tensorflow.pad如何工作？ [英] How does tensorflow.pad work?

问题描述

有tensorflow.pad（）的示例：

There is the example of tensorflow.pad():

# 't' =  is [[1, 2, 3], [4, 5, 6]].
# 'paddings' is [[1, 1,], [2, 2]].
#  rank of 't' is 2.
' tf.pad(t, paddings, "CONSTANT")'
==> [[0, 0, 0, 0, 0, 0, 0],
     [0, 0, 1, 2, 3, 0, 0],
     [0, 0, 4, 5, 6, 0, 0],
     [0, 0, 0, 0, 0, 0, 0]]

我的问题是如何在输入的每一维中填充零？而且t的形状为[2,3]，为什么pad（）之后的输出为[4，x]， 4如何出现？
感谢您的帮助！！！

my question is how to pad zeros in every dimention of input? And the shape of t is [2,3], why output after pad() is [4,x],how the '4' comes? Thanks for helping me!!!

推荐答案

文档对此非常清楚。 对于输入的每个维D，paddings [D，0]指示在该维中张量的内容之前要添加多少值，而paddings [D，1]指示在内容后的张量内容之前要添加多少值

The documentation is pretty clear about this. For each dimension D of input, paddings[D, 0] indicates how many values to add before the contents of tensor in that dimension, and paddings[D, 1] indicates how many values to add after the contents of tensor in that dimension.

为什么输出是[4，x]？

why out put is [4, x]?

4是维度0的大小，维度0具有填充 [1，1] ，根据文档，其中在 t 的零维之前添加一个，在其后添加一个， t 的零维的大小为2， 2 +1 + 1 ，结果为4。即它分别在t的开头和结尾处填充了一个零行。与维度1类似，由于 padding [1] 为 [2,2] ，因此在 t 的开头和结尾处添加了两个零列

4 is the size of dimension 0, dimension 0 has padding [1, 1], which according to the docs add one before the zero dimension of t and one after, the size of zero dimension of t is 2, 2 + 1 + 1, you have 4 in the result. i.e. it padded one zero row at the beginning and ending of t respectively. Similarly for dimension 1, since padding[1] is [2,2], two zero columns are added to t at the beginning and ending respectively.

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