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)