October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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使用卷积神经网络的深度计算机视觉
｜
397
•
每个颜色通道的像素强度表示为从 0 到 255 的字节，因此我们除以 255 即可缩放这
些特征，得到从 0 到 1 的浮点数。
•
然后，我们创建两个 7
×
7 的滤波器（一个在中间带有垂直白线，另一个在中间带有
水平白线）。
•
我们使用 tf.nn.conv2d() 函数应用于两个图像，这是 TensorFlow 的低层深度学习
API 的一部分。在此示例中，我们使用零填充（padding
="SAME"）和步幅为 1。
•
最后，绘制一个结果特征图（类似于图 14-5 中的右上图）。
tf.nn.conv2d() 行值得更多解释：
•
images 是输入的小批量（4D 张量，如前所述）。
•
filter 是要应用的一组滤波器（也是 4D 张量，如前所述）。
•
strides 等于 1，但也可以是包含四个元素的一维度组，其中两个中间元素是垂直步
幅和水平步幅（
s
h
和
s
w
）。第一个元素和最后一个元素必须等于 1。它们可能有一天用
于指定批处理步幅（跳过某些实例）和通道步幅（跳过某些上一层的特征图或通道）。
•
padding 必须为 "SAME" 或 "VALID"：
◆
如果设置为 "SAME"，则卷积层在必要时使用零填充。将输出大小设置为输入神
经元的数量除以步幅（向上取整）所得的值。例如，如果输入大小为 13 而步幅
为 5（见图 14-7），则输出大小为 3（即 13/5 = 2.6，向上舍入为 3）。然后根据
需要在输入周围尽可能均匀地添加零。当 ...
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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.