Source code for torchopt.optim.rmsprop

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"""RMSProp optimizer."""

from typing import Iterable

import torch

from torchopt import alias
from torchopt.optim.base import Optimizer
from torchopt.typing import ScalarOrSchedule


__all__ = ['RMSProp', 'RMSprop']


[docs] class RMSProp(Optimizer): """The classic RMSProp optimizer. See Also: - The functional RMSProp optimizer: :func:`torchopt.rmsprop`. - The differentiable meta-RMSProp optimizer: :class:`torchopt.MetaRMSProp`. """ # pylint: disable-next=too-many-arguments def __init__( self, params: Iterable[torch.Tensor], lr: ScalarOrSchedule = 1e-2, alpha: float = 0.99, eps: float = 1e-8, weight_decay: float = 0.0, momentum: float = 0.0, centered: bool = False, *, initial_scale: float = 0.0, nesterov: bool = False, maximize: bool = False, ) -> None: r"""Initialize the RMSProp optimizer. Args: params (iterable of Tensor): An iterable of :class:`torch.Tensor`\s. Specifies what tensors should be optimized. lr (float or callable, optional): This is a fixed global scaling factor or a learning rate scheduler. (default: :const:`1e-2`) alpha (float, optional): Smoothing constant, the decay used to track the magnitude of previous gradients. (default: :const:`0.99`) eps (float, optional): A small numerical constant to avoid dividing by zero when rescaling. (default: :const:`1e-8`) weight_decay (float, optional): Weight decay, add L2 penalty to parameters. (default: :const:`0.0`) momentum (float, optional): The decay rate used by the momentum term. The momentum is not used when it is set to :const:`0.0`. (default: :const:`0.0`) centered (bool, optional): If :data:`True`, use the variance of the past gradients to rescale the latest gradients. (default: :data:`False`) initial_scale (float, optional): Initialization of accumulators tracking the magnitude of previous updates. PyTorch uses :data:`0.0`, TensorFlow 1.x uses :data:`1.0`. When reproducing results from a paper, verify the value used by the authors. (default: :data:`0.0`) nesterov (bool, optional): Whether to use Nesterov momentum. (default: :data:`False`) maximize (bool, optional): Maximize the params based on the objective, instead of minimizing. (default: :data:`False`) """ super().__init__( params, alias.rmsprop( lr=lr, alpha=alpha, eps=eps, weight_decay=weight_decay, momentum=momentum, centered=centered, initial_scale=initial_scale, nesterov=nesterov, maximize=maximize, ), )
RMSprop = RMSProp # alias for PyTorch compatibility