Source code for pypots.optim.sgd

The optimizer wrapper for PyTorch SGD :class:`torch.optim.SGD`.


# Created by Wenjie Du <>
# License: BSD-3-Clause

from typing import Iterable, Optional

from torch.optim import SGD as torch_SGD

from .base import Optimizer
from .lr_scheduler.base import LRScheduler

[docs] class SGD(Optimizer): """The optimizer wrapper for PyTorch SGD :class:`torch.optim.SGD`. Parameters ---------- lr : float The learning rate of the optimizer. momentum : float Momentum factor. weight_decay : float Weight decay (L2 penalty). dampening : float Dampening for momentum. nesterov : bool Whether to enable Nesterov momentum. lr_scheduler : pypots.optim.lr_scheduler.base.LRScheduler The learning rate scheduler of the optimizer. """ def __init__( self, lr: float = 0.001, momentum: float = 0, weight_decay: float = 0, dampening: float = 0, nesterov: bool = False, lr_scheduler: Optional[LRScheduler] = None, ): super().__init__(lr, lr_scheduler) self.momentum = momentum self.weight_decay = weight_decay self.dampening = dampening self.nesterov = nesterov
[docs] def init_optimizer(self, params: Iterable) -> None: """Initialize the torch optimizer wrapped by this class. Parameters ---------- params : An iterable of ``torch.Tensor`` or ``dict``. Specifies what Tensors should be optimized. """ self.torch_optimizer = torch_SGD( params=params,, momentum=self.momentum, weight_decay=self.weight_decay, dampening=self.dampening, nesterov=self.nesterov, ) if self.lr_scheduler is not None: self.lr_scheduler.init_scheduler(self.torch_optimizer)