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# MultiplicativeLR

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

  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)

    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()

    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.

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