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 5章 误差反向传播法
130
图5-8 根据计算图的反向传播的结果， 等于2(
x
+
y
)
y
x
zt
1
+
**
2
5.3
反向传播
上一节介绍了计算图的反向传播是基于链式法则成立的。本节将以“
+
”
和“
×
”等运算为例，介绍反向传播的结构。
5.3.1
加法节点的反向传播
首先来考虑加法节点的反向传播。这里以z
=
x
+
y 为对象，观察它的
反向传播。z
=
x
+
y 的导数可由下式（解析性地）计算出来。
（5.5）
如 式（5.5）所 示 ，
和 同时都等于1。因此，用计算图表示的话，如
图5
-
9 所示。
在图5
-
9 中，反向传播将从上游传过来的导数（本例中是
）乘 以 1，然
后传向下游。也就是说，因为加法节点的反向传播只乘以1，所以输入的值
会原封不动地流向下一个节点。
5.3 反向传播
131
图5
-
9 加法节点的反向传播：左图是正向传播，右图是反向传播。如右图的反向传播所示，
加法节点的反向传播将上游的值原封不动地输出到下游
+
+
y
x
z
另外，本例中把从上游传过来的导数的值设为 。这是因为，如图 5
-
10
所示，我们假定了一个最终输出值为L 的大型计算图。z
=
x
+
y 的计算位于
这个大型计算图的某个地方，从上游会传来
的值，并向下游传递 和
。
图5-10 加法节点存在于某个最后输出的计算的一部分中。反向传播时，从最右边的输 ...
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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.