MultiplicativeLR — PyTorch 2.7 documentation (original) (raw)
class torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_lambda, last_epoch=-1)[source][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
lmbda = lambda epoch: 0.95 scheduler = MultiplicativeLR(optimizer, lr_lambda=lmbda) for epoch in range(100): train(...) validate(...) scheduler.step()
Return last computed learning rate by current scheduler.
Return type
Compute the learning rate of each parameter group.
load_state_dict(state_dict)[source][source]¶
Load the scheduler’s state.
Parameters
state_dict (dict) – scheduler state. Should be an object returned from a call to 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. The learning rate lambda functions will only be saved if they are callable objects and not if they are functions or lambdas.
Perform a step.