Jiangning Liu - 1 year ago 39

Python Question

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]]

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!!!

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Answer Source

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.*

why out put is [4, x]?

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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