LambdaLR — PyTorch 2.7 documentation (original) (raw)
class torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1)[source][source]¶
Sets the initial learning rate.
The learning rate of each parameter group is set to the initial lr times a given function. When last_epoch=-1, sets 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
Assuming optimizer has two groups.
lambda1 = lambda epoch: epoch // 30 lambda2 = lambda epoch: 0.95 ** epoch scheduler = LambdaLR(optimizer, lr_lambda=[lambda1, lambda2]) for epoch in range(100): train(...) validate(...) scheduler.step()
Return last computed learning rate by current scheduler.
Return type
Compute learning rate.
load_state_dict(state_dict)[source][source]¶
Load the scheduler’s state.
When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.
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.
When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.
Perform a step.