deep_learning_Function_tensorflow_transpose()

tf.transpose()的用法

一、tensorflow官方文档内容

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' 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(x, 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.

 

二、中文翻译

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

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。

 

例子：

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

# Equivalently（等价于）
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(x, perm = [ 0 , 2 , 1 ]) = = > [[[ 1   4]
                                      [ 2   5]
                                      [ 3   6]]

                                     [[ 7  10]
                                      [ 8  11]
                                      [ 9  12]]]

 

参数：　　

• a: a 是一个张量（Tensor）
• perm: perm 是 a 维度的置换
• name:操作的名称(可选).

 

返回值:

  　返回的是一个转置的张量。

 

三、解释

tf.transpose(input, [dimension_1, dimenaion_2,..,dimension_n]):这个函数主要适用于交换输入张量的不同维度用的，如果输入张量是二维，就相当是转置。dimension_n是整数，如果张量是三维，就是用0,1,2来表示。这个列表里的每个数对应相应的维度。如果是[2,1,0]，就把输入张量的第三维度和第一维度交换。　 

 

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参考链接：

　　1、 tf.transpose函数的用法：  https://i.cnblogs.com/EditPosts.aspx?opt=1

　　2、 tensorflow中的不懂得知识点——转置函数 transpose ： http://blog.csdn.net/u010417185/article/details/51900441

　　3、tensorflow官方文档 ：  https://www.tensorflow.org/versions/r1.3/api_docs/python/tf/transpose

转自：https://www.cnblogs.com/hezhiyao/p/8476160.html