sum¶

paddle. sum ( x, axis=None, dtype=None, keepdim=False, name=None ) [源代码] ¶

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¶

x (Tensor) - 输入变量为多维 Tensor，支持数据类型为 float16、float32、float64、int32、int64。

axis (int|list|tuple，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 \([−rank(x),rank(x)]\) 范围内。如果 \(axis [i] <0\)，则维度将变为 \(rank+axis[i]\)，默认值为 None。

dtype (str，可选) - 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，可选) - 是否在输出 Tensor 中保留减小的维度。如 keepdim 为 true，否则结果 Tensor 的维度将比输入 Tensor 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¶

Tensor，在指定维度上进行求和运算的 Tensor。如果输入的数据类型为 bool 或 int32，则返回的数据类型为 int64 。除此之外返回的数据类型和输入的数据类型一致。

代码示例¶

          >>> import paddle

>>> # x is 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)
>>> out1
Tensor(shape=[], dtype=float32, place=Place(cpu), stop_gradient=True,
3.50000000)
>>> out2 = paddle.sum(x, axis=0)
>>> out2
Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.30000001, 0.50000000, 1.10000002, 1.59999990])
>>> out3 = paddle.sum(x, axis=-1)
>>> out3
Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
[1.89999998, 1.60000002])
>>> out4 = paddle.sum(x, axis=1, keepdim=True)
>>> out4
Tensor(shape=[2, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
[[1.89999998],
 [1.60000002]])

>>> # y is 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])
>>> out5
Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True,
[10, 26])
>>> out6 = paddle.sum(y, axis=[0, 1])
>>> out6
Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True,
[16, 20])

>>> # x is 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)
>>> out7
Tensor(shape=[], dtype=int64, place=Place(cpu), stop_gradient=True,
4)
>>> out8 = paddle.sum(x, axis=0)
>>> out8
Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True,
[1, 1, 1, 1])
>>> out9 = paddle.sum(x, axis=1)
>>> out9
Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True,
[4, 0])

         