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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

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- Incorrect version numbers, including software package names and version numbers on the UI.

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Risk Warnings

- Risk Warnings:

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Content Compliance

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- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

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mindspore.ops.atleast_3d

mindspore.ops.atleast_3d(inputs)

Reshapes Tensor in inputs, every Tensor has at least 3 dimension after this operation.

Scalar, 1-D or 2-D Tensor is converted to 3-D Tensor, tensor with higher dimensions will be returned as it is.

Parameters

inputs (Union[Tensor, list[Tensor]]) – One or more input tensors.

Returns

Tensor or list[Tensor]. If returned a list, every element a in that list satisfies a.ndim >= 3. For example, a 1-D Tensor of shape $(N,)$ becomes a Tensor of shape $(1,N,1)$, and a 2-D Tensor of shape $(M,N)$ becomes a tensor of shape $(M,N,1)$.

Raises

TypeError – If the input is not a tensor or a list of tensors.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x1 = Tensor(np.ones((2, 3)))
>>> x2 = Tensor(np.ones(()))
>>> x3 = Tensor(np.ones(5))
>>> out = ops.atleast_3d([x1, x2, x3])
>>> print(out[0].asnumpy())
[[[1.]
  [1.]
  [1.]]

 [[1.]
  [1.]
  [1.]]]
>>> print(out[1].asnumpy())
[[[1.]]]
>>> print(out[2].asnumpy())
[[[1.]
  [1.]
  [1.]
  [1.]
  [1.]]]