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: ATensor.perm: A permutation of the dimensions ofa.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。
