我就废话不多说，咱直接看代码吧！
tf.transpose
transpose( a, perm=None, name='transpose' )
Defined in tensorflow/python/ops/array_ops.py.
See the guides: Math > Matrix Math Functions, Tensor Transformations > Slicing and Joining
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 = tf.constant([[1, 2, 3], [4, 5, 6]])
tf.transpose(x) # [[1, 4]
# [2, 5]
# [3, 6]]
tf.transpose(x, perm=[1, 0]) # [[1, 4]
# [2, 5]
# [3, 6]]
# 'perm' is more useful for n-dimensional tensors, for n > 2
x = tf.constant([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
# Take the transpose of the matrices in dimension-0
tf.transpose(x, perm=[0, 2, 1]) # [[[1, 4],
# [2, 5],
# [3, 6]],
# [[7, 10],
# [8, 11],
# [9, 12]]]
a的转置是根据 perm 的设定值来进行的。
返回数组的 dimension（尺寸、维度） i与输入的 perm[i]的维度相一致。如果未给定perm，默认设置为 (n-1…0)，这里的 n 值是输入变量的 rank 。因此默认情况下，这个操作执行了一个正规（regular）的2维矩形的转置
例如：
x = [[1 2 3]
[4 5 6]]
tf.transpose(x) ==> [[1 4]
[2 5]
[3 6]]
tf.transpose(x) 等价于：
tf.transpose(x perm=[1, 0]) ==> [[1 4]
[2 5]
[3 6]]
a=tf.constant([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
x=tf.transpose(a,[1,0,2])
array([[[ 1, 2, 3],
[ 7, 8, 9]],
[[ 4, 5, 6],
[10, 11, 12]]])
x=tf.transpose(a,[0,2,1])
array([[[ 1, 4],
[ 2, 5],
[ 3, 6]],
[[ 7, 10],
[ 8, 11],
[ 9, 12]]])
x=tf.transpose(a,[2,1,0])
array([[[ 1, 7],
[ 4, 10]],
[[ 2, 8],
[ 5, 11]],
[[ 3, 9],
[ 6, 12]]])
array([[[ 1, 7],
[ 4, 10]],
[[ 2, 8],
[ 5, 11]],
[[ 3, 9],
[ 6, 12]]])
x=tf.transpose(a,[1,2,0])
array([[[ 1, 7],
[ 2, 8],
[ 3, 9]],
[[ 4, 10],
[ 5, 11],
[ 6, 12]]])
以上这篇Tensorflow:转置函数 transpose的使用详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。
