# MultiplicativeLR¶

class torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_lambda, last_epoch=- 1, verbose=False)[source]

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

• verbose (bool) – If True, prints a message to stdout for each update. Default: False.

Example

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

get_last_lr()

Return last computed learning rate by current scheduler.

Parameters:

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.

state_dict()[source]

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.