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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.

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- Minor inconsistencies between the UI and descriptions.

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Risk Warnings

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mindspore.Tensor.reshape

Tensor.reshape(*shape)

Rearranges the input Tensor based on the given shape .

The shape can only have one -1 at most, in which case it's inferred from the remaining dimensions and the number of elements in the input.

Parameters

shape (Union[int, tuple[int], list[int]]) – If shape is a tuple or list, its elements should be integers, and only constant value is allowed. i.e., $(y_1,y_2,...,y_S)$.

Returns

Tensor, If the given shape does not contain -1, the shape of tensor is $(y_1,y_2,...,y_S)$. If the k-th position in the given shape is -1, the shape of tensor is $(y_1,...,y_{k-1},\frac{\prod _{i=1}^R{x}_i}{y_1\times ...\times y_{k-1}\times y_{k+1}\times ...\times y_S},y_{k+1},...,y_S)$, in where the shape of input tensor is $(x_1,x_2,...,x_R)$.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> output = input.reshape(3, 2)
>>> print(output)
[[-0.1  0.3]
 [ 3.6  0.4]
 [ 0.5 -3.2]]