arange

paddle. arange ( start=0, end=None, step=1, dtype=None, name=None ) [source]

Returns a 1-D Tensor with spaced values within a given interval.

Values are generated into the half-open interval [start, end) with the step. (the interval including start but excluding end).

If dtype is float32 or float64, we advise adding a small epsilon to end to avoid floating point rounding errors when comparing against end.

Parameters

  • start (float|int|Tensor) – Start of interval. The interval includes this value. If end is None, the half-open interval is [0, start). If start is a Tensor, it is a 0-D Tensor which represents a scalar and data type is int32, int64, float32, float64. Default is 0.

  • end (float|int|Tensor, optional) – End of interval. The interval does not include this value. If end is a Tensor, it is a 0-D Tensor which represents a scalar and data type is int32, int64, float32, float64. If end is None, the half-open interval is [0, start). Default is None.

  • step (float|int|Tensor, optional) – Spacing between values. For any out, it is the instance between two adjacent values, out[i+1] - out[i]. If step is a Tensor, it is a 0-D Tensor which represents a scalar and data type is int32, int64, float32, float64. . Default is 1.

  • dtype (str|np.dtype, optional) – The data type of the output tensor. Supported data types: int32, int64, float32, float64. If dtype is None, the data type is float32. Default is None.

  • name (str, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Returns

A 1-D Tensor with values from the interval [start, end) taken with common difference step beginning from start. Its data type is set by dtype.

Return type

Tensor

Examples

>>> import paddle

>>> out1 = paddle.arange(5)
>>> print(out1.numpy())
[0 1 2 3 4]

>>> out2 = paddle.arange(3, 9, 2.0)
>>> print(out2.numpy())
[3. 5. 7.]

>>> # use 4.999 instead of 5.0 to avoid floating point rounding errors
>>> out3 = paddle.arange(4.999, dtype='float32')
>>> print(out3.numpy())
[0. 1. 2. 3. 4.]

>>> start_var = paddle.to_tensor(3)
>>> out4 = paddle.arange(start_var, 7)
>>> print(out4.numpy())
[3 4 5 6]

Used in the guide/tutorials