mindspore.ops.Split

class mindspore.ops.Split(axis=0, output_num=1)

Splits the input tensor into output_num of tensors along the given axis and output numbers.

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

Parameters

• axis (int) – Index of the split position. Default: 0 .

• output_num (int) – The number of output tensors. Must be positive int. Default: 1 .

Inputs:

• input_x (Tensor) - The shape of tensor is \((x_0, x_1, ..., x_{R-1})\), R >= 1.

Outputs:

tuple[Tensor], the shape of each output tensor is the same, which is \((x_0, x_1, ..., x_{axis}/{output\_num}, ..., x_{R-1})\). And the data type is the same as input_x.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> split = ops.Split(1, 2)
>>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]), mindspore.int32)
>>> print(x)
[[1 1 1 1]
 [2 2 2 2]]
>>> output = split(x)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Int32, value=
[[1, 1],
 [2, 2]]), Tensor(shape=[2, 2], dtype=Int32, value=
[[1, 1],
 [2, 2]]))
>>> split = ops.Split(1, 4)
>>> output = split(x)
>>> print(output)
(Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]))