Source code for pypots.optim.rmsprop

The optimizer wrapper for PyTorch RMSprop.


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

from typing import Iterable, Optional

from torch.optim import RMSprop as torch_RMSprop

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

[docs] class RMSprop(Optimizer): """The optimizer wrapper for PyTorch RMSprop :class:`torch.optim.RMSprop`. Parameters ---------- lr : float The learning rate of the optimizer. momentum : float Momentum factor. alpha : float Smoothing constant. eps : float Term added to the denominator to improve numerical stability. centered : bool If True, compute the centered RMSProp, the gradient is normalized by an estimation of its variance weight_decay : float Weight decay (L2 penalty). 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, alpha: float = 0.99, eps: float = 1e-08, centered: bool = False, weight_decay: float = 0, lr_scheduler: Optional[LRScheduler] = None, ): super().__init__(lr, lr_scheduler) self.momentum = momentum self.alpha = alpha self.eps = eps self.centered = centered self.weight_decay = weight_decay
[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_RMSprop( params=params,, momentum=self.momentum, alpha=self.alpha, eps=self.eps, centered=self.centered, weight_decay=self.weight_decay, ) if self.lr_scheduler is not None: self.lr_scheduler.init_scheduler(self.torch_optimizer)