reduce_sum¶
该OP是对指定维度上的Tensor元素进行求和运算,并输出相应的计算结果。
参数¶
input (Variable)- 输入变量为多维Tensor或LoDTensor,支持数据类型为float32,float64,int32,int64。
dim (list | int,可选)- 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 \([−rank(input),rank(input)]\) 范围内。如果 \(dim [i] <0\),则维度将变为 \(rank+dim[i]\),默认值为None。
keep_dim (bool)- 是否在输出Tensor中保留减小的维度。如 keep_dim 为true,否则结果张量的维度将比输入张量小,默认值为False。
name (str,可选) - 具体用法请参见 Name,一般无需设置,默认值为 None。
返回¶
在指定dim上进行求和运算的Tensor,数据类型和输入数据类型一致。
返回类型¶
变量(Variable)
代码示例¶
import paddle.fluid as fluid
import paddle
paddle.enable_static()
# x is a Tensor variable 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 = fluid.data(name='x', shape=[2, 4], dtype='float32')
fluid.layers.reduce_sum(x) # [3.5]
fluid.layers.reduce_sum(x, dim=0) # [0.3, 0.5, 1.1, 1.6]
fluid.layers.reduce_sum(x, dim=-1) # [1.9, 1.6]
fluid.layers.reduce_sum(x, dim=1, keep_dim=True) # [[1.9], [1.6]]
# y is a Tensor variable 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 = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
fluid.layers.reduce_sum(y, dim=[1, 2]) # [10, 26]
fluid.layers.reduce_sum(y, dim=[0, 1]) # [16, 20]