mindspore.ops.Split
- class mindspore.ops.Split(axis=0, output_num=1)[source]
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
- 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]]))