train!

			train!(loss, pars::Params, data, opt::AbstractOptimiser; [cb])

Uses a loss function and training data to improve the model's parameters according to a particular optimisation rule opt.

For each d in data, first the gradient of the loss is computed like this:

			    gradient(() -> loss(d...), pars)  # if d isa Tuple
    gradient(() -> loss(d), pars)     # otherwise

Here pars is produced by calling Flux.params on your model. (Or just on the layers you want to train, like train!(loss, params(model[1:end-2]), data, opt).) This is the "implicit" style of parameter handling.

This gradient is then used by optimiser opt to update the parameters:

			    update!(opt, pars, grads)

The optimiser should be from the Flux.Optimise module (see Optimisers). Different optimisers can be combined using [ Flux.Optimise.Optimiser]( Flux.Optimiser).

This training loop iterates through data once. It will stop with a DomainError if the loss is NaN or infinite.

You can use @epochs to do this several times, or use for instance Itertools.ncycle to make a longer data iterator.

Callbacks

Callbacks are given with the keyword argument cb. For example, this will print "training" every 10 seconds (using Flux.throttle):

			    train!(loss, params, data, opt, cb = throttle(() -> println("training"), 10))

The callback can call Flux.stop to interrupt the training loop.

Multiple callbacks can be passed to cb as array.