mindspore.ops.unstack

mindspore.ops.unstack(input_x, axis=0)[source]

Unstacks tensor in specified axis, this is the opposite of mindspore.ops.stack(). Assuming input is a tensor of rank R, output tensors will have rank (R-1).

Parameters

input_x (Tensor) – The shape is \((x_1, x_2, ..., x_R)\). A tensor to be unstacked and the rank of the tensor must be greater than 0.
axis (int) – Dimension along which to unpack. Default: 0 . Negative values wrap around. The range is [-R, R).

Returns

A tuple of tensors, the shape of each objects is the same. Given a tensor of shape \((x_1, x_2, ..., x_R)\). If \(0 \le axis\), the shape of tensor in output is \((x_1, x_2, ..., x_{axis}, x_{axis+2}, ..., x_R)\).

Raises

ValueError – If axis is out of the range [-len(input_x.shape), len(input_x.shape)).

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))
>>> output = ops.unstack(input_x, 0)
>>> print(output)
(Tensor(shape=[4], dtype=Int64, value= [1, 1, 1, 1]), Tensor(shape=[4], dtype=Int64, value= [2, 2, 2, 2]))