class torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda, last_epoch=-1, verbose='deprecated')[source]

Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.

  • 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.

  • verbose (bool) –

    If True, prints a message to stdout for each update. Default: False.

    Deprecated since version 2.2: verbose is deprecated. Please use get_last_lr() to access the learning rate.


>>> # 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.


Loads the schedulers state.

When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.


state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().

print_lr(is_verbose, group, lr, epoch=None)

Display the current learning rate.


Returns 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.


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