Learner

			Learner(model, lossfn; [callbacks = [], optimizer = ADAM(), kwargs...])

Holds and coordinates all state of the training. model is trained by
optimizing lossfn with optimizer on data .

Arguments

Positional arguments:

• model : A Flux.jl model or a NamedTuple of models.

• lossfn : Loss function with signature lossfn(model(x), y) -> Number .

Keyword arguments (optional):

• data = () : Data iterators. A 2-tuple will be treated as (trainingdataiter, validdataiter) .
  You can also pass in an empty tuple () and use the epoch! method with a
  dataiter as third argument.

  A data iterator is an iterable over batches. For regular supervised training,
  each batch should be a tuple (xs, ys) .

• optimizer = ADAM() : The optimizer used to update the model ’s weights

• callbacks = [] : A list of callbacks that should be used. If usedefaultcallbacks == true ,
  this will be extended by the default callbacks

• usedefaultcallbacks = true : Whether to add some basic callbacks. Included
  are Metrics , Recorder , ProgressPrinter ,
  StopOnNaNLoss , and MetricsPrinter .

• cbrunner = LinearRunner() : Callback runner to use.

Fields

(Use this as a reference when implementing callbacks)

• model , optimizer , and lossfn are stored as passed in

• data is a PropDict of data iterators, usually :training and :validation .

• params : An instance of model ’s parameters of type Flux.Params . If model is
  a NamedTuple , then params is a NamedTuple as well.

• step:: PropDict : State of the last step. Contents depend on the last run
  Phase .

• cbstate:: PropDict : Special state container that callbacks can
  save state to for other callbacks. Its keys depend on what callbacks
  are being used. See the custom callbacks guide
  for more info.

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			Learner(model, data, optimizer, lossfn, [callbacks...; kwargs...])