mindspore.Tensor.sum
- Tensor.sum(axis=None, dtype=None, keepdims=False, initial=None)[source]
Return sum of tensor elements over a given axis.
Note
Numpy arguments out, where, casting, order, subok, signature, and extobj are not supported. The axis with tensor type is only used for compatibility with older versions and is not recommended.
- Parameters
axis (Union[None, int, tuple(int), list(int), Tensor]) – Axis or axes along which a sum is performed. Default:
None
. IfNone
, sum all the elements of the input tensor. If the axis is negative, it counts from the last to the first axis. If the axis is a tuple or list of ints, a sum is performed on all the axes specified in the tuple or list instead of a single axis or all the axes as before.dtype (
mindspore.dtype
, optional) – defaults toNone
. Overrides the dtype of the output Tensor.keepdims (bool) – If this is set to
True
, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array. If the default value is passed, then keepdims will not be passed through to the sum method of sub-classes of ndarray, however any non-default value will be. If the sub-class method does not implement keepdims any exceptions will be raised. Default:False
.initial (scalar) – Starting value for the sum. Default:
None
.
- Returns
Tensor. A tensor with the same shape as input, with the specified axis removed. If the input tensor is a 0-d array, or if the axis is
None
, a scalar is returned.- Raises
TypeError – If input is not array_like, or axis is not int, tuple of ints, list of ints or Tensor, or keepdims is not integer, or initial is not scalar.
ValueError – If any axis is out of range or duplicate axes exist.
See also
mindspore.Tensor.cumsum()
: Return the cumulative sum of the elements along a given axis.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import numpy as np >>> from mindspore import Tensor >>> input_x = Tensor(np.array([-1, 0, 1]).astype(np.float32)) >>> print(input_x.sum()) 0.0 >>> input_x = Tensor(np.arange(10).reshape(2, 5).astype(np.float32)) >>> print(input_x.sum(axis=1)) [10. 35.]