April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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122
｜
第
8
章
>>> ax2.axis('off')
>>> ax2.imshow(blurred_cat, cmap=plt.cm.gray)
>>> ax2.set_title('After convolving with a Gaussian filter')
>>> ax2.set_adjustable('box-forced')
输入图像 使用高斯滤波器求卷积后
图 8-10：应用二维高斯滤波器前后的小猫图片
AlexNet
中的卷积层是三维的，也就是说它们处理的是来自于前一层的三维像素（表示三
维图像空间的数组值）。第一个卷积神经网络接受原始
RGB
图像并学习出卷积滤波器，可
用于在所有三个颜色通道上的局部图像邻域。随后的几层接受输入的三位像素，沿着空间
和核维度进行处理。参考图
8-14
获得更多详细信息。
8.3.3
ReLU
变换
神经网络的输出通常被传递到另一种非线性变换中，这种非线性变换又称为
激活函数
。常
用的激活函数包括
tanh
函数（一个位于
-
1
和
1
之间的平滑非线性函数）、
sigmoid
函数
（一个位于
0
和
1
之间的平滑非线性函数，在
4.3
节中介绍过），以及所谓的
线性整流函数
（
ReLU
）。
ReLU
是线性函数的一个简单变种，它的负数部分都被归零。也就是说，它裁剪
掉了负数部分，保留了无界的正数部分。
ReLU
的取值范围是
0
到
∞
。
常用激活函数
ReLU
是一个负数部分归零的线性函数：
ReLU(
x
) = max(0,
x
)
tanh
函数是一个平滑地从
-
1
增长到
1
的三角函数：
sinh()
tanh( ...
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