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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附录 A Softmax-with-Loss层的计算图
268
图
A-
1 的计算图中假定了一个进行3 类别分类的神经网络。从前面的层
输入的是
(
a
1
,a
2
,a
3
)
，
softmax
层输出
(
y
1
,y
2
,y
3
)
。此外，教师标签是
(
t
1
,t
2
,t
3
)
，
Cross Entropy Error
层输出损失L。
如图
A-
1 所示，在本附录中，
Softmac-with-Loss
层的反向传播的结果为
(
y
1
−
t
1
,y
2
−
t
2
,y
3
−
t
3
)
。
A.1
正向传播
图
A-
1 的计算图中省略了
Softmax
层和
Cross Entropy Error
层的内容。
这里，我们来画出这两个层的内容。
首先是
Softmax
层。
softmax
函数可由下式表示。
（
A
.1）
因此，用计算图表示
Softmax
层的话，则如图
A-
2 所示。
图
A-
2 的计算图中，指数的和（相当于式
(A
.1
)
的分母）简写为S，最终
的输出记为
(
y
1
,y
2
,y
3
)
。
接下来是
Cross Entropy Error
层。交叉熵误差可由下式表示。
（
A
.2）
根据式
(A
.2
)
，
Cross Entropy Error
层的计算图可以画成图
A-
3 那样。
图
A-
3 的计算图很直观地表示出了式
(A
.2
)
，所以应该没有特别难的地方。
下一节，我们将看一下反向传播。
A.1 正向传播
269 ...
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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.