torch.optim.lr_scheduler.LambdaLR Class Reference

Inheritance diagram for torch.optim.lr_scheduler.LambdaLR:

Public Member Functions
def	__init__ (self, optimizer, lr_lambda, last_epoch=-1)
def	state_dict (self)
def	load_state_dict (self, state_dict)
def	get_lr (self)
Public Member Functions inherited from torch.optim.lr_scheduler._LRScheduler
def	__init__ (self, optimizer, last_epoch=-1)
def	state_dict (self)
def	load_state_dict (self, state_dict)
def	get_lr (self)
def	step (self, epoch=None)

Public Attributes
	optimizer
	lr_lambdas
	last_epoch
Public Attributes inherited from torch.optim.lr_scheduler._LRScheduler
	optimizer
	base_lrs
	last_epoch

Detailed Description

Sets the learning rate of each parameter group to the initial lr
times a given function. When last_epoch=-1, sets initial lr as lr.

Args:
    optimizer (Optimizer): Wrapped optimizer.
    lr_lambda (function or list): A function which computes a multiplicative
        factor given an integer parameter epoch, or a list of such
        functions, one for each group in optimizer.param_groups.
    last_epoch (int): The index of last epoch. Default: -1.

Example:
    >>> # Assuming optimizer has two groups.
    >>> lambda1 = lambda epoch: epoch // 30
    >>> lambda2 = lambda epoch: 0.95 ** epoch
    >>> scheduler = LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])
    >>> for epoch in range(100):
    >>>     scheduler.step()
    >>>     train(...)
    >>>     validate(...)

Definition at line 56 of file lr_scheduler.py.

Member Function Documentation

def torch.optim.lr_scheduler.LambdaLR.load_state_dict	(		self,
			state_dict
	)

Loads the schedulers state.

Arguments:
    state_dict (dict): scheduler state. Should be an object returned
from a call to :meth:`state_dict`.

Definition at line 107 of file lr_scheduler.py.

def torch.optim.lr_scheduler.LambdaLR.state_dict

(

self

)

Returns the state of the scheduler as a :class:`dict`.

It contains an entry for every variable in self.__dict__ which
is not the optimizer.
The learning rate lambda functions will only be saved if they are callable objects
and not if they are functions or lambdas.

Definition at line 90 of file lr_scheduler.py.

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The documentation for this class was generated from the following file:

• torch/optim/lr_scheduler.py