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用pycaffe实现了《Going Deeper with Convolutions》提出的GoogleNet,并在cifar10数据集上训练模型。 该作者受到NiN网络的启发,提出了一种高效的深度学习框架。使用了1x1卷积(bottleneck),用于增加网络的宽度和深度,以及减少计算量。Inception模块是在naive inception的基础上加入了1x1卷积,可以有效降低计算复杂度。最后使用了avg pool。 网络的设计遵循了这样的规则:视觉信息应该在不同尺寸进行处理,然后将它们合并在一起供下一阶段使用。

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