mindspore.nn.Moments

class mindspore.nn.Moments(axis=None, keep_dims=None)[source]

Calculates the mean and variance of x.

The mean and variance are calculated by aggregating the contents of input_x across axes. If input_x is 1-D and axes = [0] this is just the mean and variance of a vector.

Parameters

axis (Union[int, tuple(int)]) – Calculates the mean and variance along the specified axis. Default: None.
keep_dims (bool) – If true, The dimension of mean and variance are identical with input’s. If false, don’t keep these dimensions. Default: None.

Inputs:

x (Tensor) - The tensor to be calculated. Only float16 and float32 are supported. \((N,*)\) where \(*\) means,any number of additional dimensions.

Outputs:

mean (Tensor) - The mean of x, with the same data type as input x.
variance (Tensor) - The variance of x, with the same data type as input x.

Raises

TypeError – If axis is not one of int, tuple, None.
TypeError – If keep_dims is neither bool nor None.
TypeError – If dtype of x is neither float16 nor float32.

Supported Platforms:: Ascend GPU CPU

Examples

>>> 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]]]]))