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,则应为形状为[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 。默认值为 。- 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 。 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 paddle
out1 = 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``) 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 1-D Tensor with shape [1], with
data type 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 1-D Tensor with
shape [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], with
data type 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 ``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``. Its
data type is set by ``dtype``.
Examples:
.. code-block:: python
import paddle
out1 = 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]
"""
if dtype is None:
dtype = 'int64'
if end is None:
end = start
start = 0
out_shape = None
if (
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 = True
return out
check_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 = True
if out_shape is not None:
out.desc.set_shape(out_shape)
return out
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