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Update why-conv.md #1326

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4 changes: 2 additions & 2 deletions chapter_convolutional-neural-networks/why-conv.md
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Expand Up @@ -91,12 +91,12 @@ $$(f * g)(\mathbf{x}) = \int f(\mathbf{z}) g(\mathbf{x}-\mathbf{z}) d\mathbf{z}.

$$(f * g)(i) = \sum_a f(a) g(i-a).$$

对于二维张量，则为$f$的索引$(a, b)$和$g$的索引$(i-a, j-b)$上的对应加和：
对于二维张量，则为$f$的索引$(a, b)$和$g$的索引$(i-a, j-b)$上的对应的求和：

$$(f * g)(i, j) = \sum_a\sum_b f(a, b) g(i-a, j-b).$$
:eqlabel:`eq_2d-conv-discrete`

这看起来类似于 :eqref:`eq_conv-layer`，但有一个主要区别：这里不是使用$(i+a, j+b)$，而是使用差值。然而，这种区别是表面的，因为我们总是可以匹配 :eqref:`eq_conv-layer`和 :eqref:`eq_2d-conv-discrete`之间的符号。我们在 :eqref:`eq_conv-layer`中的原始定义更正确地描述了*互相关*（cross-correlation），这个问题将在下一节中讨论。
这看起来类似于 :eqref:`eq_conv-layer`，但有一个主要区别：这里不是使用$(i+a, j+b)$，而是使用$(i-a, j-b)$。当然，这种区别只是表面的，因为我们总是可以匹配 :eqref:`eq_conv-layer`和 :eqref:`eq_2d-conv-discrete`之间的符号。我们在 :eqref:`eq_conv-layer`中的原始定义更正确地描述了*互相关*（cross-correlation），这个问题将在下一节中讨论。

## “沃尔多在哪里”回顾

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