mindspore.Tensor.chunk

Tensor.chunk(chunks, dim=0) → Tuple of Tensors

Cut the self Tensor into chunks sub-tensors along the specified dimension.

Note

The number of sub-tensors returned by this function may be less than the number of sub-tensors specified by chunks.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

• chunks (int) – Number of sub-tensors to cut.

• dim (int, optional) – Specify the dimensions that you want to split. Default: 0 .

Returns

A tuple of sub-tensors.

Raises

• TypeError – The sum of chunks is not int.

• TypeError – If argument dim is not int.

• ValueError – If argument dim is out of range of $[-self.ndim,self.ndim)$ .

• ValueError – If argument chunks is not positive number.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> input_x = Tensor(np.arange(9).astype("float32"))
>>> output = input_x.chunk(3, dim=0)
>>> print(output)
(Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00]),
    Tensor(shape=[3], dtype=Float32, value= [ 3.00000000e+00,  4.00000000e+00,  5.00000000e+00]),
    Tensor(shape=[3], dtype=Float32, value= [ 6.00000000e+00,  7.00000000e+00,  8.00000000e+00]))

Tensor.chunk(chunks, axis=0) → Tuple of Tensors

Cut the self Tensor into chunks sub-tensors along the specified axis.

Note

This function may return less than the specified number of chunks!

Parameters

• chunks (int) – Number of sub-tensors to cut.

• axis (int, optional) – Specify the dimensions that you want to split. Default: 0 .

Returns

A tuple of sub-tensors.

Raises

• TypeError – The sum of chunks is not int.

• TypeError – If argument axis is not int.

• ValueError – If argument axis is out of range of $[-self.ndim,self.ndim)$ .

• ValueError – If argument chunks is not positive number.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> input_x = Tensor(np.arange(9).astype("float32"))
>>> output = input_x.chunk(3, axis=0)
>>> print(output)
(Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00,  1.00000000e+00,  2.00000000e+00]),
    Tensor(shape=[3], dtype=Float32, value= [ 3.00000000e+00,  4.00000000e+00,  5.00000000e+00]),
    Tensor(shape=[3], dtype=Float32, value= [ 6.00000000e+00,  7.00000000e+00,  8.00000000e+00]))