July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Content preview from 深度学习入门 : 基于Python的理论与实现
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第 6章 与学习相关的技巧
186
为0、方差为1 的数据
，非常简单。通过将这个处理插入到
激活函数的前面（或者后面）
A
，可以减小数据分布的偏向。
接着，
Batch Norm
层会对正规化后的数据进行缩放和平移的变换，用
数学式可以如下表示。
（6.8）
这里，
γ
和
β
是参数。一开始
γ =
1，
β =
0，然后再通过学习调整到合
适的值。
上面就是
Batch Norm
的算法。这个算法是神经网络上的正向传播。如
果使用第5 章介绍的计算图，
Batch Norm
可以表示为图6
-
17。
图6-17 Batch Normalization 的计算图（引用自文献[13]）
x
(N ,D ) (N ,D )
x −
* *
+
ˆ2
d
x
r
(D ,)
r
d
r
β
(D ,)
β
d
β
x−ε
x
1
out
dout
Batch Norm
的反向传播的推导有些复杂，这里我们不进行介绍。不过
如果使用图6
-
17 的计算图来思考的话，
Batch Norm
的反向传播或许也能比较
轻松地推导出来。
Frederik Kratzert
的博客“
Understanding the backward
pass through Batch Normalization Layer
”
[13]
里有详细说明，感兴趣的读者
可以参考一下。
6.3.2
Batch Normalization的评估
现在我们使用
Batch Norm
层进行实验。首先，使用
MNIST
数据集，
A
文献
[
11
]
、文献
[
12
]
等中有讨论（做过实验）应该把
Batch Normalization
插入到激活函数的前面还是 ...
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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.