mindspore.numpy.array_split

mindspore.numpy.array_split(x, indices_or_sections, axis=0)[source]

Splits a tensor into multiple sub-tensors.

Note

Currently, array_split only supports mindspore.float32 on CPU.

The only difference between np.split and np.array_split is that np.array_split allows indices_or_sections to be an integer that does not equally divide the axis. For a tensor of length l that should be split into n sections, it returns $l % n$ sub-arrays of size $l / / n + 1$ and the rest of size $l / / n$ .

Parameters

x (Tensor) – A Tensor to be divided.
indices_or_sections (Union[int, tuple(int), list(int)]) – If integer, $N$ , the tensor will be divided into $N$ tensors along axis. If tuple(int), list(int) or of sorted integers, the entries indicate where along axis the array is split. For example, $[2, 3]$ would, for $a x i s = 0$ , result in three sub-tensors $x [: 2]$ , $x [2 : 3]$ and $x [3 :]$ . If an index exceeds the dimension of the array along axis, an empty sub-array is returned correspondingly.
axis (int) – The axis along which to split. Default: 0 .

Returns

A list of sub-tensors.

Raises

TypeError – If argument indices_or_sections is not integer, tuple(int) or list(int) or argument axis is not integer.
ValueError – If argument axis is out of range of $[- x . n d i m, x . n d i m)$ .

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> input_x = np.arange(9).astype("float32")
>>> output = np.array_split(input_x, 4)
>>> print(output)
(Tensor(shape=[3], dtype=Float32,
    value= [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00]),
Tensor(shape=[2], dtype=Float32,
    value= [ 3.00000000e+00,  4.00000000e+00]),
Tensor(shape=[2], dtype=Float32,
    value= [ 5.00000000e+00,  6.00000000e+00]),
Tensor(shape=[2], dtype=Float32,
    value= [ 7.00000000e+00,  8.00000000e+00]))