mindspore.ops.ReduceSum

class mindspore.ops.ReduceSum(keep_dims=False, skip_mode=False)[source]

Reduces a dimension of a tensor by summing all elements in the dimension, by default. And also can reduce a dimension of x along the axis. Determine whether the dimensions of the output and input are the same by controlling keep_dims.

Parameters

keep_dims (bool) – If True , keep these reduced dimensions and the length is 1. If False , don’t keep these dimensions. Default: False .
skip_mode (bool) – If True and axis is empty tuple or empty list, the ReduceSum operation isn’t performed, skip it. If True and axis is other values, the ReduceSum calculation is performed normally. If False , do reduce. Default: False .

Inputs:

x (Tensor[Number]) - The input tensor.
axis (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: () , reduce all dimensions when skip_mode is False . 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 (), keep_dims is False , and skip_mode is False , the output is a 0-D tensor representing the sum of all elements in the input tensor.
If axis is (), and skip_mode is True , the ReduceSum operation is not performed, output tensor is equal to 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) or list(int), set as (2, 3), and keep_dims is False , the shape of output is \((x_1, x_4, ..., x_R)\).

Raises

TypeError – If keep_dims is not a bool.
TypeError – If skip_mode is not a bool.
TypeError – If x is not a Tensor.
ValueError – If axis is None.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> 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)
>>> # case 1: Reduces a dimension by summing all elements in the dimension.
>>> 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)
>>> output = op(x)
>>> print(output)
[[[270.]]]
>>> print(output.shape)
(1, 1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = op(x, 0)
>>> print(output)
[[[12. 12. 12. 12. 12. 12.]
  [15. 15. 15. 15. 15. 15.]
  [18. 18. 18. 18. 18. 18.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = op(x, 1)
>>> print(output)
[[[ 6.  6.  6.  6.  6.  6.]]
 [[15. 15. 15. 15. 15. 15.]]
 [[24. 24. 24. 24. 24. 24.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = op(x, 2)
>>> print(output)
[[[ 6.]
  [12.]
  [18.]]
 [[24.]
  [30.]
  [36.]]
 [[42.]
  [48.]
  [54.]]]