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mindspore.ops.sum

mindspore.ops.sum(input, dim=None, keepdim=False, *, dtype=None)[source]

Calculate sum of Tensor elements over a given dim.

Parameters

input (Tensor) – The input tensor.
dim (Union[None, int, tuple(int), list(int)]) – 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 $[- i n p u t . n d i m, i n p u t . n d i m)$ . Default: None.
keepdim (bool) – Whether the output tensor has dim retained or not. If True, keep these reduced dimensions and the length is 1. If False, 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) or None.
ValueError – If dim is not in the range $[- i n p u t . n d i m, i n p u t . n d i m)$ .
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.]]]