Tensorflow:转置函数 transpose的使用详解

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]]])

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