Jaya Tata Hardinata, Harly Okprana, Agus Perdana Windarto, Widodo Saputra
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引用次数: 1

摘要

反向传播是一种人工神经网络,它具有进行训练和确定正确参数以产生相似但不相同输入的正确输出的架构。影响反向传播体系结构确定的参数之一是学习率,如果学习率的值过高,则网络体系结构变得不稳定;如果学习率的值过低,则网络体系结构收敛,并且在训练网络体系结构时需要很长时间。本研究数据是来自UCI数据机器学习的辅助数据。本研究的最佳网络结构为13-10-3,其学习率为0.01、0.03、0.06、0.01、0.13、0.16、0.2、0.23、0.026、0.3、0.35、0.4、0.45、0.5、0.55、0.6、0.65、0.7、0.75、0.8、0.9。从13-10-3网络体系结构的21个不同的学习率值中发现,学习率的高低对于得到正确、快速的网络体系结构是非常重要的。这可以在实验中看到,与小于0.65的学习率相比,0.65的学习率可以产生更好的准确性水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analisis Laju Pembelajaran dalam Mengklasifikasi Data Wine Menggunakan Algoritma Backpropagation
Backpropagation is an artificial neural network that has the architecture in conducting training and determining the right parameters to produce the correct output of similar but not the same input. One of the parameters that influences the determination of bacpropagation architecture is the rate of learning, where if the value of the learning rate is too high then the network architecture becomes unstable otherwise if the value of the learning rate is too low the network architecture converges and takes a long time in training network architecture. This research data is secondary data sourced from UCI Data Mechine Learning. The best network architecture in this study is 13-10-3, with different learning rates ranging from 0.01, 0.03, 0.06, 0.01, 0.13, 0.16, 0.2, 0.23, 0.026, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.9. From the 21 different learning rate values in the 13-10-3 network architecture, it is found that the level of learning rate is very important to get the right and fast network architecture. This can be seen in experiments with a learning rate of 0.65 can produce a better level of accuracy compared to a learning rate smaller than 0.65.
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