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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3.2 激活函数
45
图3-6 阶跃函数的图形
1.0
0.8
0.6
0.4
0.2
0.0
−
6
−
4
−
2 0 2 4 6
如图 3
-
6 所示，阶跃函数以 0为界，输出从 0 切换为 1（或者从1 切换为 0）。
它的值呈阶梯式变化，所以称为阶跃函数。
3.2.4
sigmoid函数的实现
下面，我们来实现
sigmoid
函数。用
Python
可以像下面这样写出式（3.6）
表示的
sigmoid
函数。
def sigmoid(x):
return 1 / (1 + np.exp(-x))
这里，
np.exp(-x)
对应
exp(−
x
)
。这个实现没有什么特别难的地方，但
是要注意参数
x
为
NumPy
数组时，结果也能被正确计算。实际上，如果在
这个
sigmoid
函数中输入一个
NumPy
数组，则结果如下所示。
>>> x = np.array([-1.0, 1.0, 2.0])
>>> sigmoid(x)
array([ 0.26894142, 0.73105858, 0.88079708])
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