oneflow.optim.lr_scheduler.ExponentialLR

class oneflow.optim.lr_scheduler.ExponentialLR(optimizer: oneflow.optim.optimizer.Optimizer, gamma: float, last_step: int = - 1, verbose: bool = False)

Decays the learning rate of each parameter group by gamma every epoch. When last_epoch=-1, sets initial lr as lr.

Parameters
  • optimizer (Optimizer) – Wrapped optimizer.

  • gamma (float) – Multiplicative factor of learning rate decay.

  • last_step (int) – The index of last step. Default: -1.

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

__init__(optimizer: oneflow.optim.optimizer.Optimizer, gamma: float, last_step: int = - 1, verbose: bool = False)

Initialize self. See help(type(self)) for accurate signature.

Methods

__delattr__(name, /)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(optimizer, gamma[, last_step, verbose])

Initialize self.

__init_subclass__

This method is called when a class is subclassed.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__ne__(value, /)

Return self!=value.

__new__(**kwargs)

Create and return a new object.

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__subclasshook__

Abstract classes can override this to customize issubclass().

_generate_conf_for_graph(lr_conf)

_init_base_lrs()

get_last_lr()

Return last computed learning rate by current scheduler.

get_lr(base_lr, step)

Compute learning rate using chainable form of the scheduler

load_state_dict(state_dict)

Load the schedulers state.

print_lr(group, lr)

Display the current learning rate.

state_dict()

Return the state of the scheduler as a dict.

step()

update_lrs(lrs)