mindspore.ops.Unstack

class mindspore.ops.Unstack(axis=0, num=None)[source]

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

Refer to mindspore.ops.unstack() for more details.

Parameters

axis (int) – Dimension along which to unpack. Default: 0 . Negative values wrap around. The range is [-R, R).
num (Union[None, int]) – The number of output tensors. Automatically inferred by input_x and axis if None . Default: None .

Inputs:

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.

Outputs:

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 \((x_1, x_2, ..., x_{axis}, x_{axis+2}, ..., x_R)\).

Supported Platforms:

Ascend GPU CPU

Examples

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