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# LinearLR

class torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.3333333333333333, end_factor=1.0, total_iters=5, last_epoch=- 1, verbose=False)

  Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a pre-defined milestone: total_iters. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler. When last_epoch=-1, sets initial lr as lr.

  Parameters:

    - optimizer (Optimizer) – Wrapped optimizer.

    - start_factor (float) – The number we multiply learning rate in the first epoch. The multiplication factor changes towards end_factor in the following epochs. Default: 1./3.

    - end_factor (float) – The number we multiply learning rate at the end of linear changing process. Default: 1.0.

    - total_iters (int) – The number of iterations that multiplicative factor reaches to 1. Default: 5.

    - last_epoch (int) – The index of the last epoch. Default: -1.

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

  Example

      >>> # Assuming optimizer uses lr = 0.05 for all groups
      >>> # lr = 0.025    if epoch == 0
      >>> # lr = 0.03125  if epoch == 1
      >>> # lr = 0.0375   if epoch == 2
      >>> # lr = 0.04375  if epoch == 3
      >>> # lr = 0.05    if epoch >= 4
      >>> scheduler = LinearLR(self.opt, start_factor=0.5, total_iters=4)
      >>> 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.

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