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 . If None , 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 to None . 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

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.]