对比了几种经典的卷积神经网络的算法，方便自己写的网络与经典的几种算法进行对比，包括简单卷积神经网络（LeNet）[1] 、深度卷积神经网络（AlexNet）[2]、包含重复元素的卷积神经网络（VGG）[3]、嵌入网络的卷积神经网络（NiN）[4]、并行连接的卷积神经网络（GoogLeNet）[5]、残差卷积神经网络（ResNet）[6]。
程序主要包括3个部分： compare.py 调用各类CNN模型，定义数据集，优化算法 collect_net.py 定义CNN模型，以及需要的模块 utils 定义辅助函数，包括准确度计算，训练模式。
模型的参数都按照相同的参数进行计算，需要特殊结构的，如GoogLeNet中的Inception和ResNet中的Residual模块，具体参数设置在collect_net.py。
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[3] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
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[6] He, K., Zhang, X., Ren, S., & Sun, J. (2016, October). Identity mappings in deep residual networks. In European Conference on Computer Vision (pp. 630-645). Springer, Cham.