tf.transpose

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. 

按照翻译来解释，就是转置的意思．如果把一个矩阵
［［１　２　０］
　［３　４　０］］　转置，那么很好理解，就是
［　［１　３］
　　［２　４］
　　［０　０］
　］
这是二维的矩阵，一旦这个矩阵的维度大于等于３维，那么必须定义怎么转置，这就是perm参数的意义．假如一个矩阵有n维，那么perm就是一个大小为n的列表，其值就是［０：n-1］的一个排列.perm中，位置i的值j代表转置后的矩阵j维就是转置前矩阵i维的值．
例子：

arr = np.zeros([2, 1 ,3])
arr
after_transpose = tf.transpose(arr, [1, 2, 0])
print(after_transpose)
# Tensor("transpose:0", shape=(1, 3, 2), dtype=float64)