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。
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作者：cc19
来源：CSDN
原文：https://blog.csdn.net/cc1949/article/details/78422704
版权声明：本文为博主原创文章，转载请附上博文链接！