April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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118
｜
第
8
章
子集。）如果用图表示的话，全连接神经网络可以表示为一个完全二分图，其中每个输入
节点都连接到每个输出节点（见图
8-7
）。
全连接神经网络
输出
输入
图 8-7：全连接神经网络的图表示
全连接层包含最大可能数目的参数（输入数 × 输出数），因此它非常昂贵。如此密集的连
接可以让网络探测包含所有输入的全局模式。由于这个原因，
AlexNet
的最后两层是全连
接层。全连接层的输出彼此之间还是独立的，依输入情况而定。
8.3.2
卷积层
与全连接层不同，卷积层仅使用输入的一个子集来生成输出。转换过程在输入中“移动”，
每次使用若干特征生成输出。为简单起见，我们可以对不同输入集合使用同样的权重，而
不用为每个输入集合学习新权重。
在数学上，卷积算子接受两个函数作为输入，生成一个函数作为输出。它要先翻转一个输
入函数，再将它沿着另一个函数移动，并输出在每个点上递增的曲线下的面积：
计算一条曲线下总面积的方法是求它的积分。这个算子对于输入是对称的，也就是说，翻
转第一个函数还是第二个函数都没有关系，输出是一样的。
在介绍图像梯度时（
8.2.1
节），我们已经见过了一个简单的卷积示例。但卷积的数学定义
还是显得有些复杂，这自有其原因。使用信号处理中的例子来解释卷积背后的思想是最容
易的。
假设有一个小黑盒。为了知道这个黑盒的用处，我们向它传递一个单位的刺激信号，然后
在一张纸上记录输出结果，直到再没有对初始刺激的反应为止。最后得到的随时间变化的
自动特征生成：图像特征提取和深度学习
｜
119
函数就是
响应函数
，我们称它为 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.