tf.transpose函式的用法講解（多維情況，看似複雜，其實也簡單）
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tf.transpose函式中文意思是轉置，對於低維度的轉置問題，很簡單，不想討論，直接轉置就好（大家看下面文件，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.
本文主要討論高維度的情況：
為了形象理解高維情況，這裡以矩陣組合舉例：
先定義下： 2 x （3*4）表示2個3*4的矩陣，（其實，它是個3維張量）。
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]]]
輸出：
---------------
[[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
[[21 22 23 24]
[25 26 27 28]
[29 30 31 32]]]
---------------
重點來了：
tf.transpose的第二個引數perm=[0,1,2],0代表三維陣列的高（即為二維陣列的個數），1代表二維陣列的行，2代表二維陣列的列。
tf.transpose(x, perm=[1,0,2])代表將三位陣列的高和行進行轉置。
我們寫個測試程式如下：
-
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 ('---------------')
我們期待的結果是得到如下矩陣：
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*2
執行指令碼，結果一致，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]]] -
---------------
最後，總結下：
[0, 1, 2]是正常顯示，那麼交換哪兩個數字，就是把對應的輸入張量的對應的維度對應交換即可。
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