MVN

Versioned name: MVN-1

Category: Normalization

Short description: Reference

Detailed description

MVN subtracts mean value from the input blob:

$o_{i} = i_{i} - \frac{\sum{i_{k}}}{C * H * W}$

If normalize_variance is set to 1, the output blob is divided by variance:

$o_{i}=\frac{o_{i}}{\sum \sqrt {o_{k}^2}+\epsilon}$

Attributes

• across_channels
• Description: across_channels is a flag that specifies whether mean values are shared across channels. For example, across_channels equal to false means that mean values are not shared across channels.
• Range of values:
• false - do not share mean values across channels
• true - share mean values across channels
• Type: boolean
• Default value: false
• Required: no
• normalize_variance
• Description: normalize_variance is a flag that specifies whether to perform variance normalization.
• Range of values:
• false – do not normalize variance
• true – normalize variance
• Type: boolean
• Default value: false
• Required: no
• eps
• Description: eps is the number to be added to the variance to avoid division by zero when normalizing the value. For example, epsilon equal to 0.001 means that 0.001 is added to the variance.
• Range of values: a positive floating-point number
• Type: float
• Default value: None
• Required: yes

Inputs

• 1: 4D or 5D input tensor of any floating point type. Required.

Outputs

• 1: normalized tensor of the same type and shape as input tensor.

Example

<layer ... type="MVN">
<data across_channels="true" eps="1e-9" normalize_variance="true"/>
<input>
<port id="0">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
</input>
<output>
<port id="2">
<dim>6</dim>
<dim>12</dim>
<dim>10</dim>
<dim>24</dim>
</port>
</output>
</layer>