MultiplicativeLR

class tensorplay.optim.lr_scheduler.MultiplicativeLR(optimizer: Optimizer, lr_lambda: Callable[[int], float] | list[Callable[[int], float]], last_epoch: int = -1)[source]

Multiply the learning rate of each parameter group by the factor given in the specified function.

When last_epoch=-1, set initial lr as lr.

Parameters:

• optimizer (Optimizer) – Wrapped optimizer.

• lr_lambda (function or list) – A function which computes a multiplicative factor given an integer parameter epoch, or a list of such functions, one for each group in optimizer.param_groups.

• last_epoch (int) – The index of last epoch. Default: -1.

Example

>>> # xdoctest: +SKIP
>>> lmbda = lambda epoch: 0.95
>>> scheduler = MultiplicativeLR(optimizer, lr_lambda=lmbda)
>>> for epoch in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

get_last_lr() → list[float | TensorBase]

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() → list[float | TensorBase][source]

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

Scales the current group["lr"]s in each of the optimizer’s param_groups by the outputs of the lr_lambdas at last_epoch.

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: 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. The learning rate lambda functions will only be saved if they are callable objects and not if they are functions or lambdas.

step(epoch: int | None = None) → 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().