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Transposes a
, where a
is a Tensor.
tf.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 (n1...0), where n is the rank
of the input tensor. Hence, by default, this operation performs a regular
matrix transpose on 2D 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 ndimensional 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 dimension0 (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  

A transposed Tensor .

numpy compatibility
In numpy
transposes are memoryefficient 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.