# LRN {#openvino_docs_ops_normalization_LRN_1}

**Versioned name**: *LRN-1*

**Category**: *Normalization*

**Short description**: Local response normalization.

**Detailed description**:
Local Response Normalization performs a normalization over local input regions.
Each input value is divided by
\f[ (bias + \frac{alpha}{{size}^{len(axes)}} \cdot \sum_{i} data_{i})^{beta} \f]
The sum is taken over a region of a side length `size` and number of dimensions equal to number of axes.
The region is centered at the input value that's being normalized (with zero padding added if needed).

Here is an example for 4D `data` input tensor and `axes = [1]`:
```
sqr_sum[a, b, c, d] =
    sum(data[a, max(0, b - size / 2) : min(data.shape[1], b + size / 2 + 1), c, d] ** 2)
output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta
```

Example for 4D `data` input tensor and `axes = [2, 3]`:
```
sqr_sum[a, b, c, d] =
    sum(data[a, b, max(0, c - size / 2) : min(data.shape[2], c + size / 2 + 1),  max(0, d - size / 2) : min(data.shape[3], d + size / 2 + 1)] ** 2)
output = data / (bias + (alpha / size ** len(axes)) * sqr_sum) ** beta
```

**Attributes**:

* *alpha*

  * **Description**: *alpha* represents the scaling attribute for the normalizing sum. For example, *alpha* equal `0.0001` means that the normalizing sum is multiplied by `0.0001`.
  * **Range of values**: no restrictions
  * **Type**: `float`
  * **Required**: *yes*

* *beta*

  * **Description**: *beta* represents the exponent for the normalizing sum. For example, *beta* equal `0.75` means that the normalizing sum is raised to the power of `0.75`.
  * **Range of values**: positive number
  * **Type**: `float`
  * **Required**: *yes*

* *bias*

  * **Description**: *bias* represents the offset. Usually positive number to avoid dividing by zero.
  * **Range of values**: no restrictions
  * **Type**: `float`
  * **Required**: *yes*

* *size*

  * **Description**: *size* represents the side length of the region to be used for the normalization sum. The region can have one or more dimensions depending on the second input axes indices.
  * **Range of values**: positive integer
  * **Type**: `int`
  * **Required**: *yes*

**Inputs**

* **1**: `data` - tensor of type *T* and arbitrary shape. **Required.**

* **2**: `axes` - 1D tensor of type *T_IND* which specifies indices of dimensions in `data` which define normalization slices. **Required.**

**Outputs**

* **1**: Output tensor of type *T* and the same shape as the `data` input tensor.

**Types**
* *T*: any supported floating-point type.
* *T_IND*: any supported integer type.

**Example**

```xml
<layer id="1" type="LRN" ...>
    <data alpha="1.0e-04" beta="0.75" size="5" bias="1"/>
    <input>
        <port id="0">
            <dim>6</dim>
            <dim>12</dim>
            <dim>10</dim>
            <dim>24</dim>
        </port>
        <port id="1">
            <dim>1</dim>         <!-- value is [1] that means independent normalization for each pixel along channels -->
        </port>
    </input>
    <output>
        <port id="2">
            <dim>6</dim>
            <dim>12</dim>
            <dim>10</dim>
            <dim>24</dim>
        </port>
    </output>
</layer>
```
