tf.transpose函数中文意思是转置，对于低维度的转置问题，很简单，不想讨论，直接转置就好(大家看下面文档，一看就懂)。

tf.transpose(a, perm=None, name='transpose')

Transposes a. Permutes the dimensions according to perm.

The returned tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor. Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors.

For example:

# 'x' is [[1 2 3]

# [4 5 6]]

tf.transpose(x) ==> [[1 4]

[2 5]

[3 6]]

# Equivalently

tf.transpose(x perm=[1, 0]) ==> [[1 4]

[2 5]

[3 6]]

# 'perm' is more useful for n-dimensional tensors, for n > 2

# 'x' is [[[1 2 3]

# [4 5 6]]

# [[7 8 9]

# [10 11 12]]]

# Take the transpose of the matrices in dimension-0

tf.transpose(b, perm=[0, 2, 1]) ==> [[[1 4]

[2 5]

[3 6]]

[[7 10]

[8 11]

[9 12]]]

Args:

•a: A Tensor.

•perm: A permutation of the dimensions of a.

•name: A name for the operation (optional).

Returns:

A transposed Tensor.

本文主要讨论高维度的情况：

为了形象理解高维情况，这里以矩阵组合举例：

先定义下： 2 x (3*4)表示2个3*4的矩阵，(其实，它是个3维张量)。

x = [[[1,2,3,4],[5,6,7,8],[9,10,11,12]],[[21,22,23,24],[25,26,27,28],[29,30,31,32]]]

输出：

---------------

[[[ 1  2  3  4]

[ 5  6  7  8]

[ 9 10 11 12]]

[[21 22 23 24]

[25 26 27 28]

[29 30 31 32]]]

---------------

重点来了：

tf.transpose的第二个参数perm=[0,1,2],0代表三维数组的高(即为二维数组的个数)，1代表二维数组的行，2代表二维数组的列。

tf.transpose(x, perm=[1,0,2])代表将三位数组的高和行进行转置。

我们写个测试程序如下：

import tensorflow as tf

#x = tf.constant([[1, 2 ,3],[4, 5, 6]])

x = [[[1,2,3,4],[5,6,7,8],[9,10,11,12]],[[21,22,23,24],[25,26,27,28],[29,30,31,32]]]

#a=tf.constant(x)

a=tf.transpose(x, [0, 1, 2])

b=tf.transpose(x, [0, 2, 1])

c=tf.transpose(x, [1, 0, 2])

d=tf.transpose(x, [1, 2, 0])

e=tf.transpose(x, [2, 1, 0])

f=tf.transpose(x, [2, 0, 1])

# 'perm' is more useful for n-dimensional tensors, for n > 2

# 'x' is [[[1 2 3]

# [4 5 6]]

# [[7 8 9]

# [10 11 12]]]

# Take the transpose of the matrices in dimension-0

#tf.transpose(b, perm=[0, 2, 1])

with tf.Session() as sess:

print ('---------------')

print (sess.run(a))

print ('---------------')

print (sess.run(b))

print ('---------------')

print (sess.run(c))

print ('---------------')

print (sess.run(d))

print ('---------------')

print (sess.run(e))

print ('---------------')

print (sess.run(f))

print ('---------------')

我们期待的结果是得到如下矩阵：

a: 2 x 3*4

b: 2 x 4*3

c: 3 x 2*4

d: 3 x 4*2

e: 4 x 3*2

f: 4 x 2*3

运行脚本，结果一致，如下：

---------------

[[[ 1 2 3 4]

[ 5 6 7 8]

[ 9 10 11 12]]

[[21 22 23 24]

[25 26 27 28]

[29 30 31 32]]]

---------------

[[[ 1 5 9]

[ 2 6 10]

[ 3 7 11]

[ 4 8 12]]

[[21 25 29]

[22 26 30]

[23 27 31]

[24 28 32]]]

---------------

[[[ 1 2 3 4]

[21 22 23 24]]

[[ 5 6 7 8]

[25 26 27 28]]

[[ 9 10 11 12]

[29 30 31 32]]]

---------------

[[[ 1 21]

[ 2 22]

[ 3 23]

[ 4 24]]

[[ 5 25]

[ 6 26]

[ 7 27]

[ 8 28]]

[[ 9 29]

[10 30]

[11 31]

[12 32]]]

---------------

[[[ 1 21]

[ 5 25]

[ 9 29]]

[[ 2 22]

[ 6 26]

[10 30]]

[[ 3 23]

[ 7 27]

[11 31]]

[[ 4 24]

[ 8 28]

[12 32]]]

---------------

[[[ 1 5 9]

[21 25 29]]

[[ 2 6 10]

[22 26 30]]

[[ 3 7 11]

[23 27 31]]

[[ 4 8 12]

[24 28 32]]]

---------------

最后，总结下：

[0, 1, 2]是正常显示，那么交换哪两个数字，就是把对应的输入张量的对应的维度对应交换即可。

---------------------

作者：cc19

来源：CSDN

原文：https://blog.csdn.net/cc1949/article/details/78422704

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