基于Warmup和cosine回火算法的联合改进的WuC-Adam算法
Can Zhang1, Yichuan Shao2, Haijing Sun2
1School of Information Engineering, Shenyang University, Shenyang 110044, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
这项研究使用Warmup和cosine化来增强Adam优化算法,以改善神经网络的训练. 增强的Adam算法在基准数据集上展示了卓越的融合速度和概括性能.
科学领域:
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习优化优化
背景情况:
- 亚当算法被广泛用于神经网络优化,但面临着局部最佳和不稳定的学习率等挑战.
- 这些局限性可能会阻碍模型的融合和通用化性能.
研究的目的:
- 引入一个增强的Adam优化算法,整合了Warmup和cosine回火技术.
- 为了解决传统的亚当算法固有的不稳定性,局部优化和过拟合问题.
主要方法:
- 实施了热身策略,以稳定初始培训阶段的学习率.
- 纳入了一个动态的共弦回火策略,用于适应性学习率调整.
- 在MNIST,CIFAR10和CIFAR100数据集上进行了广泛的实验,与传统的Adam和其他优化器进行比较.
主要成果:
- 改进的Adam算法在MNIST上达到98.87%,在CIFAR10上达到87.67%,在CIFAR100上达到58.88%的精度.
- 与现有方法相比,在模型融合速度和概括能力方面取得了显著的改进.
- 验证了加热和共弦结集成的有效性.
结论:
- 提议的增强的Adam算法有效地减轻了深度学习中常见的优化挑战.
- 整合加热和共弦化提供了一种有前途的方法,用于改善各种深度学习任务中的模型性能.
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