Optimal Hyperparameter $\epsilon$ for Adaptive Stochastic Optimizers through Gradient Histograms

• URL: http://arxiv.org/abs/2311.11532v1
• Date: Mon, 20 Nov 2023 04:34:19 GMT
• Title: Optimal Hyperparameter $\epsilon$ for Adaptive Stochastic Optimizers through Gradient Histograms
• Authors: Gustavo Silva, Paul Rodriguez
• Abstract summary: We introduce a new framework based on gradient histograms to analyze and justify attributes adaptives. We propose a novel gradient histogram-based algorithm that automatically estimates a reduced and accurate search space for the safeguard factor $epsilon$.
• Score: 0.8702432681310399
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Optimizers are essential components for successfully training deep neural network models. In order to achieve the best performance from such models, designers need to carefully choose the optimizer hyperparameters. However, this can be a computationally expensive and time-consuming process. Although it is known that all optimizer hyperparameters must be tuned for maximum performance, there is still a lack of clarity regarding the individual influence of minor priority hyperparameters, including the safeguard factor $\epsilon$ and momentum factor $\beta$, in leading adaptive optimizers (specifically, those based on the Adam optimizers). In this manuscript, we introduce a new framework based on gradient histograms to analyze and justify important attributes of adaptive optimizers, such as their optimal performance and the relationships and dependencies among hyperparameters. Furthermore, we propose a novel gradient histogram-based algorithm that automatically estimates a reduced and accurate search space for the safeguard hyperparameter $\epsilon$, where the optimal value can be easily found.

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