megengine.functional.mean¶
- mean(inp, axis=None, keepdims=False)[源代码]¶
Calculates the mean of tensor elements over a given axis (or axes).
- 参数
inp (
Tensor
) – input tensor. Should have a numeric data type.axis (
Union
[int
,Sequence
[int
],None
]) – axis or axes along which means must be computed. By default, the mean must be computed over the entire tensor. If a sequence of integers, means must be computed over multiple axes.keepdims (
bool
) – ifTrue
, the reduced axes (dimensions) must be included in the result as singleton dimensions, and, accordingly, the result must be compatible with the input tensor (see 广播机制与规则). Otherwise, ifFalse
, the reduced axes (dimensions) must not be included in the result.
- 返回类型
- 返回
if the mean was computed over the entire tensor, a zero-dimensional tensor containing the mean; otherwise, a non-zero-dimensional tensor containing the means. The returned tensor must have a data type determined by 类型提升规则.
Special Cases
Let
N
equal the number of elements over which to compute the mean.If
N
is 0, the mean isNaN
.If \(x_i\) is
NaN
, the mean isNaN
(i.e.,NaN
values propagate).
实际案例
>>> F.mean(Tensor([1, 2, 3])) Tensor(2.0, device=xpux:0)
>>> import numpy as np >>> F.mean(Tensor([1, np.nan, 3])) Tensor(nan, device=xpux:0)
Along an axis:
>>> F.mean(Tensor([[1, 2, 3], [4, 5, 6]]), axis=0) Tensor([2.5 3.5 4.5], device=xpux:0) >>> F.mean(Tensor([[1, 2, 3], [4, 5, 6]]), axis=1) Tensor([2. 5.], device=xpux:0)