1D GAN with Flux

Specific Domains Machine Learning
5

Thanks a lot for the reply. Yet, the generator still does not replicate like the example here. So, here is my updated code:

loss_D(x, y, dscr) = sum(Flux.Losses.logitbinarycrossentropy(dscr(x), y))

function trainDiscriminator!(dscr,gen,train_size)
    real = generate_real_data(train_size)
    fake = gen(rand(5,train_size))

    X    = hcat(real,fake)
    Y    = vcat(ones(train_size),zeros(train_size))
    data = Flux.Data.DataLoader(X, Y', batchsize=128,shuffle=true);
    for d in data
        gs = gradient(Flux.params(dscr)) do
            l = loss_D(d...,dscr)
        end
        Flux.update!(opt, Flux.params(dscr), gs)
    end
end

loss_G(z,gen,dscr) = sum(Flux.Losses.logitbinarycrossentropy(dscr(gen(z)),1))

function trainGenerator!(gen,dscr,train_size)
    noise = rand(5,train_size)
    data = Flux.Data.DataLoader(noise, batchsize=128,shuffle=true);
    for d in data
        gs = gradient(Flux.params(gen)) do
            l = loss_G(d,gen,dscr)
        end
        Flux.update!(opt, Flux.params(gen), gs)
    end
    fake_generated = gen(rand(5,train_size))
end