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分类目录：《深入浅出PaddlePaddle函数》总目录
相关文章：
· 深入浅出TensorFlow2函数——tf.range
· 深入浅出Pytorch函数——torch.arange
· 深入浅出PaddlePaddle函数——paddle.arange

语法

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

当dtype表示浮点类型时，为了避免浮点计算误差，建议给end加上一个极小值epsilon，使边界可以更加明确。

返回值

返回以步长step均匀分隔给定数值区间[start , end)的一维张量，数据类型为dtype。

参数

• start： [float/int/Tensor] 区间起点（且区间包括此值）。当start类型是Tensor时，是形状为[1]且数据类型为int32、int64、float32、float64的Tensor。如果仅指定start，而end为None，则区间为$[0,start)$。默认值为$0$。
• end：[可选，float/int/Tensor] 区间终点（且通常区间不包括此值）。当end类型是Tensor时，是形状为[1]且数据类型为int32、int64、float32、float64的Tensor。默认值为None。
• step：[可选，float/int/Tensor] 均匀分割的步长。当step类型是Tensor时，是形状为[1]且数据类型为int32、int64、float32、float64的Tensor。默认值为$1$。
• dtype： [可选，str/np.dtype] 输出Tensor的数据类型，支持int32、int64、float32、float64。当该参数值为None时，输出Tensor的数据类型为int64。默认值为None。
• name： [可选，str] 具体用法参见Name，一般无需设置，默认值为None。

实例

import paddleout1 = paddle.arange(5)              # [0, 1, 2, 3, 4]
out2 = paddle.arange(3, 9, 2.0)      # [3, 5, 7]# use 4.999 instead of 5.0 to avoid floating point rounding errors
out3 = paddle.arange(4.999, dtype='float32')
# [0., 1., 2., 3., 4.]start_var = paddle.to_tensor([3])
out4 = paddle.arange(start_var, 7)
# [3, 4, 5, 6]

函数实现

def arange(start=0, end=None, step=1, dtype=None, name=None):"""Returns a 1-D Tensor with spaced values within a given interval.Values are generated into the half-open interval [``start``, ``end``) withthe ``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 thisvalue. If ``end`` is None, the half-open interval is [0, ``start``).If ``start`` is a Tensor, it is a 1-D Tensor with shape [1], withdata type int32, int64, float32, float64. Default is 0.end(float|int|Tensor, optional): End of interval. The interval does notinclude this value. If ``end`` is a Tensor, it is a 1-D Tensor withshape [1], with data type 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 istance between two adjacent values, out[i+1] - out[i].If ``step`` is a Tensor, it is a 1-D Tensor with shape [1], withdata type int32, int64, float32, float64. Default is 1.dtype(str|np.dtype, optional): The data type of theoutput tensor. Supported data types: int32, int64, float32, float64.If ``dytpe`` is None, the data type is float32. Default is None.name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.Returns:Tensor: A 1-D Tensor with values from the interval [``start``, ``end``)taken with common difference ``step`` beginning from ``start``. Itsdata type is set by ``dtype``.Examples:.. code-block:: pythonimport paddleout1 = paddle.arange(5)# [0, 1, 2, 3, 4]out2 = paddle.arange(3, 9, 2.0)# [3, 5, 7]# use 4.999 instead of 5.0 to avoid floating point rounding errorsout3 = paddle.arange(4.999, dtype='float32')# [0., 1., 2., 3., 4.]start_var = paddle.to_tensor([3])out4 = paddle.arange(start_var, 7)# [3, 4, 5, 6]"""if dtype is None:dtype = 'int64'if end is None:end = startstart = 0out_shape = Noneif (not isinstance(start, Variable)and not isinstance(end, Variable)and not isinstance(step, Variable)):out_shape = [int(math.ceil((end - start) / step))]if not isinstance(dtype, core.VarDesc.VarType):dtype = convert_np_dtype_to_dtype_(dtype)if not isinstance(start, Variable):with device_guard("cpu"):start = fill_constant([1], dtype, start, force_cpu=True)elif start.dtype != dtype:start = paddle.cast(start, dtype)if not isinstance(end, Variable):with device_guard("cpu"):end = fill_constant([1], dtype, end, force_cpu=True)elif end.dtype != dtype:end = paddle.cast(end, dtype)if not isinstance(step, Variable):with device_guard("cpu"):step = fill_constant([1], dtype, step, force_cpu=True)elif step.dtype != dtype:step = paddle.cast(step, dtype)if in_dygraph_mode():return _C_ops.arange(start, end, step, dtype, _current_expected_place())if _in_legacy_dygraph():out = _legacy_C_ops.range(start, end, step)out.stop_gradient = Truereturn outcheck_dtype(dtype, 'dtype', ['float32', 'float64', 'int32', 'int64'], 'range/arange')helper = LayerHelper('range', **locals())out = helper.create_variable_for_type_inference(dtype, shape=out_shape)helper.append_op(type='range',inputs={'Start': start, 'End': end, 'Step': step},outputs={'Out': out},)out.stop_gradient = Trueif out_shape is not None:out.desc.set_shape(out_shape)return out