To actually train a model we need three things:

With these we can call Flux.train!:

Flux.train!(objective, params, 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)
ps = Flux.params(m)

# later
Flux.train!(loss, ps, 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.


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 xs and ys 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
INFO: Epoch 2

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


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

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

Calling Flux.stop() in a callback will exit the training loop early.

cb = function ()
  accuracy() > 0.9 && Flux.stop()