forked from mindspore-Ecosystem/mindspore
67 lines
2.7 KiB
ReStructuredText
67 lines
2.7 KiB
ReStructuredText
mindspore.nn.Moments
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====================
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.. py:class:: mindspore.nn.Moments(axis=None, keep_dims=None)
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沿指定轴 `axis` 计算输入 `x` 的均值和方差。
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**参数：**
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- **axis** (Union[int, tuple(int), None]) - 沿指定轴 `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。
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- **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，则会降低 `axis` 的维度。默认值：None。
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**输入：**
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- **x** (Tensor) - 用于计算均值和方差的任意维度的Tensor。数据类型仅支持float16和float32。
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**输出：**
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- **mean** (Tensor) - `x` 在 `axis` 上的均值，数据类型与输入 `x` 相同。
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- **variance** (Tensor) - `x` 在 `axis` 上的方差，数据类型与输入 `x` 相同。
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**异常：**
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- **TypeError** - `axis` 不是int，tuple或None。
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- **TypeError** - `keep_dims` 既不是bool也不是None。
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- **TypeError** - `x` 的数据类型既不是float16也不是float32。
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**支持平台：**
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``Ascend`` ``GPU`` ``CPU``
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**样例：**
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>>> # case1: axis = 0, keep_dims=True
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>>> x = Tensor(np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]), mindspore.float32)
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>>> net = nn.Moments(axis=0, keep_dims=True)
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>>> output = net(x)
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>>> print(output)
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(Tensor(shape=[1, 2, 2], dtype=Float32, value=
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[[[ 3.00000000e+00, 4.00000000e+00],
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[ 5.00000000e+00, 6.00000000e+00]]]), Tensor(shape=[1, 2, 2], dtype=Float32, value=
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[[[ 4.00000000e+00, 4.00000000e+00],
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[ 4.00000000e+00, 4.00000000e+00]]]))
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>>> # case2: axis = 1, keep_dims=True
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>>> net = nn.Moments(axis=1, keep_dims=True)
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>>> output = net(x)
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>>> print(output)
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(Tensor(shape=[2, 1, 2], dtype=Float32, value=
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[[[ 2.00000000e+00, 3.00000000e+00]],
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[[ 6.00000000e+00, 7.00000000e+00]]]), Tensor(shape=[2, 1, 2], dtype=Float32, value=
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[[[ 1.00000000e+00, 1.00000000e+00]],
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[[ 1.00000000e+00, 1.00000000e+00]]]))
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>>> # case3: axis = 2, keep_dims=None(default)
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>>> net = nn.Moments(axis=2)
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>>> output = net(x)
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>>> print(output)
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(Tensor(shape=[2, 2], dtype=Float32, value=
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[[ 1.50000000e+00, 3.50000000e+00],
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[ 5.50000000e+00, 7.50000000e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
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[[ 2.50000000e-01, 2.50000000e-01],
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[ 2.50000000e-01, 2.50000000e-01]]))
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>>> # case4: axis = None(default), keep_dims=None(default)
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>>> net = nn.Moments()
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>>> output = net(x)
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>>> print(output)
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(Tensor(shape=[], dtype=Float32, value= 4.5), Tensor(shape=[], dtype=Float32, value= 5.25))
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