Table of Contents
Shortcuts

# PolynomialLR

class torch.optim.lr_scheduler.PolynomialLR(optimizer, total_iters=5, power=1.0, last_epoch=- 1, verbose=False)

  Decays the learning rate of each parameter group using a polynomial function in the given total_iters. When last_epoch=-1, sets initial lr as lr.

  Parameters:

    - optimizer (Optimizer) – Wrapped optimizer.

    - total_iters (int) – The number of steps that the scheduler decays the learning rate. Default: 5.

    - power (int) – The power of the polynomial. Default: 1.0.

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

  Example

      >>> # Assuming optimizer uses lr = 0.001 for all groups
      >>> # lr = 0.001     if epoch == 0
      >>> # lr = 0.00075   if epoch == 1
      >>> # lr = 0.00050   if epoch == 2
      >>> # lr = 0.00025   if epoch == 3
      >>> # lr = 0.0       if epoch >= 4
      >>> scheduler = PolynomialLR(self.opt, total_iters=4, power=1.0)
      >>> 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.

## 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

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.