sum

paddle. sum ( x: Tensor, axis: int | Sequence[int] | None = None, dtype: DTypeLike | None = None, keepdim: bool = False, name: str | None = None ) → Tensor [source]

Computes the sum of tensor elements over the given dimension.

Note

Parameter order support: When passing positional parameters, it is possible to support swapping the positional order of dtype and axis. For example, sum(x, axis, keepdim, dtype) is equivalent to sum(x, axis, dtype, keepdim). Alias Support: The parameter name input can be used as an alias for x and the parameter name dim can be used as an alias for axis. For example, sum(input=tensor_x, dim=1) is equivalent to sum(x=tensor_x, axis=1).

Parameters

x (Tensor) – An N-D Tensor, the data type is bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128. alias: input.
axis (int|list|tuple|None, optional) – The dimensions along which the sum is performed. If None, sum all elements of x and return a Tensor with a single element, otherwise must be in the range \([-rank(x), rank(x))\). If \(axis[i] < 0\), the dimension to reduce is \(rank + axis[i]\). alias: dim.
dtype (str|paddle.dtype|np.dtype, 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 x unless keepdim is true, default value is False.
name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.

Returns

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.

Return type

Tensor

Examples

           >>> 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])

          