| mlr_callback_set {mlr3torch} | R Documentation |
Base Class for Callbacks
Description
Base class from which callbacks should inherit (see section Inheriting). A callback set is a collection of functions that are executed at different stages of the training loop. They can be used to gain more control over the training process of a neural network without having to write everything from scratch.
When used a in torch learner, the CallbackSet is wrapped in a TorchCallback.
The latters parameter set represents the arguments of the CallbackSet's $initialize() method.
Inheriting
For each available stage (see section Stages) a public method $on_<stage>() can be defined.
The evaluation context (a ContextTorch) can be accessed via self$ctx, which contains
the current state of the training loop.
This context is assigned at the beginning of the training loop and removed afterwards.
Different stages of a callback can communicate with each other by assigning values to $self.
State:
To be able to store information in the $model slot of a LearnerTorch, callbacks support a state API.
You can overload the $state_dict() public method to define what will be stored in learner$model$callbacks$<id>
after training finishes.
This then also requires to implement a $load_state_dict(state_dict) method that defines how to load a previously saved
callback state into a different callback.
Note that the $state_dict() should not include the parameter values that were used to initialize the callback.
For creating custom callbacks, the function torch_callback() is recommended, which creates a
CallbackSet and then wraps it in a TorchCallback.
To create a CallbackSet the convenience function callback_set() can be used.
These functions perform checks such as that the stages are not accidentally misspelled.
Stages
-
begin:: Run before the training loop begins. -
epoch_begin:: Run he beginning of each epoch. -
batch_begin:: Run before the forward call. -
after_backward:: Run after the backward call. -
batch_end:: Run after the optimizer step. -
batch_valid_begin:: Run before the forward call in the validation loop. -
batch_valid_end:: Run after the forward call in the validation loop. -
valid_end:: Run at the end of validation. -
epoch_end:: Run at the end of each epoch. -
end:: Run after last epoch. -
exit:: Run at last, usingon.exit().
Terminate Training
If training is to be stopped, it is possible to set the field $terminate of ContextTorch.
At the end of every epoch this field is checked and if it is TRUE, training stops.
This can for example be used to implement custom early stopping.
Public fields
ctx(
ContextTorchorNULL)
The evaluation context for the callback. This field should always beNULLexcept during the$train()call of the torch learner.
Active bindings
stages(
character())
The active stages of this callback set.
Methods
Public methods
Method print()
Prints the object.
Usage
CallbackSet$print(...)
Arguments
...(any)
Currently unused.
Method state_dict()
Returns information that is kept in the the LearnerTorch's state after training.
This information should be loadable into the callback using $load_state_dict() to be able to continue training.
This returns NULL by default.
Usage
CallbackSet$state_dict()
Method load_state_dict()
Loads the state dict into the callback to continue training.
Usage
CallbackSet$load_state_dict(state_dict)
Arguments
state_dict(any)
The state dict as retrieved via$state_dict().
Method clone()
The objects of this class are cloneable with this method.
Usage
CallbackSet$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other Callback:
TorchCallback,
as_torch_callback(),
as_torch_callbacks(),
callback_set(),
mlr3torch_callbacks,
mlr_callback_set.checkpoint,
mlr_callback_set.progress,
mlr_context_torch,
t_clbk(),
torch_callback()