lazy_mnist.ml

#!/usr/bin/env owl
(* This example demonstrates using lazy functor to train a model on mnist. *)

open Owl

module CPU_Engine = Owl_computation_cpu_engine.Make (Owl_algodiff_primal_ops.S)
module CGCompiler = Owl_neural_compiler.Make (CPU_Engine)

open CGCompiler.Neural
open CGCompiler.Neural.Graph
open CGCompiler.Neural.Algodiff


let make_network input_shape =
  input input_shape
  |> lambda (fun x -> Maths.(x / pack_flt 256.))
  |> conv2d [|5;5;1;32|] [|1;1|] ~act_typ:Activation.Relu
  |> max_pool2d [|2;2|] [|2;2|]
  |> dropout 0.1
  |> fully_connected 1024 ~act_typ:Activation.Relu
  |> linear 10 ~act_typ:Activation.(Softmax 1)
  |> get_network ~name:"mnist"

let pack x = CGCompiler.Engine.pack_arr x |> Algodiff.pack_arr
let unpack x = Algodiff.unpack_arr x |> CGCompiler.Engine.unpack_arr

let train network =
  let x, _, y = Dataset.load_mnist_train_data_arr () in
  let x = pack x in
  let y = pack y in
  let params = Params.config
    ~batch:(Batch.Mini 100) ~learning_rate:(Learning_Rate.Adagrad 0.005) 0.1
    (* ~momentum:(Momentum.Standard 0.1) *)
  in
  CGCompiler.train ~params network x y

let test network =
  let imgs, _, labels = Dataset.load_mnist_test_data () in
  let m = Dense.Matrix.S.row_num imgs in
  let imgs = Dense.Ndarray.S.reshape imgs [|m;28;28;1|] in
  let eval = CGCompiler.model ~batch_size:100 network in

  let mat2num x = Dense.Matrix.S.of_array (
      x |> Dense.Matrix.Generic.max_rows
        |> Array.map (fun (_,_,num) -> float_of_int num)
    ) 1 m
  in

  let result = unpack (eval (pack imgs)) in
  let pred = mat2num result in
  let fact = mat2num labels in
  let accu = Dense.Matrix.S.(elt_equal pred fact |> sum') in
  Owl_log.info "Accuracy on test set: %f" (accu /. (float_of_int m))

let () =
  Owl_log.(set_level INFO);
  let network = make_network [|28;28;1|] in
  Graph.print network; flush_all ();
  let _ = train network in
  test network