衡量神经架构训练效率的框架

Eduardo Cueto-Mendoza, John D. Kelleher
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摘要

衡量神经网络系统开发的效率是一个尚未解决的研究问题。本文提出了一个测量神经架构训练效率的实验框架。为了证明我们的方法,我们分析了卷积神经网络和贝叶斯等效网络在 MNIST 和 CIFAR-10 任务上的训练效率。我们的结果表明,训练效率会随着训练的进行而下降,并且在给定神经模型和学习任务的不同停止标准下也会有所不同。我们还发现训练停止标准、训练效率、模型大小和训练效率之间存在非线性关系。此外,我们还说明了过度训练对衡量神经架构训练效率的潜在干扰效应。关于不同架构的相对训练效率,我们的结果表明,在两个数据集上,CNN 比 BCNN 更有效率。一般来说,随着学习任务变得越来越复杂,不同架构之间训练效率的相对差异也会越来越明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A framework for measuring the training efficiency of a neural architecture
Measuring Efficiency in neural network system development is an open research problem. This paper presents an experimental framework to measure the training efficiency of a neural architecture. To demonstrate our approach, we analyze the training efficiency of Convolutional Neural Networks and Bayesian equivalents on the MNIST and CIFAR-10 tasks. Our results show that training efficiency decays as training progresses and varies across different stopping criteria for a given neural model and learning task. We also find a non-linear relationship between training stopping criteria, training Efficiency, model size, and training Efficiency. Furthermore, we illustrate the potential confounding effects of overtraining on measuring the training efficiency of a neural architecture. Regarding relative training efficiency across different architectures, our results indicate that CNNs are more efficient than BCNNs on both datasets. More generally, as a learning task becomes more complex, the relative difference in training efficiency between different architectures becomes more pronounced.
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