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Changes the learning rate based on the 1cycle learning rate policy.

Wraps 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.

Resuming

As for CallbackSetLRScheduler, with one additional restriction: the 1cycle policy is defined over the total number of steps of the run, so a resumed run must be configured for the same number of steps as the one that wrote the checkpoint, i.e. the same epochs and the same number of batches per epoch. A run that is not errors before its first epoch rather than somewhere in the middle.

Super classes

CallbackSet -> CallbackSetLRScheduler -> CallbackSetLRSchedulerOneCycle

Methods

Inherited methods


CallbackSetLRSchedulerOneCycle$new()

Creates a new instance of this R6 class.

Arguments

...

(any)
The scheduler-specific initialization arguments.


CallbackSetLRSchedulerOneCycle$on_begin()

Creates the scheduler using the optimizer from the context

Usage

CallbackSetLRSchedulerOneCycle$on_begin()


CallbackSetLRSchedulerOneCycle$clone()

The objects of this class are cloneable with this method.

Usage

CallbackSetLRSchedulerOneCycle$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.