

This function minimises ``f : x -> y`` w.r.t ``x``.

``x`` is an ndarray; and ``y`` is an scalar value.

val minimise_compiled_network : ?state:Checkpoint.state -> Params.typ -> (Algodiff.t -> Algodiff.t -> Algodiff.t) -> (unit -> unit) -> (string -> unit) -> Algodiff.t -> Algodiff.t -> Checkpoint.state

TODO

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