# Copyright 2022-2023 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
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# ==============================================================================
"""Adadelta optimizer."""
from __future__ import annotations
from typing import Iterable
import torch
from torchopt import alias
from torchopt.optim.base import Optimizer
from torchopt.typing import ScalarOrSchedule
__all__ = ['AdaDelta', 'Adadelta']
[docs]class AdaDelta(Optimizer):
"""The classic AdaDelta optimizer.
See Also:
- The functional AdaDelta optimizer: :func:`torchopt.adadelta`.
- The differentiable meta-AdaDelta optimizer: :class:`torchopt.MetaAdaDetla`.
"""
# pylint: disable-next=too-many-arguments
def __init__(
self,
params: Iterable[torch.Tensor],
lr: ScalarOrSchedule = 1.0,
rho: float = 0.9,
eps: float = 1e-6,
weight_decay: float = 0.0,
) -> None:
r"""Initialize the AdaDelta 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-3`)
rho (float, optional): Coefficients used for computing running averages of gradient and its square.
(default: :const:`0.9`)
eps (float, optional): A small constant applied to the square root (as in the AdaDelta paper)
to avoid dividing by zero when rescaling.
(default: :const:`1e-6`)
weight_decay (float, optional): Weight decay, add L2 penalty to parameters.
(default: :const:`0.0`)
"""
super().__init__(
params,
alias.adadelta(
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
moment_requires_grad=False,
),
)
Adadelta = AdaDelta # alias for PyTorch compatibility