April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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自动特征生成：图像特征提取和深度学习
｜
123
图
8-11
给出了这
3
个函数的示意图。
常用激活函数
图 8-11：3 个常用激活函数的示意图：ReLU、tanh 和 sigmoid
ReLU
变换对非负函数没有效果，如原始图像或高斯滤波器。然而，训练后的神经网络，不
管是全连接类型还是卷积类型，都可能输出负值。
AlexNet
使用
ReLU
变换而不是其他变换，
在训练时可以更快地收敛（
Krizhevsky
等，
2012
）。它在所有卷积层和全连接层都使用
ReLU
。
8.3.4
响应归一化层
经过了第
4
章和本章前面的讨论，归一化这个概念我们已经耳熟能详。归一化用单个输出
除以全部响应的一个函数。所以，理解归一化的另一种方式是，它在邻居之间建立了一种
竞争关系，因为这样每个输出的强度都要相对于它的邻居来测量（见图
8-12
）。
AlexNet
在
不同核的每个位置上对输出进行归一化。
响应归一化
卷积核输出
归一化后的输出
邻域的大小
图 8-12：来自上一层卷积核输出的响应归一化结构，归一化常数是根据上一层的邻域计算得出的
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.