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 > 2x = tf.constant([[[ 1, 2, 3], [ 4, 5, 6]], [[ 7, 8, 9], [10, 11, 12]]])# Take the transpose of the matrices in dimension-0tf.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。