改进卷积神经网络模型设计方法

针对现有卷积神经网络模型参数量大、训练耗费时间的问题,提出一种网络串联和并联共用的方法,使用较小的卷积核和较多的非线性激活减少参数量的同时增加网络特征学习能力,提出尺度归一化池化层取代全连接层,避免全连接层参数过多容易导致过拟合的问题,改进后的模型支持训练任意尺寸的图片。实验结果表明,提出方法减少了大量的参数和训练消耗的时间,有效提升了算法的效率。

Improvement of convolutional neural network model design method

Aiming at the problems that the existing convolutional neural network model has large parameters and its training is time-consuming,a method of network serial and parallel sharing was proposed,which used a smaller convolution kernel and more nonlinear activation to reduce the parameter quantity and increase the network feature learning ability,and scale normalization pooling layer was proposed to replace the full connection layer,avoiding the problem that the full connection layer parameters are too easy to cause over-fitting,and the improved model supported the training of images of any size. Experimental results show that the proposed method reduces the number of parameters and time spent on training,which effectively improves the efficiency of the algorithm.