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# tf.transpose

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. 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)  # [[1, 4]
#  [2, 5]
#  [3, 6]]

# Equivalently
tf.transpose(x, perm=[1, 0])  # [[1, 4]
#  [2, 5]
#  [3, 6]]

# 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)  # [[1 - 1j, 4 - 4j],
#  [2 - 2j, 5 - 5j],
#  [3 - 3j, 6 - 6j]]

# 'perm' is more useful for n-dimensional tensors, for n > 2
x = tf.constant([[[ 1,  2,  3],
[ 4,  5,  6]],
[[ 7,  8,  9],
[10, 11, 12]]])

# Take the transpose of the matrices in dimension-0
# (this common operation has a shorthand `linalg.matrix_transpose`)
tf.transpose(x, perm=[0, 2, 1])  # [[[1,  4],
#   [2,  5],
#   [3,  6]],
#  [[7, 10],
#   [8, 11],
#   [9, 12]]]
``````

`a` A `Tensor`.
`perm` A permutation of the dimensions of `a`.
`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.

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