LRScheduler

class LRScheduler(optimizer: Optimizer, last_epoch: int = -1)

Bases: object

Base class for all learning rate schedulers.

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

Args:

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.

Methods Summary

get_last_lr()	Get the most recent learning rates computed by this scheduler.
get_lr()	Compute the next learning rate for each of the optimizer's param_groups.
load_state_dict(state_dict)	Load the scheduler's state.
state_dict()	Return the state of the scheduler as a dict.
step([epoch])	Step the scheduler.

Methods Documentation

Parameters:

• optimizer (Optimizer)

• last_epoch (int)

get_last_lr() → list[float | Tensor]

Get the most recent learning rates computed by this scheduler.

Returns:

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

Note

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

Return type:

list[float | Tensor]

get_lr() → list[float | Tensor]

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

Returns:

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

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.

Return type:

list[float | Tensor]

load_state_dict(state_dict: dict[str, Any]) → None

Load the scheduler’s state.

Args:

state_dict (dict): scheduler state. Should be an object returned

from a call to state_dict().

Parameters:

state_dict (dict[str, Any])

Return type:

None

state_dict() → dict[str, Any]

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: int | None = None) → None

Step the scheduler.

Args:

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().

Parameters:

epoch (int | None)

Return type:

None