Training

# Training

To actually train a model we need three things:

- A objective function, that evaluates how well a model is doing given some input data.

- A collection of data points that will be provided to the objective function.

- An optimiser that will update the model parameters appropriately.

With these we can call `Flux.train!`:

    Flux.train!(objective, data, opt)

There are plenty of examples in the model zoo.

## Loss Functions

The objective function must return a number representing how far the model is from its target – the loss of the model. The `loss` function that we defined in basics will work as an objective. We can also define an objective in terms of some model:

    m = Chain(
      Dense(784, 32, σ),
      Dense(32, 10), softmax)

    loss(x, y) = Flux.mse(m(x), y)

    # later
    Flux.train!(loss, data, opt)

The objective will almost always be defined in terms of some cost function that measures the distance of the prediction `m(x)` from the target `y`. Flux has several of these built in, like `mse` for mean squared error or `crossentropy` for cross entropy loss, but you can calculate it however you want.

## Datasets

The `data` argument provides a collection of data to train with (usually a set of inputs `x` and target outputs `y`). For example, here's a dummy data set with only one data point:

    x = rand(784)
    y = rand(10)
    data = [(x, y)]

`Flux.train!` will call `loss(x, y)`, calculate gradients, update the weights and then move on to the next data point if there is one. We can train the model on the same data three times:

    data = [(x, y), (x, y), (x, y)]
    # Or equivalently
    data = Iterators.repeated((x, y), 3)

It's common to load the `x`s and `y`s separately. In this case you can use `zip`:

    xs = [rand(784), rand(784), rand(784)]
    ys = [rand( 10), rand( 10), rand( 10)]
    data = zip(xs, ys)

Note that, by default, `train!` only loops over the data once (a single "epoch"). A convenient way to run multiple epochs from the REPL is provided by `@epochs`.

    julia> using Flux: @epochs

    julia> @epochs 2 println("hello")
    INFO: Epoch 1
    hello
    INFO: Epoch 2
    hello

    julia> @epochs 2 Flux.train!(...)
    # Train for two epochs

## Callbacks

`train!` takes an additional argument, `cb`, that's used for callbacks so that you can observe the training process. For example:

    train!(objective, data, opt, cb = () -> println("training"))

Callbacks are called for every batch of training data. You can slow this down using `Flux.throttle(f, timeout)` which prevents `f` from being called more than once every `timeout` seconds.

A more typical callback might look like this:

    test_x, test_y = # ... create single batch of test data ...
    evalcb() = @show(loss(test_x, test_y))

    Flux.train!(objective, data, opt,
                cb = throttle(evalcb, 5))