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Transposes `a`

.

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

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.

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]]]

Args | |
---|---|

`a` | A `Tensor` . |

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

`name` | A name for the operation (optional). |

`conjugate` | Optional bool. Setting it to `True` is mathematically equivalent to tf.math.conj(tf.transpose(input)). |

Returns | |
---|---|

A transposed `Tensor` . |

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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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/transpose