mindspore.ops.ReduceSum

class mindspore.ops.ReduceSum(*args, **kwargs)[source]

Reduces a dimension of a tensor by summing all elements in the dimension.

The dtype of the tensor to be reduced is number.

Parameters

keep_dims (bool) – If true, keep these reduced dimensions and the length is 1. If false, don’t keep these dimensions. Default: False.

Inputs:

  • x (Tensor[Number]) - The input tensor.

  • axis (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions. Only constant value is allowed. Must be in the range [-rank(x), rank(x)).

Outputs:

Tensor, has the same dtype as the x.

  • If axis is (), and keep_dims is False, the output is a 0-D tensor representing the sum of all elements in the input tensor.

  • If axis is int, set as 2, and keep_dims is False, the shape of output is \((x_1, x_3, ..., x_R)\).

  • If axis is tuple(int), set as (2, 3), and keep_dims is False, the shape of output is \((x_1, x_4, ..., x_R)\).

Raises
Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = ops.ReduceSum(keep_dims=True)
>>> output = op(x, 1)
>>> output.shape
(3, 1, 5, 6)
>>> 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]]]), mindspore.float32)
>>> op = ops.ReduceSum(keep_dims=True)
>>> output = op(x, 0)
>>> print(output)
[[[12. 12. 12. 12. 12. 12.]
  [15. 15. 15. 15. 15. 15.]
  [18. 18. 18. 18. 18. 18.]]]
>>> output = op(x, 1)
>>> print(output)
[[[6. 6. 6. 6. 6. 6.]]
 [[15. 15. 15. 15. 15. 15.]]
 [[24. 24. 24. 24. 24. 24.]]]
>>> output = op(x, 2)
>>> print(output)
[[[ 6.]
  [12.]
  [18.]]
 [[24.]
  [30.]
  [36.]]
 [[42.]
  [48.]
  [54.]]]