mindspore.Tensor.mean

Tensor.mean(axis=(), keep_dims=False)[source]

Reduces a dimension of a tensor by averaging 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

  • axis (Union[None, int, tuple(int), list(int)]) – Dimensions of reduction. When the axis is None or empty tuple, reduce all dimensions. When the axis is int, tuple(int) or list(int), if the dimension of Tensor is dim, the value range is [-dim, dim). Default: ().

  • keep_dims (bool) – Whether to keep the reduced dimensions. Default: False.

Returns

Tensor, has the same data type as input tensor.

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

  • If axis is int, set as 1, and keep_dims is False, the shape of output is \((x_0, x_2, ..., x_R)\).

  • If axis is tuple(int), set as (1, 2), and keep_dims is False, the shape of output is \((x_0, x_3, ..., x_R)\).

Raises

  • TypeError – If axis is not one of the following: int, tuple or list.

  • TypeError – If keep_dims is not a bool.

  • ValueError – If axis is out of range.

Supported Platforms:

Ascend GPU CPU

See also

mindspore.Tensor.std(): Compute the standard deviation along the specified axis.

mindspore.Tensor.var(): Compute the variance along the specified axis.

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> input_x = Tensor(np.array([1, 2, 3], dtype=np.float32))
>>> output = input_x.mean()
>>> print(output)
2.0