How are gradients for weights and biases calculated for individual layers?

[zgrkpnr]

Hi. I am lost after the following part.

let func = fun w x -> 
                    l.Decode w
                    l.Run x
        let w0 = l.Encode()
        let w, loss, _, lhist = Optimize.Train(func, w0, dataset, valid, par)

We send func to be optimized w.r.t dataset. But, Optimize.Train method have no idea about individual layers for FeedForward network.

In Optimize module I guess the whole network is flattened to be a single logistic regression function, hence the vector form of weights (probably I am wrong). Finally, where do biases get updated?

Not: I am deeply sorry that I ask these questions on issues board but the only person who can answer these question is the person who wrote the library. I have been trying to write Contrastive Divergence algorithm, but stuck in this particular part of Hype.