megengine.optimizer.adagrad 源代码

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
from typing import Iterable, Union

import numpy as np

from ..tensor import Parameter, tensor
from .optimizer import Optimizer


[文档]class Adagrad(Optimizer): r""" Implements Adagrad algorithm. It has been proposed in `"Adaptive Subgradient Methods for Online Learning and Stochastic Optimization" <http://jmlr.org/papers/v12/duchi11a.html>`_. :param params: iterable of parameters to optimize or dicts defining parameter groups. :param lr: coefficient that scales delta before it is applied to the parameters. Default: 1e-2 :param lr_decay: learning rate decay. Default: 0 :param eps: term added to the denominator to improve numerical stability. Default: 1e-10 :param weight_decay: weight decay (L2 penalty). Default: 0 """ def __init__( self, params: Union[Iterable[Parameter], dict], lr: float = 1e-2, lr_decay: float = 0.0, eps: float = 1e-10, weight_decay: float = 0.0, ): assert lr >= 0.0, "Invalid learning rate: {}".format(lr) assert lr_decay >= 0, "Invalid learning rate decay: {}".format(lr_decay) assert eps >= 0.0, "Invalid epsilon value: {}".format(eps) assert weight_decay >= 0.0, "Invalid weight_decay value: {}".format( weight_decay ) defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay) super().__init__(params, defaults) def _create_state(self, param_group): for param in param_group["params"]: self._add_state(param, "square_avg") self._add_state(param, "step", initializer=0.0) def _updates(self, param_group): lr = param_group["lr"] lr_decay = param_group["lr_decay"] weight_decay = param_group["weight_decay"] eps = param_group["eps"] # since `conver_inputs` is disabled for param updates, # scalar should be explicitly tansforred to tensor _lr = tensor([lr]) _lr_decay = tensor([lr_decay]) _weight_decay = tensor([weight_decay]) _eps = tensor([eps]) c05 = tensor([0.5]) c1 = tensor([1.0]) c2 = tensor([2.0]) for param in param_group["params"]: if param.grad is None: continue states = self._state[param] step = states["step"] step += c1 grad = param.grad if weight_decay != 0.0: grad += param * _weight_decay square_avg = states["square_avg"] square_avg += grad ** c2 delta = grad / (square_avg + _eps) ** c05 clr = _lr / (c1 + (step - c1) * _lr_decay) param -= clr * delta