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NALA:一个Nesterov加速前优化器,用于深度学习.

Xuan Zuo1, Hui-Yan Li2, Shan Gao1

  • 1School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China.

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一个新的深度学习优化器NALA将自适应梯度与Nesterov加速相结合. 与现有方法相比,这种方法加快了融合,提高了图像分类任务的准确性.

关键词:
深度学习是一种深度学习.展望未来的人生尼斯特罗夫的加速梯度渐变.优化算法的优化算法

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科学领域:

  • 深度学习 (Deep Learning) 是一种深度学习.
  • 优化算法 优化算法
  • 机器学习 机器学习

背景情况:

  • 适应梯度算法在深度学习中被广泛使用,以实现更快的融合.
  • 目前的方法经常适应重型球加速,理论上比内斯特罗夫加速慢.
  • 尼斯特罗夫加速度通过在取值点上使用梯度来提供更快的收.

研究的目的:

  • 提出一种新的优化算法,NALA,用于深度学习.
  • 结合适应梯度方法与Nesterov加速,使用前性策略.
  • 提高深度学习任务中的融合速度和模型准确性.

主要方法:

  • 引入了NALA,一种代更新"快速"和"缓慢"权重的算法.
  • 使用Adam优化器在内部循环中更新快速权重.
  • 使用Nesterov的加速梯度 (NAG) 在外部循环中更新缓慢的权重.

主要成果:

  • 与其他流行的优化算法相比,NALA表现出更快的趋同.
  • 对图像分类任务的实验表明,NALA实现了更高的准确性.
  • 纳拉的前计划有效地整合了自适应梯度和内斯特罗夫加速.

结论:

  • NALA提供了一种优越的方法来优化深度学习.
  • 拟议的方法实现了融合速度和准确性之间的有利平衡.
  • NALA代表了自适应梯度优化技术的重大进步.