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mindspore.Tensor.chunk

Tensor.chunk(chunks, dim=0) tuple[Tensor][source]

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[Tensor][source]

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]))