增强深度神经网络的性能:优化算法的比较分析

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Noor Fatima
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引用次数: 20

摘要

为神经网络模型采用最合适的优化算法(优化器)是深度学习和所有类型的神经网络中最重要的冒险之一。这是一个反复试验的案例。在本文中,我们将在四个不相关的数据集上离散地实验七种最流行的优化算法:sgd, rmsprop, adagrad, adadelta, adam, adamax和nadam,以得出哪一种算法为我们的深度神经网络分配了最好的精度,效率和性能。这项工作将为数据科学家在建模深度神经网络时选择最佳优化器提供有见地的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Performance of a Deep Neural Network: A Comparative Analysis of Optimization Algorithms
Adopting the most suitable optimization algorithm (optimizer) for a Neural Network Model is among the most important ventures in Deep Learning and all classes of Neural Networks. It’s a case of trial and error experimentation. In this paper, we will experiment with seven of the most popular optimization algorithms namely: sgd, rmsprop, adagrad, adadelta, adam, adamax and nadam on four unrelated datasets discretely, to conclude which one dispenses the best accuracy, efficiency and performance to our deep neural network. This work will provide insightful analysis to a data scientist in choosing the best optimizer while modelling their deep neural network.
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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