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.

Parameters

x (Tensor) – An N-D Tensor, the data type is bool, bfloat16, float16, float32, float64, uint8, int8, int16, int32, int64, complex64, complex128.
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 \(rank + axis[i]\).
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 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])

          