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# ChainedScheduler

class torch.optim.lr_scheduler.ChainedScheduler(schedulers)

  Chains list of learning rate schedulers. It takes a list of chainable learning rate schedulers and performs consecutive step() functions belonging to them by just one call.

  Parameters:

    schedulers (list) – List of chained schedulers.

  Example

      >>> # Assuming optimizer uses lr = 1. for all groups
      >>> # lr = 0.09     if epoch == 0
      >>> # lr = 0.081    if epoch == 1
      >>> # lr = 0.729    if epoch == 2
      >>> # lr = 0.6561   if epoch == 3
      >>> # lr = 0.59049  if epoch >= 4
      >>> scheduler1 = ConstantLR(self.opt, factor=0.1, total_iters=2)
      >>> scheduler2 = ExponentialLR(self.opt, gamma=0.9)
      >>> scheduler = ChainedScheduler([scheduler1, scheduler2])
      >>> 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 wrapped scheduler states will also be saved.

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