# mindspore.numpy.dsplit¶

mindspore.numpy.dsplit(x, indices_or_sections)[source]

Splits a tensor into multiple sub-tensors along the 3rd axis (depth). It is equivalent to split with $$axis=2$$ (default), the array is always split along the third 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 $$axis=0$$, 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.

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((1, 2, 3)).astype('float32')
>>> output = np.dsplit(input_x, 3)
>>> print(output)
(Tensor(shape=[1, 2, 1], dtype=Float32,
value=[[[ 0.00000000e+00],
[ 3.00000000e+00]]]),
Tensor(shape=[1, 2, 1], dtype=Float32,
value=[[[ 1.00000000e+00],
[ 4.00000000e+00]]]),
Tensor(shape=[1, 2, 1], dtype=Float32,
value=[[[ 2.00000000e+00],
[ 5.00000000e+00]]]))