Transposes a
, where a
is a Tensor.
meridian
.
backend
.
transpose
(
a
,
perm
=
None
,
conjugate
=
False
,
name
=
'transpose'
)
Permutes the dimensions according to the value of 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.
If conjugate is True
and a.dtype
is either complex64
or complex128
then the values of a
are conjugated and transposed.
For example:
>>>
x
=
tf
.
constant
([[
1
,
2
,
3
],
[
4
,
5
,
6
]])
>>>
tf
.
transpose
(
x
)
< tf
.
Tensor
:
shape
=
(
3
,
2
),
dtype
=
int32
,
numpy
=
array
([[
1
,
4
],
[
2
,
5
],
[
3
,
6
]],
dtype
=
int32
)
>
Equivalently, you could call tf.transpose(x, perm=[1, 0])
.
If x
is complex, setting conjugate=True gives the conjugate transpose:
>>>
x
=
tf
.
constant
([[
1
+
1j
,
2
+
2j
,
3
+
3j
],
...
[
4
+
4j
,
5
+
5j
,
6
+
6j
]])
>>>
tf
.
transpose
(
x
,
conjugate
=
True
)
< tf
.
Tensor
:
shape
=
(
3
,
2
),
dtype
=
complex128
,
numpy
=
array
([[
1.
-
1.j
,
4.
-
4.j
],
[
2.
-
2.j
,
5.
-
5.j
],
[
3.
-
3.j
,
6.
-
6.j
]])
>
'perm' is more useful for n-dimensional tensors where n > 2:
>>>
x
=
tf
.
constant
([[[
1
,
2
,
3
],
...
[
4
,
5
,
6
]],
...
[[
7
,
8
,
9
],
...
[
10
,
11
,
12
]]])
As above, simply calling tf.transpose
will default to perm=[2,1,0]
.
To take the transpose of the matrices in dimension-0 (such as when you are
transposing matrices where 0 is the batch dimension), you would set perm=[0,2,1]
.
>>>
tf
.
transpose
(
x
,
perm
=[
0
,
2
,
1
])
< tf
.
Tensor
:
shape
=(
2
,
3
,
2
),
dtype
=
int32
,
numpy
=
array
([[[
1
,
4
],
[
2
,
5
],
[
3
,
6
]],
[[
7
,
10
],
[
8
,
11
],
[
9
,
12
]]],
dtype
=
int32
)
>
Returns
Tensor
.numpy compatibility
In numpy
transposes are memory-efficient constant time operations as they
simply return a new view of the same data with adjusted strides
.
TensorFlow does not support strides, so transpose
returns a new tensor with
the items permuted.
