MVN#
Versioned name: MVN-6
Category: Normalization
Short description: Calculates mean-variance normalization of the input tensor.
Detailed description
MVN subtracts mean value from the input blob:
If normalize_variance is set to true
, the output blob is divided by variance. When normalizing the value, the number eps
is added to the variance to avoid division by zero. According to the eps_mode
flag’s value, eps
is added inside or outside the sqrt:
If
eps_mode
isinside_sqrt
:\[o_{i}=\frac{o_{i}}{\sqrt {\sum {o_{k}^2}+\epsilon}}\]If
eps_mode
isoutside_sqrt
:\[o_{i}=\frac{o_{i}}{\sqrt {\sum {o_{k}^2}}+\epsilon}\]
Attributes
normalize_variance
Description: normalize_variance is a flag that specifies whether to perform variance normalization.
Range of values:
false
- do not normalize variancetrue
- normalize variance
Type:
boolean
Required: yes
eps
Description: eps is the number to be added to the variance to avoid division by zero when normalizing the value.
Range of values: a positive floating-point number
Type:
float
Required: yes
eps_mode
Description: Choose where to add epsilon.
Range of values:
inside_sqrt
- add epsilon inside sqrtoutside_sqrt
- add epsilon outside of sqrt
Type:
string
Required: yes
Inputs
1:
data
- Input tensor to be normalized of type T and arbitrary shape. Required.2:
axes
- 1D tensor which specifies indices of dimensions indata
that define normalization slices. Allowed range of axes is[-r; r-1]
wherer = rank(data)
, the order can be not sorted. Negative value means counting dimensions from the back. Type T_IND. Required.
Outputs
1: Output tensor of the same shape and type as the
data
input tensor.
Types
T: any floating point type.
T_IND:
int64
orint32
.
Example
<layer ... type="MVN">
<data eps="1e-9" eps_mode="inside_sqrt" normalize_variance="true"/>
<input>
<port id="0">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
<port id="1">
<dim>3</dim> <!-- value of [0,2,3] means independent normalization per channels -->
</port>
</input>
<output>
<port id="2">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
</output>
</layer>