NN.jl

include("NNData.jl")
include("RealNames.jl")

using CUDAnative
using Flux
using Flux.Data
using Flux: crossentropy, normalise, onecold, onehotbatch, data
using Statistics: mean

function TrainNN()
    model = Chain(RNN(2371, length(GetRealNames())), softmax)

    model = gpu(model)

    loss(x, y) = crossentropy(model(x), y)

    optimiser = ADAM(0.01)

    datas = zip(gpu(_trainDataX), gpu(_trainDataY))

    for e = 0:4
        println("Epoch: $e")

        Flux.train!(loss, params(model), datas, optimiser)
    end

    CalcAccuracy(model)
end

function CalcAccuracy(nn)
    accuracy_buffer = Vector{Int64}()

    for one in _testDataX
        push!(
            accuracy_buffer,
            findfirst(isequal(max(cpu(nn(one))...)), cpu(nn(one))),
        )
    end

    accuracy_mean = 0.0

    for one in accuracy_buffer
        if one == findfirst(isequal(1), _testDataY)
            accuracy_mean += 1
        else
            accuracy_mean += 0
        end
    end

    accuracy_score = accuracy_mean / length(_testDataY)

    println("\nAccuracy: $accuracy_score")
end