深入浅出PaddlePaddle函数——paddle.sum

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.reduce_sum
· 深入浅出TensorFlow2函数——tf.math.reduce_sum
· 深入浅出Pytorch函数——torch.sum
· 深入浅出PaddlePaddle函数——paddle.sum

对指定维度上的

Tensor

元素进行求和运算，并输出相应的计算结果。

语法

paddle.sum(x, axis=None, dtype=None, keepdim=False, name=None)

参数

• x：[Tensor] 输入变量为多维Tensor，支持数据类型为float32、float64、int32、int64。
• axis：[可选, int/list/tuple] 求和运算的维度。如果为None，则计算所有元素的和并返回包含单个元素的Tensor变量，否则必须在 [ − rank ( x ) , rank ( x ) ] [-\text{rank}(x), \text{rank}(x)] [−rank(x),rank(x)]范围内。如果 axis [ i ] < 0 \text{axis}[i]<0 axis[i]<0，则维度将变为 rank + axis [ i ] \text{rank} + \text{axis}[i] rank+axis[i]，默认值为None。
• dtype：[可选, str] 输出变量的数据类型。若参数为空，则输出变量的数据类型和输入变量相同，默认值为None。
• keepdim：[bool] 是否在输出Tensor中保留减小的维度。如keepdim=True，否则结果张量的维度将比输入张量小，默认值为False。
• name：[可选, str] 具体用法参见Name，一般无需设置，默认值为None。

返回值

Tensor

，在指定维度上进行求和运算的

Tensor

，数据类型和输入数据类型一致。

实例

import paddle

#xis a Tensor with following elements:
#    [[0.2,0.3,0.5,0.9]
#     [0.1,0.2,0.6,0.7]]#Each example is followed by the corresponding output tensor.
x = paddle.to_tensor([[0.2,0.3,0.5,0.9],[0.1,0.2,0.6,0.7]])
out1 = paddle.sum(x)  # [3.5]
out2 = paddle.sum(x, axis=0)  # [0.3,0.5,1.1,1.6]
out3 = paddle.sum(x, axis=-1)  # [1.9,1.6]
out4 = paddle.sum(x, axis=1, keepdim=True)  # [[1.9],[1.6]]#yis a Tensor with shape [2,2,2] and elements as below:
#      [[[1,2],[3,4]],
#      [[5,6],[7,8]]]#Each example is followed by the corresponding output tensor.
y = paddle.to_tensor([[[1,2],[3,4]],[[5,6],[7,8]]])
out5 = paddle.sum(y, axis=[1,2]) # [10,26]
out6 = paddle.sum(y, axis=[0,1]) # [16,20]#xis a Tensor with following elements:
#    [[True, True, True, True]
#     [False, False, False, False]]#Each example is followed by the corresponding output tensor.
x = paddle.to_tensor([[True, True, True, True],[False, False, False, False]])
out7 = paddle.sum(x)  # [4]
out8 = paddle.sum(x, axis=0)  # [1,1,1,1]
out9 = paddle.sum(x, axis=1)  # [4,0]

函数实现

def sum(x, axis=None, dtype=None, keepdim=False, name=None):"""
    Computes the sum of tensor elements over the given dimension.
    Args:x(Tensor): An N-D Tensor, the data type is bool, float16, float32, float64, int32 or int64.axis(int|list|tuple, optional): The dimensions along which the sum is performed. If
            :attr:`None`, sum all elements of :attr:`x` and return a
            Tensor with a single element, otherwise must be in the
            range :math:`[-rank(x),rank(x))`. If :math:`axis[i]<0`,
            the dimension to reduce is :math:`rank + axis[i]`.dtype(str, optional): The dtype of output Tensor. The default value is None, the dtype
            of output is the same as input Tensor `x`.keepdim(bool, optional): Whether to reserve the reduced dimension in the
            output Tensor. The result Tensor will have one fewer dimension
            than the :attr:`x` unless :attr:`keepdim` is true,default
            value is False.name(str, optional): Name for the operation(optional,default is None). For more information, please refer to :ref:`api_guide_Name`.
    Returns:
        Tensor: Results of summation operation on the specified axis of input Tensor `x`,if `x.dtype='bool'`, `x.dtype='int32'`, it's data type is `'int64'`, 
        otherwise it's data type is the same as `x`.
    Examples:.. code-block:: python
            import paddle
            #xis a Tensor with following elements:
            #    [[0.2,0.3,0.5,0.9]
            #     [0.1,0.2,0.6,0.7]]#Each example is followed by the corresponding output tensor.
            x = paddle.to_tensor([[0.2,0.3,0.5,0.9],[0.1,0.2,0.6,0.7]])
            out1 = paddle.sum(x)  # [3.5]
            out2 = paddle.sum(x, axis=0)  # [0.3,0.5,1.1,1.6]
            out3 = paddle.sum(x, axis=-1)  # [1.9,1.6]
            out4 = paddle.sum(x, axis=1, keepdim=True)  # [[1.9],[1.6]]#yis a Tensor with shape [2,2,2] and elements as below:
            #      [[[1,2],[3,4]],
            #      [[5,6],[7,8]]]#Each example is followed by the corresponding output tensor.
            y = paddle.to_tensor([[[1,2],[3,4]],[[5,6],[7,8]]])
            out5 = paddle.sum(y, axis=[1,2]) # [10,26]
            out6 = paddle.sum(y, axis=[0,1]) # [16,20]#xis a Tensor with following elements:
            #    [[True, True, True, True]
            #     [False, False, False, False]]#Each example is followed by the corresponding output tensor.
            x = paddle.to_tensor([[True, True, True, True],[False, False, False, False]])
            out7 = paddle.sum(x)  # [4]
            out8 = paddle.sum(x, axis=0)  # [1,1,1,1]
            out9 = paddle.sum(x, axis=1)  # [4,0]"""
    ifisinstance(axis, Variable):
        reduce_all_flag = True if axis.shape[0]==len(x.shape)else False
    else:if axis is not None and not isinstance(axis,(list, tuple)):
            axis =[axis]if not axis:
            axis =[]iflen(axis)==0:
            reduce_all_flag = True
        else:iflen(axis)==len(x.shape):
                reduce_all_flag = True
            else:
                reduce_all_flag = False

    dtype_flag = False
    if dtype is not None:
        dtype_flag = True
        dtype =convert_np_dtype_to_dtype_(dtype)ifin_dygraph_mode():return _C_ops.sum(x, axis, dtype, keepdim)if not isinstance(axis, Variable):
        axis = axis if axis != None and axis !=[] and axis !=()else[0]if utils._contain_var(axis):
            axis = utils._convert_to_tensor_list(axis)if_in_legacy_dygraph():if dtype_flag:return _legacy_C_ops.reduce_sum(x,'dim', axis,'keep_dim', keepdim,'reduce_all', reduce_all_flag,'in_dtype',
                                       x.dtype,'out_dtype', dtype)else:return _legacy_C_ops.reduce_sum(x,'dim', axis,'keep_dim', keepdim,'reduce_all', reduce_all_flag)

    attrs ={'dim': axis,'keep_dim': keepdim,'reduce_all': reduce_all_flag
    }if dtype_flag:
        attrs.update({'in_dtype': x.dtype,'out_dtype': dtype
        })check_variable_and_dtype(
        x,'x',['bool','float16','float32','float64','int16','int32','int64','complex64','complex128',
                u'bool', u'float16', u'float32', u'float64',
                u'int32', u'int64', u'complex64', u'complex128'],'sum')check_type(axis,'axis',(int, list, tuple,type(None), Variable),'sum')

    helper =LayerHelper('sum',**locals())if dtype_flag:
        out = helper.create_variable_for_type_inference(
            dtype=dtype)else:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type='reduce_sum',
        inputs={'X': x},
        outputs={'Out': out},
        attrs=attrs)return