|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/array_ops.py#L1996-L2073) | Transposes last two dimensions of tensor `a`. #### View aliases **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.matrix_transpose`](https://www.tensorflow.org/api_docs/python/tf/linalg/matrix_transpose)
tf.linalg.matrix_transpose( a, name='matrix_transpose', conjugate=False ) ### Used in the notebooks | Used in the tutorials | |---| | - [TensorFlow Probability Case Study: Covariance Estimation](https://www.tensorflow.org/probability/examples/TensorFlow_Probability_Case_Study_Covariance_Estimation) | #### 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`](https://www.tensorflow.org/api_docs/python/tf/linalg/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`](https://www.tensorflow.org/api_docs/python/tf/linalg/matrix_transpose) returns a new tensor with the items permuted.