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Adam算法是在2014年提出的一种基于一阶梯度的优化算法,它结合了 动量 (Momentum)和 RMSprop (Root Mean Square Propagation)的思想, 自适应地调整每个参数的学习率。 Adam优化器凭借其独特的设计和出色的性能,已成为深度学习领域不可或缺的工具。 深入理解其原理和性质,能帮助我们更好地运用它提升模型训练效果,推动深度学习技术不断发展。 如果想使训练深层网络模型快速收敛或所构建的神经网络较为复杂,则应该使用Adam或其他自适应学习速率的方法,因为这些方法的实际效果更优。
AdamW目前是大语言模型训练的默认优化器,而大部分资料对Adam跟AdamW区别的介绍都不是很明确,在此梳理一下Adam与AdamW的计算流程,明确一下二者的区别。 基本原理 Adam本质上是一个优化器,用于优化模型的参数。 这样的优化步骤可以由以下公式描述: θ t = θ t 1 η m ^ t v ^ t + ϵ ,其中 η 为初始学习率, ϵ 为数值稳定常数,说白了是用于防止除零异常。 关键的在于新增的两大参数 m ^ t 和 v ^ t 。 Adam,这个名字在许多获奖的 Kaggle 竞赛中广为人知。 参与者尝试使用几种优化器(如 SGD、Adagrad、Adam 或 AdamW)进行实验是常见的做法,但真正理解它们的工作原理是另一回事。
在 PyTorch 里, Adam 和 AdamW 的调用语法几乎一模一样,这是因为 PyTorch 的优化器接口是统一设计的,使用方式都继承自 torch.optim.Optimizer 的通用结构。
正因为Adam是深度学习时代最有影响力的工作之一,该如何(定量地)理解它就是一个非常重要、非常困难、又非常迷人的挑战。 Adam Optimizer应该是最常用的优化算法,并且其已经在大量的深度神经网络实验上验证了其有效性,下面我将一步一步拆解,介绍Adam Optimizer的来龙去脉。 虽然Adam算法目前成为主流的优化算法,不过在很多领域里(如计算机视觉的对象识别、NLP中的机器翻译)的最佳成果仍然是使用带动量(Momentum)的SGD来获取到的。
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