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Create a new callback


  name = NULL,
  private = NULL,
  active = NULL,
  parent_env = parent.frame(),
  inherit = NULL



name of the callback


Public methods of the callback. The name of the methods is used to know how they should be called. See the details section.


An optional list of private members, which can be functions and non-functions.


An optional list of active binding functions.


An environment to use as the parent of newly-created objects.


A R6ClassGenerator object to inherit from; in other words, a superclass. This is captured as an unevaluated expression which is evaluated in parent_env each time an object is instantiated.


A luz_callback that can be passed to fit.luz_module_generator().


Let’s implement a callback that prints ‘Iteration n’ (where n is the iteration number) for every batch in the training set and ‘Done’ when an epoch is finished. For that task we use the luz_callback function:

print_callback <- luz_callback(
  name = "print_callback",
  initialize = function(message) {
    self$message <- message
  on_train_batch_end = function() {
    cat("Iteration ", ctx$iter, "\n")
  on_epoch_end = function() {
    cat(self$message, "\n")

luz_callback() takes named functions as ... arguments, where the name indicates the moment at which the callback should be called. For instance on_train_batch_end() is called for every batch at the end of the training procedure, and on_epoch_end() is called at the end of every epoch.

The returned value of luz_callback() is a function that initializes an instance of the callback. Callbacks can have initialization parameters, like the name of a file where you want to log the results. In that case, you can pass an initialize method when creating the callback definition, and save these parameters to the self object. In the above example, the callback has a message parameter that is printed at the end of each epoch.

Once a callback is defined it can be passed to the fit function via the callbacks parameter:

fitted <- net %>%
  setup(...) %>%
  fit(..., callbacks = list(
    print_callback(message = "Done!")

Callbacks can be called in many different positions of the training loop, including combinations of them. Here’s an overview of possible callback breakpoints:

Start Fit
   - on_fit_begin
  Start Epoch Loop
     - on_epoch_begin
    Start Train
       - on_train_begin
      Start Batch Loop
         - on_train_batch_begin
          Start Default Training Step
            - on_train_batch_after_pred
            - on_train_batch_after_loss
            - on_train_batch_before_backward
            - on_train_batch_before_step
            - on_train_batch_after_step
          End Default Training Step:
         - on_train_batch_end
      End Batch Loop
       - on_train_end
    End Train
    Start Valid
       - on_valid_begin
      Start Batch Loop
         - on_valid_batch_begin
          Start Default Validation Step
            - on_valid_batch_after_pred
            - on_valid_batch_after_loss
          End Default Validation Step
         - on_valid_batch_end
      End Batch Loop
       - on_valid_end
    End Valid
      - on_epoch_end
  End Epoch Loop
   - on_fit_end
End Fit

Every step market with on_* is a point in the training procedure that is available for callbacks to be called.

The other important part of callbacks is the ctx (context) object. See help("ctx") for details.

By default, callbacks are called in the same order as they were passed to fit (or predict or evaluate), but you can provide a weight attribute that will control the order in which it will be called. For example, if one callback has weight = 10 and another has weight = 1, then the first one is called after the second one. Callbacks that don’t specify a weight attribute are considered weight = 0. A few built-in callbacks in luz already provide a weight value. For example, the ?luz_callback_early_stopping has a weight of Inf, since in general we want to run it as the last thing in the loop.


print_callback <- luz_callback(
 name = "print_callback",
 on_train_batch_end = function() {
   cat("Iteration ", ctx$iter, "\n")
 on_epoch_end = function() {