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
48
另一个不同点是，相对于阶跃函数只能返回0 或 1，
sigmoid
函数可以返
回 0.731
...
、0.880
...
等实数（这一点和刚才的平滑性有关）。也就是说，感
知机中神经元之间流动的是0 或 1的二元信号，而神经网络中流动的是连续
的实数值信号。
如果把这两个函数与水联系起来，则阶跃函数可以比作“竹筒敲石”
A
，
sigmoid
函数可以比作“水车”。阶跃函数就像竹筒敲石一样，只做是否传送
水（0 或1）两个动作，而
sigmoid
函数就像水车一样，根据流过来的水量相应
地调整传送出去的水量。
接着说一下阶跃函数和
sigmoid
函数的共同性质。阶跃函数和
sigmoid
函数虽然在平滑性上有差异，但是如果从宏观视角看图 3
-
8，可以发现它们
具有相似的形状。实际上，两者的结构均是“输入小时，输出接近 0（为 0）；
随着输入增大，输出向 1 靠近（变成 1）”。也就是说，当输入信号为重要信息时，
阶跃函数和
sigmoid
函数都会输出较大的值；当输入信号为不重要的信息时，
两者都输出较小的值。还有一个共同点是，不管输入信号有多小，或者有多
大，输出信号的值都在0 到 1之间。
3.2.6
非线性函数
阶跃函数和
sigmoid
函数还有其他共同点，就是两者均为非线性函数。
sigmoid
函数是一条曲线，阶跃函数是一条像阶梯一样的折线，两者都属于
非线性的函数。
在介绍激活函数时，经常会看到“非线性函数”和“线性函数”等术语。
函数本来是输入某个值后会返回一个值的转换器。向这个转换器输
入某个值后，输出值是输入值的常数倍的函数称为线性函数 ...
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