mindspore.ops.tensor_split

mindspore.ops.tensor_split(input, indices_or_sections, axis=0)[source]

Splits a tensor into multiple sub-tensors along the given axis.

Parameters

  • input (Tensor) – A Tensor to be divided.

  • indices_or_sections (Union[int, tuple(int), list(int)]) –

    • If indices_or_sections is an integer n, input tensor will be split into n sections.

      • If \(input.shape(axis)\) can be divisible by n, sub-sections will have equal size \(input.shape(axis) / n\) .

      • If \(input.shape(axis)\) is not divisible by n, the first \(input.shape(axis) % n\) sections will have size \(input.shape(axis) // n + 1\) , and the rest will have size \(input.shape(axis) // n\) .

    • If indices_or_sections is of type tuple(int) or list(int), the input tensor will be split at the indices in the list or tuple. For example, given parameters \(indices\_or\_sections=[1, 4]\) and \(axis=0\) , the input tensor will be split into sections \(input[:1]\) , \(input[1:4]\) , and \(input[4:]\) .

  • axis (int) – The axis along which to split. Default: 0.

Returns

A tuple of sub-tensors.

Raises

  • TypeError – If argument input is not Tensor.

  • TypeError – If argument axis is not int.

  • ValueError – If argument axis is out of range of \([-input.ndim, input.ndim)\) .

  • TypeError – If each element in ‘indices_or_sections’ is not integer.

  • TypeError – If argument indices_or_sections is not int, tuple(int) or list(int).

Supported Platforms:

Ascend GPU CPU

Examples

>>> input_x = np.arange(9).astype("float32")
>>> output = ops.tensor_split(Tensor(input_x), 3)
>>> 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]))