|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/array_ops.py#L1826-L1904) | 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 | |---|---| | - [Matrix approximation with Core APIs](https://www.tensorflow.org/guide/core/matrix_core) - [Effective Tensorflow 2](https://www.tensorflow.org/guide/effective_tf2) - [Better performance with tf.function](https://www.tensorflow.org/guide/function) - [Advanced automatic differentiation](https://www.tensorflow.org/guide/advanced_autodiff) - [TensorFlow basics](https://www.tensorflow.org/guide/basics) | - [Scalable model compression](https://www.tensorflow.org/tutorials/optimization/compression) - [Time series forecasting](https://www.tensorflow.org/tutorials/structured_data/time_series) - [Quantum data](https://www.tensorflow.org/quantum/tutorials/quantum_data) - [Neural machine translation with a Transformer and Keras](https://www.tensorflow.org/text/tutorials/transformer) - [MoViNet for streaming action recognition](https://www.tensorflow.org/hub/tutorials/movinet) | 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) 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) '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`](https://www.tensorflow.org/api_docs/python/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]) > [!NOTE] > **Note:** This has a shorthand [`linalg.matrix_transpose`](https://www.tensorflow.org/api_docs/python/tf/linalg/matrix_transpose)):


| ## Args || |---|---| | `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). |



| ## Returns || |---|---| | 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.