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
250
图 8
-
15 中出现的
cuDNN
是在
CUDA
上运行的库，它里面实现了为深度学习
最优化过的函数等。
图8-15 使用CPU 的“ 16-core Xeon CPU”和 GPU 的 “Titan 系列”进行AlexNet 的
学习时分别所需的时间（引用自文献[27]）
通过
im2col
可以将卷积层进行的运算转换为大型矩阵的乘积。这个
im2col
方式的实现对GPU来说是非常方便的实现方式。这是因为，
相比按小规模的单位进行计算，GPU更擅长计算大规模的汇总好的
数据。也就是说，通过基于
im2col
以大型矩阵的乘积的方式汇总计算，
更容易发挥出GPU 的能力。
8.3.3
分布式学习
虽然通过
GPU
可以实现深度学习运算的高速化，但即便如此，当网络
较深时，学习还是需要几天到几周的时间。并且，前面也说过，深度学习伴
随着很多试错。为了创建良好的网络，需要反复进行各种尝试，这样一来就
必然会产生尽可能地缩短一次学习所需的时间的要求。于是，将深度学习的
学习过程扩展开来的想法（也就是分布式学习）就变得重要起来。
为了进一步提高深度学习所需的计算的速度，可以考虑在多个
GPU
或
8.3 深度学习的高速化
251
者多台机器上进行分布式计算。现在的深度学习框架中，出现了好几个支持
多
GPU
或者多机器的分布式学习的框架。 ...
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