oneflow.optim.lr_scheduler.CosineDecayLR

class oneflow.optim.lr_scheduler.CosineDecayLR(optimizer: oneflow.optim.optimizer.Optimizer, decay_steps: int, alpha: float = 0.0, last_step: int = - 1, verbose: bool = False)

This operator creates a Cosine decayed learning rate scheduler.

Before the decay_steps are specified by user, the learning rate will be updated as:

\[ \begin{align}\begin{aligned}& cos\_decay = 0.5*(1+cos(\pi*\frac{current\_step}{decay\_steps}))\\& decay\_factor = (1-\alpha)*cos\_decay+\alpha\\& learning\_rate = base\_learning\_rate*decay\_factor\end{aligned}\end{align} \]

After the decay_steps specified by user, the learning rate will be :

\[learning\_rate = {base\_learning\_rate}*{\alpha}\]

It has been proposed in SGDR: Stochastic Gradient Descent with Warm Restarts. Note that this only implements the cosine annealing part of SGDR, and not the restarts.

Parameters
  • optimizer (Optimizer) – Wrapped optimizer.

  • decay_steps (int) – The decay steps in the scheduler.

  • alpha (float, optional) – The learning rate scale factor (\(\alpha\)). (default: 0.0)

  • last_step (int, optional) – The index of last step. (default: -1)

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

For example:

import oneflow as flow

...
cosine_decay_lr = flow.optim.lr_scheduler.CosineDecayLR(optimizer, decay_steps=100, alpha=0.0)
for epoch in range(num_epoch):
    train(...)
    cosine_decay_lr.step()
__init__(optimizer: oneflow.optim.optimizer.Optimizer, decay_steps: int, alpha: float = 0.0, 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, decay_steps[, alpha, …])

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)