In this paper, the deep convolutional neural networks (DCNNs) are studied to perform the complex feature extraction on the image in the convolution layer and to improve the final test accuracy of the network. By improving the DCNNs algorithm and framework, it can enhance the accurate extraction of the image features. We replace the fully connection layer of the original network with the global average pooling layer. In the absence of the large number of calculations of network parameters, the final effect is not changed; thereby, it increases the speed of the network. The simulation result is given to show the effectiveness of the DCNNs algorithm by comparing the training accuracy and test accuracy of the five improvement algorithms.

中文翻译：

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用于大规模识别应用的 AlexNet 网络的改进

本文研究了深度卷积神经网络（DCNNs）在卷积层对图像进行复杂的特征提取，提高网络的最终测试精度。通过改进DCNNs算法和框架，可以增强对图像特征的准确提取。我们用全局平均池化层替换了原始网络的全连接层。在没有大量计算网络参数的情况下，最终效果没有变化；因此，它提高了网络的速度。通过比较五种改进算法的训练精度和测试精度，给出仿真结果以展示DCNNs算法的有效性。