我就廢話不多說，咱直接看代碼吧！

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的轉置是根據 perm 的設定值來進行的。

返回數組的 dimension（尺寸、維度） i與輸入的 perm[i]的維度相一致。如果未給定perm，默認設置為 (n-1…0)，這里的 n 值是輸入變量的 rank 。因此默認情況下，這個操作執行了一個正規（regular）的2維矩形的轉置

例如：

x = [[1 2 3]   [4 5 6]]tf.transpose(x) ==> [[1 4]           [2 5]           [3 6]]tf.transpose(x) 等價于：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]]])

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