TensorFlow 2 version | View source on GitHub |
Transposes last two dimensions of tensor a
.
tf.linalg.matrix_transpose(
a, name='matrix_transpose', conjugate=False
)
For example:
x = tf.constant([[1, 2, 3], [4, 5, 6]])
tf.linalg.matrix_transpose(x) # [[1, 4],
# [2, 5],
# [3, 6]]
x = tf.constant([[1 + 1j, 2 + 2j, 3 + 3j],
[4 + 4j, 5 + 5j, 6 + 6j]])
tf.linalg.matrix_transpose(x, conjugate=True) # [[1 - 1j, 4 - 4j],
# [2 - 2j, 5 - 5j],
# [3 - 3j, 6 - 6j]]
# Matrix with two batch dimensions.
# x.shape is [1, 2, 3, 4]
# tf.linalg.matrix_transpose(x) is shape [1, 2, 4, 3]
Note that tf.matmul
provides kwargs allowing for transpose of arguments.
This is done with minimal cost, and is preferable to using this function. E.g.
# Good! Transpose is taken at minimal additional cost.
tf.matmul(matrix, b, transpose_b=True)
# Inefficient!
tf.matmul(matrix, tf.linalg.matrix_transpose(b))
Args | |
---|---|
a
|
A Tensor with rank >= 2 .
|
name
|
A name for the operation (optional). |
conjugate
|
Optional bool. Setting it to True is mathematically equivalent
to tf.math.conj(tf.linalg.matrix_transpose(input)).
|
Returns | |
---|---|
A transposed batch matrix Tensor .
|
Raises | |
---|---|
ValueError
|
If a is determined statically to have rank < 2 .
|
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, linalg.matrix_transpose
returns a new
tensor with the items permuted.