摘要
深度学习是多层神经网络运用各种学习算法解决图像、文本等相关问题的算法合集。卷积神经网络作为深度学习的重要算法,尤其擅长图像处理领域。卷积神经网络通过卷积核来提取图像的各种特征,通过权值共享和池化极大降低了网络需要训练的数量级。本文以MINST手写体数据库为训练样本,讨论卷积神经网络的权值反向传播机制和MATLAB的实现方法;对激活函数tanh和relu梯度消失问题进行分析和优化,对改进后的激活函数进行训练,得出最优的修正参数和学习速率。
Deep learning is a collection of algorithms that are used to solve related problems such as image and text. As an important algorithm for deep learning, convolutional neural network is especially good at image processing field. The convolution neural network extracts the various features of the image through the convolution kernel, and its orders of magnitude are greatly reduced by weight sharing and pooling. In this paper, MINST handwritten database is used as training sample to dis-cuss the reverse propagation mechanism of weight value of convolutional neural network, and the implementation method with MATLAB;In order to obtain the optimal correction parameters and learning rate, the problems of gradient disappearance of activation functions tanh and relu were analyzed and optimized, and the improved activation function was trained.
出处
《计算机科学与应用》
2018年第11期1773-1781,共9页
Computer Science and Application
