# 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
# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================
"""AdaGrad 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__ = ['AdaGrad', 'Adagrad']
[docs]class AdaGrad(Optimizer):
"""The classic AdaGrad optimizer.
See Also:
- The functional AdaGrad optimizer: :func:`torchopt.adagrad`.
- The differentiable meta-AdaGrad optimizer: :class:`torchopt.MetaAdaGrad`.
"""
# pylint: disable-next=too-many-arguments
def __init__(
self,
params: Iterable[torch.Tensor],
lr: ScalarOrSchedule = 1e-2,
lr_decay: float = 0.0,
weight_decay: float = 0.0,
initial_accumulator_value: float = 0.0,
eps: float = 1e-10,
*,
maximize: bool = False,
) -> None:
r"""Initialize the AdaGrad 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`)
lr_decay (float, optional): Learning rate decay. (default: :const:`0.0`)
weight_decay (float, optional): Weight decay, add L2 penalty to parameters.
(default: :const:`0.0`)
initial_accumulator_value (float, optional): Initial value for the accumulator.
(default: :const:`0.0`)
eps (float, optional): A small constant applied to denominator outside of the square
root (as in the Adam paper) to avoid dividing by zero when rescaling.
(default: :const:`1e-10`)
maximize (bool, optional): Maximize the params based on the objective, instead of
minimizing. (default: :data:`False`)
"""
super().__init__(
params,
alias.adagrad(
lr=lr,
lr_decay=lr_decay,
weight_decay=weight_decay,
initial_accumulator_value=initial_accumulator_value,
eps=eps,
maximize=maximize,
),
)
Adagrad = AdaGrad # alias for PyTorch compatibility