LRScheduler

class torch.optim.lr_scheduler.LRScheduler(optimizer, last_epoch=-1)

Base class for all learning rate schedulers.

Subclasses implement get_lr() and optionally override step() to define scheduling behavior.

Parameters

• optimizer (Optimizer) – The optimizer this scheduler will adjust the learning rates of.

• last_epoch (int) – Index of the last epoch seen by the scheduler. Use -1 (default) to initialize the scheduler. Only use a non-default value when restoring this scheduler from a saved checkpoint.

Warning

Initializing a scheduler overwrites its optimizer’s param_group["lr"]s. When restoring a checkpoint, initialize the scheduler before calling your optimizer’s load_state_dict() to avoid overwriting the loaded learning rates.

get_last_lr()

Get the most recent learning rates computed by this scheduler.

Returns

A list of learning rates with entries for each of the optimizer’s param_groups, with the same types as their group["lr"]s.

Return type

list[float | Tensor]

Note

The returned Tensors are copies, and never alias the optimizer’s group["lr"]s.

get_lr()

Compute the next learning rate for each of the optimizer’s param_groups.

Returns

A list of learning rates for each of the optimizer’s param_groups with the same types as their current group["lr"]s.

Return type

list[float | Tensor]

Note

If you’re trying to inspect the most recent learning rate, use get_last_lr() instead.

Note

The returned Tensors are copies, and never alias the optimizer’s group["lr"]s.

load_state_dict(state_dict)

Load the scheduler’s state.

Parameters

state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().

state_dict()

Return the state of the scheduler as a dict.

It contains an entry for every variable in self.__dict__ which is not the optimizer.

Return type

dict[str, Any]

step(epoch=None)

Step the scheduler.

Parameters

epoch (int, optional) –

Deprecated since version 1.4: If provided, sets last_epoch to epoch and uses _get_closed_form_lr() if it is available. This is not universally supported. Use step() without arguments instead.

Note

Call this method after calling the optimizer’s step().