论文标题
Hyper-Sinh：从tensorflow和Keras中从浅层学习到深度学习的准确和可靠的功能
hyper-sinh: An Accurate and Reliable Function from Shallow to Deep Learning in TensorFlow and Keras
论文作者
论文摘要
本文介绍了“ Hyper-Sinh”，这是M-Arcsinh激活函数的变体，适合深度学习（DL）基于监督学习的算法，例如卷积神经网络（CNN）。因此，在开源Python库Tensorflow和Keras中开发的Hyper-Sinh因此被描述并被描述为浅层和深神经网络的准确可靠激活函数。讨论了五（n = 5）基准数据集的图像和文本分类任务的准确性和可靠性的提高。实验结果表明，通过这种新功能获得的浅神经和深神经网络的总体竞争分类性能。根据黄金标准激活功能评估了此功能，这表明了图像和文本分类的整体竞争准确性和可靠性。
This paper presents the 'hyper-sinh', a variation of the m-arcsinh activation function suitable for Deep Learning (DL)-based algorithms for supervised learning, such as Convolutional Neural Networks (CNN). hyper-sinh, developed in the open source Python libraries TensorFlow and Keras, is thus described and validated as an accurate and reliable activation function for both shallow and deep neural networks. Improvements in accuracy and reliability in image and text classification tasks on five (N = 5) benchmark data sets available from Keras are discussed. Experimental results demonstrate the overall competitive classification performance of both shallow and deep neural networks, obtained via this novel function. This function is evaluated with respect to gold standard activation functions, demonstrating its overall competitive accuracy and reliability for both image and text classification.
