# Copyright 2022-2024 MetaOPT Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================
"""SGD optimizer."""
from typing import Iterable
import torch
from torchopt import alias
from torchopt.optim.base import Optimizer
from torchopt.typing import ScalarOrSchedule
__all__ = ['SGD']
[docs]
class SGD(Optimizer):
"""The classic SGD optimizer.
See Also:
- The functional SGD optimizer: :func:`torchopt.sgd`.
- The differentiable meta-SGD optimizer: :class:`torchopt.MetaSGD`.
"""
# pylint: disable-next=too-many-arguments
def __init__(
self,
params: Iterable[torch.Tensor],
lr: ScalarOrSchedule,
momentum: float = 0.0,
weight_decay: float = 0.0,
dampening: float = 0.0,
nesterov: bool = False,
maximize: bool = False,
) -> None:
r"""Initialize the SGD optimizer.
Args:
params (iterable of Tensor): An iterable of :class:`torch.Tensor`\s. Specifies what
tensors should be optimized.
lr (float or callable): This is a fixed global scaling factor or a learning rate
scheduler.
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`)
weight_decay (float, optional): Weight decay, add L2 penalty to parameters.
(default: :const:`0.0`)
dampening (float, optional): Dampening for momentum. (default: :const:`0.0`)
nesterov (bool, optional): Whether to use Nesterov momentum. (default: :data:`False`)
moment_requires_grad (bool, optional): If :data:`True` the momentums will be created
with flag ``requires_grad=True``, this flag is often used in Meta-Learning
algorithms. (default: :data:`False`)
maximize (bool, optional): Maximize the params based on the objective, instead of
minimizing. (default: :data:`False`)
"""
super().__init__(
params,
alias.sgd(
lr=lr,
momentum=momentum,
weight_decay=weight_decay,
dampening=dampening,
nesterov=nesterov,
moment_requires_grad=False,
maximize=maximize,
),
)