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 3章 神经网络
42
下面，我们将仔细介绍激活函数。激活函数是连接感知机和神经网络的
桥梁。
A
本书在使用“感知机”一词时，没有严格统一它所指的算法。一
般而言，“朴素感知机”是指单层网络，指的是激活函数使用了阶
跃函数
A
的模型。“多层感知机”是指神经网络，即使用sigmoid
函数（后述）等平滑的激活函数的多层网络。
3.2
激活函数
式（3.3）表示的激活函数以阈值为界，一旦输入超过阈值，就切换输出。
这样的函数称为“阶跃函数”。因此，可以说感知机中使用了阶跃函数作为
激活函数。也就是说，在激活函数的众多候选函数中，感知机使用了阶跃函数。
那么，如果感知机使用其他函数作为激活函数的话会怎么样呢？实际上，如
果将激活函数从阶跃函数换成其他函数，就可以进入神经网络的世界了。下
面我们就来介绍一下神经网络使用的激活函数。
3.2.1
sigmoid函数
神经网络中经常使用的一个激活函数就是式（3.6）表示的sigmoid 函数
（
sigmoid function
）。
（3.6）
式（3.6）中 的
exp(−
x
)
表示
e
−
x
的意思。
e
是纳皮尔常数 2.7182
...
。 式（3.6）
表示的
sigmoid
函数看上去有些复杂，但它也仅仅是个函数而已。而函数就是
给定某个输入后，会返回某个输出的转换器。比如，向
sigmoid
函数输入 1.0或 2.0
后，就会有某个值被输出，类似h
(
1.0
) =
0.731
...
、h
(
2.0
) =
0.880
...
这样。
神经网络中用
sigmoid
函数作为激活函数，进行信号的转换，转换后的 ...
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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.