mindspore.mint.sum

mindspore.mint.sum(input, *, dtype=None) → Tensor[source]

Calculate sum of all elements in Tensor.

Parameters: input (Tensor) – The input tensor.
Keyword Arguments: dtype (mindspore.dtype, optional) – The desired data type of returned Tensor. Default: None .
Returns: A Tensor, sum of all elements in input.
Raises: TypeError – If input is not a Tensor.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> 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 = mint.sum(x)
>>> print(out)
270.0

mindspore.mint.sum(input, dim, keepdim=False, *, dtype=None) → Tensor[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[int, tuple(int), list(int), Tensor]) – Dimensions along which a sum is performed. 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 \([-input.ndim, input.ndim)\) .
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 Tensor.
ValueError – If dim is not in the range \([-input.ndim, input.ndim)\) .
TypeError – If keepdim is not a bool.

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> 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 = mint.sum(x)
>>> print(out)
270.0
>>> out = mint.sum(x, dim=2)
>>> print(out)
[[ 6. 12. 18.]
 [24. 30. 36.]
 [42. 48. 54.]]
>>> out = mint.sum(x, dim=2, keepdim=True)
>>> print(out)
[[[ 6.]
  [12.]
  [18.]]
 [[24.]
  [30.]
  [36.]]
 [[42.]
  [48.]
  [54.]]]