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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。 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。 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。 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。 Adam(Adaptive momentum)是一种自适应动量的随机优化方法(A method for stochastic optimization),经常作为 深度学习 中的优化器算法。 2014年12月, Kingma和Lei Ba两位学者提出了Adam优化器,结合AdaGrad和RMSProp两种优化算法的优点。 对梯度的一阶矩估计(First Moment Estimation,即梯度的均值)和二阶矩估计(Second Moment Estimation,即梯度的未中心化的方差)进行综合考虑,计算出更新步长。
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