forked from mindspore-Ecosystem/mindspore
70 lines
3.1 KiB
ReStructuredText
70 lines
3.1 KiB
ReStructuredText
|
mindspore.nn.Moments
|
|||
|
|
====================
|
|||
|
|
|
|||
|
|
.. py:class:: mindspore.nn.Moments(axis=None, keep_dims=None)
|
|||
|
|
|
|||
|
|
计算 `x` 的均值和方差。
|
|||
|
|
|
|||
|
|
均值和方差是通过聚合 `x` 在 `axis` 上的值来计算的。特别的，如果 `x` 是1-D的Tensor， `axis` 等于0，这相当于计算向量的均值和方差。
|
|||
|
|
|
|||
|
|
**参数：**
|
|||
|
|
|
|||
|
|
- **axis** (Union[int, tuple(int), None]) - 沿指定 `axis` 计算均值和方差，值为None时代表计算 `x` 所有值的均值和方差。默认值：None。
|
|||
|
|
- **keep_dims** (Union[bool, None]) - 如果为True，计算结果会保留 `axis` 的维度，即均值和方差的维度与输入的相同。如果为False或None，则会消减 `axis` 的维度。默认值：None。
|
|||
|
|
|
|||
|
|
**输入：**
|
|||
|
|
|
|||
|
|
- **x** (Tensor) - 用于计算均值和方差的Tensor。数据类型仅支持float16和float32。shape为 :math:`(N,*)`， 其中 :math:`*` 表示任意的附加维度数。
|
|||
|
|
|
|||
|
|
**输出：**
|
|||
|
|
|
|||
|
|
- **mean** (Tensor) - `x` 在 `axis` 上的均值，数据类型与输入 `x` 相同。
|
|||
|
|
- **variance** (Tensor) - `x` 在 `axis` 上的方差，数据类型与输入 `x` 相同。
|
|||
|
|
|
|||
|
|
**异常：**
|
|||
|
|
|
|||
|
|
- **TypeError** - `axis` 不是int，tuple或None。
|
|||
|
|
- **TypeError** - `keep_dims` 既不是bool也不是None。
|
|||
|
|
- **TypeError** - `x` 的数据类型既不是float16也不是float32。
|
|||
|
|
|
|||
|
|
**支持平台：**
|
|||
|
|
|
|||
|
|
``Ascend`` ``GPU`` ``CPU``
|
|||
|
|
|
|||
|
|
**样例：**
|
|||
|
|
|
|||
|
|
>>> x = Tensor(np.array([[[[1, 2, 3, 4], [3, 4, 5, 6]]]]), mindspore.float32)
|
|||
|
|
>>> net = nn.Moments(axis=0, keep_dims=True)
|
|||
|
|
>>> output = net(x)
|
|||
|
|
>>> print(output)
|
|||
|
|
(Tensor(shape=[1, 1, 2, 4], dtype=Float32, value=
|
|||
|
|
[[[[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, 4.00000000e+00],
|
|||
|
|
[ 3.00000000e+00, 4.00000000e+00, 5.00000000e+00, 6.00000000e+00]]]]),
|
|||
|
|
Tensor(shape=[1, 1, 2, 4], dtype=Float32, value=
|
|||
|
|
[[[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
|
|||
|
|
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]]]))
|
|||
|
|
>>> net = nn.Moments(axis=1, keep_dims=True)
|
|||
|
|
>>> output = net(x)
|
|||
|
|
>>> print(output)
|
|||
|
|
(Tensor(shape=[1, 1, 2, 4], dtype=Float32, value=
|
|||
|
|
[[[[ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, 4.00000000e+00],
|
|||
|
|
[ 3.00000000e+00, 4.00000000e+00, 5.00000000e+00, 6.00000000e+00]]]]),
|
|||
|
|
Tensor(shape=[1, 1, 2, 4], dtype=Float32, value=
|
|||
|
|
[[[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
|
|||
|
|
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]]]))
|
|||
|
|
>>> net = nn.Moments(axis=2, keep_dims=True)
|
|||
|
|
>>> output = net(x)
|
|||
|
|
>>> print(output)
|
|||
|
|
(Tensor(shape=[1, 1, 1, 4], dtype=Float32, value=
|
|||
|
|
[[[[ 2.00000000e+00, 3.00000000e+00, 4.00000000e+00, 5.00000000e+00]]]]),
|
|||
|
|
Tensor(shape=[1, 1, 1, 4], dtype=Float32, value=
|
|||
|
|
[[[[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]]]))
|
|||
|
|
>>> net = nn.Moments(axis=3, keep_dims=True)
|
|||
|
|
>>> output = net(x)
|
|||
|
|
>>> print(output)
|
|||
|
|
(Tensor(shape=[1, 1, 2, 1], dtype=Float32, value=
|
|||
|
|
[[[[ 2.50000000e+00],
|
|||
|
|
[ 4.50000000e+00]]]]), Tensor(shape=[1, 1, 2, 1], dtype=Float32, value=
|
|||
|
|
[[[[ 1.25000000e+00],
|
|||
|
|
[ 1.25000000e+00]]]]))
|
|||
|
|
|