Tensorflow:転置関数transpose解析

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の転置はpermの設定値に基づいて行われる.配列を返すdimension(寸法、次元)iは、入力したperm[i]の次元と一致する.permが指定されていない場合、デフォルトは(n-1...0)に設定され、ここでn値は入力変数のrankです.従って、デフォルトでは、この操作は、例えば、正規の2次元矩形の回転を実行する.

x =  [[1 2 3]
      [4 5 6]]

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

tf.transpose(x)    ：
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]]])