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
onCPU
.The only difference between
np.split
andnp.array_split
is thatnp.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 \(axis=0\), result in three sub-tensors \(x[:2]\), \(x[2:3]\). 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 \([-x.ndim, x.ndim)\).
- 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]))