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 7章 卷积神经网络
224
1. 展开输入数据。
2. 求各行的最大值。
3. 转换为合适的输出大小。
各阶段的实现都很简单，只有一两行代码。
最大值的计算可以使用NumPy的
np.max
方 法。
np.max
可以指定
axis
参数，并在这个参数指定的各个轴方向上求最大值。比如，如
果写成
np.max(x, axis=1)
，就可以在输入
x
的第1维的各个轴方向
上求最大值。
以上就是池化层的
forward
处理的介绍。如上所述，通过将输入数据展
开为容易进行池化的形状，后面的实现就会变得非常简单。
关于池化层的
backward
处理，之前已经介绍过相关内容，这里就不再介绍了。
另外，池化层的
backward
处理可以参考
ReLU
层的实现中使用的
max
的反向
传 播（5.5.1节）。池化层的实现在
common/layer.py
中，有兴趣的读者可以参考。
7.5
CNN的实现
我们已经实现了卷积层和池化层，现在来组合这些层，搭建进行手写数
字识别的
CNN
。这里要实现如图7
-
23 所示的
CNN
。
图7-23 简单CNN 的网络构成
Conv PoolingReLU Affine ReLU SoftmaxAffine
7.5 CNN的实现
225
如图7
-
23 所示，网络的构成是“
Convolution - ReLU - Pooling ...
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