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 8章 深度学习
256
行处理，而只由卷积层构成的网络中，空间容量可以保持原样直到最后的输出。
如图8
-
20 所示，
FCN
的特征在于最后导入了扩大空间大小的处理。基
于这个处理，变小了的中间数据可以一下子扩大到和输入图像一样的大小。
FCN
最后进行的扩大处理是基于双线性插值法的扩大（双线性插值扩大）。
FCN
中，这个双线性插值扩大是通过去卷积（逆卷积运算）来实现的（细节请
参考
FCN
的论文
[37]
）。
图8-20 FCN 的概略图（引用自文献[37]）
全连接层中，输出和全部的输入相连。使用卷积层也可以实现与此
结构完全相同的连接。比如，针对输入大小是32×10×10（通道
数32、高10、长10）的数据的全连接层可以替换成滤波器大小为
32×10×10的卷积层。如果全连接层的输出节点数是100，那么在
卷积层准备 100 个 32×10×10 的滤波器就可以实现完全相同的处理。
像这样，全连接层可以替换成进行相同处理的卷积层。
8.4.3
图像标题的生成
有一项融合了计算机视觉和自然语言的有趣的研究，该研究如图8
-
21 所
示，给出一个图像后，会自动生成介绍这个图像的文字（图像的标题）。
给出一个图像后，会像图8
-
21 一样自动生成表示该图像内容的文本。比
如，左上角的第一幅图像生成了文本“
A person riding a motorcycle on a
8.4 深度学习的应用案例
257
dirt road.
”（在没有铺装的道路上骑摩托车的人），而且这个文本只从该图像
自动生成。文本的内容和图像确实是一致的。 ...
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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.