使用Python进行超参数搜索探索超参组合的最佳值

使用Python进行超参数搜索探索超参组合的最佳值

作者：禅与计算机程序设计艺术

1.简介

智能算法、机器学习和深度学习是当今社会一个重要研究方向，而自动化的超参数搜索（Hyperparameter Tuning）也是一个非常关键的环节。超参数搜索也就是要找到最优的参数设置，来获得更好的模型效果。因此，如何对超参数进行合理的搜索，也是一项重要工作。本文将通过一个典型的超参数搜索例子——图像分类任务，给读者介绍如何利用Python实现超参数搜索。
Hyperparameter tuning (HPT) is a critical component of machine learning systems that enable the algorithm to adapt itself to the specific characteristics of the data it is trained on and minimize overfitting or underfitting. In general, HPT involves searching through multiple combinations of hyperparameters to find the best-performing one(s). This process can be computationally expensive as the number of hyperparameters grows exponentially with respect to their dimensionality, which makes traditional grid search methods infeasible for large datasets.
本文首先介绍超参数搜索的概念及其不同类型，包括grid search, random search, Bayesian optimization, and neural architecture search (NAS). 然后，通过一个具体的案例来