DeepBeliefNetwork in F#

DBN_example.fs

#r "Debug/Accord.dll"
#r "Debug/Accord.Math.dll"
#r "Debug/Accord.Neuro.dll"
#I "Debug"

// based on C# from http://whoopsidaisies.hatenablog.com/entry/2014/08/19/015420

open Accord.Neuro
open Accord.Neuro.Networks
open Accord.Neuro.Learning
open AForge.Neuro.Learning
open Accord.Neuro.ActivationFunctions
open Accord.Math

let inputs = [|
    [|1.;1.;1.;0.;0.;0.;|];
    [|1.;0.;1.;0.;0.;0.;|];
    [|1.;1.;1.;0.;0.;0.;|];
    [|0.;0.;1.;1.;1.;0.;|];
    [|0.;0.;1.;1.;0.;0.;|];
    [|0.;0.;1.;1.;1.;0.;|];
|]

let outputs = [|
    [|1.;0.|];
    [|1.;0.|];
    [|1.;0.|];
    [|0.;1.|];
    [|0.;1.|];
    [|0.;1.|];
|]


let network = new DeepBeliefNetwork(inputs.Length, [|4;2|])
(new GaussianWeights(network)).Randomize()
let teacher = new BackPropagationLearning(network)
[ for _ in [0 .. 5000] -> teacher.RunEpoch(inputs, outputs) ]

network.UpdateVisibleWeights()

let input = [| 1.; 1.;1.;1.;0.;0. |]
let output = network.Compute(input)
let imax = output |> Array.max |> int

printfn "%A" output