oneflow.optim.lr_scheduler.LinearLR

class oneflow.optim.lr_scheduler.LinearLR(optimizer: oneflow.optim.optimizer.Optimizer, start_factor: float = 0.3333333333333333, end_factor: float = 1.0, total_iters: int = 5, last_step: int = - 1, verbose: bool = False)

Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the number of step reaches a pre-defined milestone: total_iters.

Parameters

• optimizer (Optimizer) – Wrapped optimizer.

• start_factor (float) – The number we multiply learning rate in the first step. The multiplication factor changes towards end_factor in the following steps. 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_step (int) – The index of the last step. 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 step == 0
>>> # lr = 0.03125  if step == 1
>>> # lr = 0.0375   if step == 2
>>> # lr = 0.04375  if step == 3
>>> # lr = 0.05    if step >= 4
>>> scheduler = LinearLR(self.opt, start_factor=0.5, total_iters=4)
>>> for step in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

__init__(optimizer: oneflow.optim.optimizer.Optimizer, start_factor: float = 0.3333333333333333, end_factor: float = 1.0, total_iters: int = 5, 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[, start_factor, …])	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)