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mindspore.ops.arange

mindspore.ops.arange(start=0, end=None, step=1, *, dtype=None)

Creates a sequence of numbers that begins at start and extends by increments of step up to but not including end.

Parameters

• start (Union[float, int, Tensor], optional) – The start of the interval. If Tensor, the shape must be $()$ . Default: 0 .

• end (Union[float, int, Tensor], optional) – The end of the interval, exclusive. If Tensor, the shape must be $()$. Default: None . If None , it defaults to the value of start, and 0 is used as the starting value.

• step (Union[float, int, Tensor], optional) – Number that increments start. If Tensor, the shape must be $()$. Default: 1 .

Keyword Arguments

dtype (mindspore.dtype, optional) –

The required data type of returned Tensor. Default: None . When dtype is not specified or None:

If start, end, and step are all integers, the dtype of output is int64,

If start, end, and step contain at least one floating-point number, the dtype of output is float32.

Returns

A 1-D Tensor, with the same type as the inputs.

Raises

• TypeError – If start, end or step is not an int or a float or a TensorScalar(Special Tensor with shape ()) in valid dtypes.

• ValueError – If step = 0.

• ValueError – If start >= end when step > 0.

• ValueError – If start <= end when step < 0.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> from mindspore import Tensor, ops
>>> output = ops.arange(1, 6)
>>> print(output)
[1 2 3 4 5]
>>> print(output.dtype)
Int64
>>> output = ops.arange(0, 3, 1.2)
>>> print(output)
[0.  1.2 2.4]
>>> print(output.dtype)
Float32
>>> output = ops.arange(7, 1, -2)
>>> print(output)
[7 5 3]
>>> print(output.dtype)
Int64
>>> output = ops.arange(ms.Tensor(12.0, dtype=ms.float64), 2, ms.Tensor(-1.0, dtype=ms.float32))
>>> print(output)
[12. 11. 10.  9.  8.  7.  6.  5.  4.  3.]
>>> print(output.dtype)
Float32