mindspore.numpy.hsplit

mindspore.numpy.hsplit(x, indices_or_sections)[source]

Splits a tensor into multiple sub-tensors horizontally (column-wise). It is equivalent to split with $a x i s = 1$ (default), the array is always split along the second axis regardless of the array dimension.

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 $N$ equal 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.

Returns

A list of sub-tensors.

Raises

TypeError – If argument indices_or_sections is not integer.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore.numpy as np
>>> input_x = np.arange(6).reshape((2, 3)).astype('float32')
>>> output = np.hsplit(input_x, 3)
>>> print(output)
(Tensor(shape=[2, 1], dtype=Float32,
value=[[ 0.00000000e+00],
       [ 3.00000000e+00]]),
Tensor(shape=[2, 1], dtype=Float32,
value=[[ 1.00000000e+00],
       [ 4.00000000e+00]]),
Tensor(shape=[2, 1], dtype=Float32,
value=[[ 2.00000000e+00],
       [ 5.00000000e+00]]))