Which Deep Learning Framework Should I Use: A Comparative Study For Deep Regression Modeling
Which Deep Learning Framework Should I Use: A Comparative Study For Deep Regression Modeling
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DOI：
10.1109/csci58124.2022.00018
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Vishu Gupta;W. Liao;Alok Ratan Choudhary
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文献类型：
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作者：
Vishu Gupta;W. Liao;Alok Ratan Choudhary
深度学习技术和计算资源的综合影响以及数据库可用性的增加正在改变许多研究领域，从而导致技术进步，以帮助解决现实生活和研究相关的问题。近年来，已经开发了各种深度学习框架和库来实现这些算法，这些算法可以在大规模和异构环境下有效地运行。然而，这些实现可以根据框架而变化，从而导致即使对于相同的算法，结果也会出现意想不到的不一致。这种不规则性在使用GPU等高级并行计算资源训练的深度学习模型中更常见。在这项研究中，我们对三个著名的深度学习框架进行了调查：Tensorflow 1，Tensorflow 2与Keras，以及Pytorch，用于物理科学中基于回归的问题，以分析结果如何根据框架而变化。我们实现了具有不同复杂度的不同深度神经网络，并进行了基于准确性、时间和计算的分析，以研究框架对模型准确性、训练/测试时间、可重复性和内存使用的影响。
The combined impact of deep learning techniques and computing resources with an increase in the availability of databases is transforming many research fields leading to technological advances to help solve real-life and research-related problems. In the recent years, various deep learning frameworks and libraries have been developed to implement these algorithms that can operate efficiently at large scale and heterogeneous en-vironments. However, these implementations can vary depending on the framework leading to unexpected inconsistency in the results even for the same algorithm. This irregularity is seen more often in deep learning models trained using advanced parallel computing resources such as GPUs. In this study, we perform an investigation with three of the well-known deep learning frameworks: Tensorflow 1, Tensorflow 2 with Keras, and Pytorch for regression-based problems in physical sciences to analyze how the results vary depending on the framework. We implement different deep neural networks with varying complexity and perform accuracy, time, and computational based analysis to study the effect of the framework on model accuracy, training/testing times, reproducibility, and memory usage.