# sums¶

`paddle.fluid.layers.``sums`(input, out=None)[源代码]

• 示例：3个Tensor求和
```输入：
x0.shape = [2, 3]
x0.data = [[1., 2., 3.],
[4., 5., 6.]]
x1.shape = [2, 3]
x1.data = [[10., 20., 30.],
[40., 50., 60.]]
x2.shape = [2, 3]
x2.data = [[100., 200., 300.],
[400., 500., 600.]]

out.shape = [2, 3]
out.data = [[111., 222., 333.],
[444., 555., 666.]]
```

## 参数¶

• input (list) - 多个维度相同的Tensor组成的元组。支持的数据类型：float32，float64，int32，int64。
• out (Variable，可选) - 指定求和的结果Tensor，可以是程序中已经创建的任何Variable。默认值为None，此时将创建新的Variable来保存输出结果。

Variable

## 代码示例¶

```import paddle.fluid as fluid

x0 = fluid.layers.fill_constant(shape=[16, 32], dtype='int64', value=1)
x1 = fluid.layers.fill_constant(shape=[16, 32], dtype='int64', value=2)
x2 = fluid.layers.fill_constant(shape=[16, 32], dtype='int64', value=3)
x3 = fluid.layers.fill_constant(shape=[16, 32], dtype='int64', value=0)

# 多个Tensor求和，结果保存在一个新建的Variable sum0，即sum0=x0+x1+x2，值为[[6, ..., 6], ..., [6, ..., 6]]
sum0 = fluid.layers.sums(input=[x0, x1, x2])

# 多个Tensor求和，sum1和x3是同一个Variable，相当于x3=x0+x1+x2，值为[[6, ..., 6], ..., [6, ..., 6]]
sum1 = fluid.layers.sums(input=[x0, x1, x2], out=x3)
```