结合Inception模块的卷积神经网络图像分类方法

Convolutional Neural Network Image Classification Method Combined with Inception Module

针对现有卷积神经网络模型参数量大、训练时间长的问题,提出了一种结合VGG模型和Inception模块特点的网络模型。该模型通过结合两种经典模型的特点,增加网络模型的宽度和深度,使用较小的卷积核和较多的非线性激活,在减少参数量的同时增加了网络特征提取能力,同时利用全局平均池化层替代全连接层,避免全连接层参数过多容易导致的过拟合问题。在MNIST和CIFAR-10数据集上的实验结果表明,该方法在MNIST数据集上的准确率达到了99.76%,在CIFAR-10数据集上的准确率相比传统卷积神经网络模型提高了6%左右。

Aiming at the problem that the existing convolutional neural network model has large parameters and long training time,we propose a network model combining VGG model and inception module.By combining the characteristics of the two classical models,the model increases the width and depth of the network model,uses a smaller convolution kernel and more nonlinear activation,and increases the network feature extraction ability while reducing the parameter quantity.The average pooling layer replaces the fully connected layer,avoiding the over-fitting problem that is easily caused by too many parameters of the full-connected layer.Experimental results on the MNIST and CIFAR-10 datasets show that the accuracy of this method on the MNIST dataset is 99.76%.The accuracy on the CIFAR-10 dataset is about 6%higher than the traditional convolutional neural network model.