Learning Rate Scheduling Callback

Changes the learning rate based on the schedule specified by a torch::lr_scheduler.

As of this writing, the following are available:

torch::lr_cosine_annealing()
torch::lr_lambda()
torch::lr_multiplicative()
torch::lr_one_cycle() (where the default values for epochs and steps_per_epoch are the number of training epochs and the number of batches per epoch)
torch::lr_reduce_on_plateau()
torch::lr_step()
Custom schedulers defined with torch::lr_scheduler().

Super class

mlr3torch::CallbackSet -> CallbackSetLRScheduler

scheduler_fn: (lr_scheduler_generator)
The torch function that creates a learning rate scheduler
scheduler: (LRScheduler)
The learning rate scheduler wrapped by this callback

Inherited methods

Creates a new instance of this R6 class.

CallbackSetLRScheduler$new(.scheduler, step_on_epoch, ...)

.scheduler: (lr_scheduler_generator)
The torch scheduler generator (e.g. torch::lr_step).
step_on_epoch: (logical(1))
Whether the scheduler steps after every epoch (otherwise every batch).
...: (any)
The scheduler-specific initialization arguments.

Creates the scheduler using the optimizer from the context

CallbackSetLRScheduler$on_begin()

The objects of this class are cloneable with this method.

CallbackSetLRScheduler$clone(deep = FALSE)