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mindspore.ops.Split

class mindspore.ops.Split(*args, **kwargs)

Splits the input tensor into output_num of tensors along the given axis and output numbers.

The input_x tensor will be split into equally sized sub-tensors. This requires that input_x.shape(axis) is divisible by output_num.

Parameters

• axis (int) – Index of the split position. Default: 0.

• output_num (int) – The number of output tensors. Must be positive int. Default: 1.

Inputs:

• input_x (Tensor) - The shape of tensor is $(x_1,x_2,...,x_R)$.

Outputs:

tuple[Tensor], the shape of each output tensor is the same, which is $(y_1,y_2,...,y_S)$. And the data type is the same with input_x.

Raises

• TypeError – If axis or output_num is not an int.

• ValueError – If axis is out of the range [-len(input_x.shape), len(input_x.shape)), or if the output_num is less than or equal to 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> split = ops.Split(1, 2)
>>> x = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]), mindspore.int32)
>>> print(x)
[[1 1 1 1]
 [2 2 2 2]])
>>> output = split(x)
>>> print(output)
(Tensor(shape=[2, 2], dtype=Int32, value=
[[1, 1],
 [2, 2]]), Tensor(shape=[2, 2], dtype=Int32, value=
[[1, 1],
 [2, 2]]))
>>> split = ops.Split(1, 4)
>>> output = split(x)
>>> print(output)
(Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]), Tensor(shape=[2, 1], dtype=Int32, value=
[[1],
 [2]]))