改进的Dropout正则化卷积神经网络

针对深度卷积神经网络(CNN)中出现的过度拟合的问题,给出了一种改进的结构,训练阶段采用Dropout正则化,测试阶段采用了模型平均方法且同时考虑了保留概率和池化区域内单元值所占概率.仿真实验表明:在MNIST手写数据库和CMU—PIE的部分图像库中,相同迭代次数及CNN结构下新算法均优于其他方法,不仅能够得到更好的识别率而且更能防止过拟合的问题.

Improved dropout regularization CNN

In order to solve over-fitting problem in deep convolution neural network(CNN),an improved structure is presented. Dropout is used at training time,model averaging method used at test stage,considers retaining probability and probability of each unit within pooling region at the same time.Simulation experiments show that in MNIST handwritten database and part of CMU-PIE database,under the same structure and the same number of iterations,the method is superior to other methods.It can get a better recognition rate and can overcome over-fitting problem better.