mindspore.ops.sum
- mindspore.ops.sum(input, dim=None, keepdim=False, *, dtype=None)[source]
Calculate sum of Tensor elements over a given dim.
Note
The dim with tensor type is only used for compatibility with older versions and is not recommended.
- Parameters
input (Tensor) – The input tensor.
dim (Union[None, int, tuple(int), list(int), Tensor]) – Dimensions along which a sum is performed. If
None
, sum all the elements of the input tensor. If the dim is a tuple or list of ints, a sum is performed on all the dimensions specified in the tuple. Must be in the range \([-input.ndim, input.ndim)\) . Default:None
.keepdim (bool) – Whether the output tensor has dim retained or not. If
True
, keep these reduced dimensions and the length is 1. IfFalse
, don't keep these dimensions. Default:False
.
- Keyword Arguments
dtype (
mindspore.dtype
, optional) – The desired data type of returned Tensor. Default:None
.- Returns
A Tensor, sum of elements over a given dim in input.
- Raises
TypeError – If input is not a Tensor.
TypeError – If dim is not an int, tulpe(int), list(int), Tensor or None.
ValueError – If dim is not in the range \([-input.ndim, input.ndim)\) .
TypeError – If keepdim is not a bool.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor, ops >>> from mindspore import dtype as mstype >>> x = Tensor(np.array([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]], ... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]], ... [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]]), mstype.float32) >>> out = ops.sum(x) >>> print(out) 270.0 >>> out = ops.sum(x, dim=2) >>> print(out) [[ 6. 12. 18.] [24. 30. 36.] [42. 48. 54.]] >>> out = ops.sum(x, dim=2, keepdim=True) >>> print(out) [[[ 6.] [12.] [18.]] [[24.] [30.] [36.]] [[42.] [48.] [54.]]]