An Analysis of the Effects and Interaction of Hyper Parameters in Convolutional Neural Networks

Gaurav Arora, R. Mutha, M. Sangari, U. Aswal, Abhishek Bhattacherjee, Ankit Agarwal
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Abstract

Deep Neural Network (DNN) hyper parameters are currently physically tuned, so robotized AI methods are being developed to find the best combination of hyper parameters. Finding the right design is difficult since the results of AutoML calculations heavily rely on the underlying frameworks. As a result, applying a visual analytics method is considered as a viable solution. As a solution, HyperTendril, an internet data discovery system is developed to enable user-driven hyperparameter tuning procedures in a model-independent configuration. HyperTendril encompasses a progressive approach to successfully execute the AutoML framework through a repetitive tuning process to assist clients in the tuning and execution of the AutoML framework based on a client's knowledge on the obtained outcome. Users can use HyperTendril to diagnose the configurations of various hyperparameter search methods and gain insights into their complex behaviour. Furthermore, HyperTendril provides various feature analysis to help users narrow their search areas based on the relative relevance of various hyperparameters and the consequences of their interactions.
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卷积神经网络中超参数的作用及相互作用分析
深度神经网络(DNN)的超参数目前是物理调谐的,因此正在开发机器人人工智能方法来寻找超参数的最佳组合。找到正确的设计是困难的,因为AutoML计算的结果严重依赖于底层框架。因此,应用可视化分析方法被认为是一种可行的解决方案。作为解决方案,高血压钻,一个互联网数据发现系统开发,使用户驱动的超参数调优过程在一个模型独立的配置。hypertension drill包含一种渐进式方法,通过重复调优过程来成功执行AutoML框架,以帮助客户根据所获得的结果对AutoML框架进行调优和执行。用户可以使用hypertension drill来诊断各种超参数搜索方法的配置,并深入了解它们的复杂行为。此外,hypertension drill还提供各种特征分析,以帮助用户根据各种超参数的相对相关性及其相互作用的结果缩小搜索范围。
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