mindspore.ops.reshape
- mindspore.ops.reshape(input, shape)[source]
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
input (Tensor) – The shape of tensor is \((x_1, x_2, ..., x_R)\).
shape (Union[tuple[int], list[int], Tensor[int]]) – If shape is a tuple or list, its elements should be integers, and only constant value is allowed. i.e., \((y_1, y_2, ..., y_S)\). If shape is a Tensor, data type should be int32 or int64, and only one-dimensional tensor is supported.
- 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 \((y_1, ..., y_{k-1}, \frac{\prod_{i=1}^{R}x_{i}}{y_1\times ...\times y_{k-1}\times y_{k+1}\times...\times y_S} , y_{k+1}, ..., y_S)\)
- Raises
ValueError – The given shape contains more than one -1.
ValueError – The given shape contains elements less than -1.
ValueError – For scenarios where the given shape does not contain -1, the product of elements of the given shape is not equal to the product of the input's shape, \(\prod_{i=1}^{R}x_{i} \ne \prod_{i=1}^{S}y_{i}\), (Namely, it does not match the input's array size). And for scenarios where the given shape contains -1, the product of elements other than -1 of the given shape is an aliquant part of the product of the input's shape \(\prod_{i=1}^{R}x_{i}\).
- 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 = ops.reshape(input, (3, 2)) >>> print(output) [[-0.1 0.3] [ 3.6 0.4] [ 0.5 -3.2]]