mindspore.Tensor.transpose

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Tensor.transpose(dim0, dim1) → Tensor

Interchange two axes of a tensor.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

  • dim0 (int) – Specifies the first dimension to be transposed.

  • dim1 (int) – Specifies the second dimension to be transposed.

Returns

Transposed tensor, has the same data type as self.

Raises

  • TypeError – If dim0 or dim1 is not integer.

  • ValueError – If dim0 or dim1 is not in the range of \([-ndim, ndim-1]\).

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> input = Tensor(np.ones((2,3,4), dtype=np.float32))
>>> output = Tensor.transpose(input, 0, 2)
>>> print(output.shape)
(4, 3, 2)
Tensor.transpose(*axes) → Tensor

Permutes the dimensions of the self tensor according to self permutation.

For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector please refer to mindspore.ops.expand_dims(). For a 2-D array, this is a standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape is \((i[0], i[1], ... i[n-2], i[n-1])\), then a.transpose().shape is \((i[n-1], i[n-2], ... i[1], i[0])\).

Note

On GPU and CPU, if the value of axes is negative, its actual value is axes[i] + rank(self).

Parameters

axes (tuple[int]) – The permutation to be converted. The elements in axes are composed of the indexes of each dimension of self. The length of axes and the shape of self must be the same. Only constant value is allowed. Must be in the range [-rank(self), rank(self)).

Returns

Tensor, the type of output tensor is the same as self and the shape of output tensor is decided by the shape of self and the value of axes.

Raises

  • TypeError – If axes is not a tuple.

  • ValueError – If length of shape of self is not equal to length of shape of axes.

  • ValueError – If the same element exists in axes.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> input = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32)
>>> axes = (0, 2, 1)
>>> output = Tensor.transpose(input, axes)
>>> print(output)
[[[ 1.  4.]
  [ 2.  5.]
  [ 3.  6.]]
 [[ 7. 10.]
  [ 8. 11.]
  [ 9. 12.]]]