tf.transpose

Transposes a, where a is a Tensor.

tf.transpose(     a, perm=None, conjugate=False, name='transpose' )

Used in the notebooks

Used in the guide Used in the tutorials

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)>

a A Tensor.
perm A permutation of the dimensions of a. This should be a vector.
conjugate Optional bool. Setting it to True is mathematically equivalent to tf.math.conj(tf.transpose(input)).
name A name for the operation (optional).

A transposed 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.