Shortcuts

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.

load_state_dict(state_dict)[source]

Loads the schedulers state.

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.

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources