卷积神经网络中优化算法性能比较研究

Performance comparison of optimization algorithms in convolutional neural networks

深度学习作为人工智能最热门的技术之一,对网络结构的设计及其他参数调整有较高的要求,而优化算法的性能决定了调参的效率。本文基于卷积神经网络,比较了常用的优化算法的性能,并分别分析了处理简单图像和复杂图像时各个优化算法的优缺点,为深度学习的应用提供参考。

As one of the most popular technologies of artificial intelligence,deep learning has higher requirements for the design of network structure and other parameter adjustment,and the performance of optimization algorithm determines the efficiency of parameter adjustment.Based on the convolution neural network,this paper compares the performance of the commonly used optimization algorithms,and analyzes the advantages and disadvantages of each optimization algorithm when processing simple images and complex images,which provides a reference for the application of deep learning.