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LRScheduler
- class tensorplay.optim.lr_scheduler.LRScheduler(optimizer: Optimizer, last_epoch: int = -1)[source]
Base class for all learning rate schedulers.
Subclasses implement
get_lr()and optionally overridestep()to define scheduling behavior.- Parameters:
Warning
Initializing a scheduler overwrites its optimizer’s
param_group["lr"]s. When restoring a checkpoint, initialize the scheduler before calling your optimizer’sload_state_dict()to avoid overwriting the loaded learning rates.- get_last_lr() list[float | TensorBase][source]
Get the most recent learning rates computed by this scheduler.
- Returns:
A
listof learning rates with entries for each of the optimizer’sparam_groups, with the same types as theirgroup["lr"]s.- Return type:
Note
The returned
Tensors are copies, and never alias the optimizer’sgroup["lr"]s.
- get_lr() list[float | TensorBase][source]
Compute the next learning rate for each of the optimizer’s
param_groups.- Returns:
A
listof learning rates for each of the optimizer’sparam_groupswith the same types as their currentgroup["lr"]s.- Return type:
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’sgroup["lr"]s.
- load_state_dict(state_dict: dict[str, Any]) None[source]
Load the scheduler’s state.
- Parameters:
state_dict (dict) – scheduler state. Should be an object returned from a call to
state_dict().
- state_dict() dict[str, Any][source]
Return the state of the scheduler as a
dict.It contains an entry for every variable in
self.__dict__which is not the optimizer.
- step(epoch: int | None = None) None[source]
Step the scheduler.
- Parameters:
epoch (int, optional) –
Deprecated since version 1.4: If provided, sets
last_epochtoepochand uses_get_closed_form_lr()if it is available. This is not universally supported. Usestep()without arguments instead.
Note
Call this method after calling the optimizer’s
step().
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