# tf.nn.local_response_normalization

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Local Response Normalization.

#### View aliases


**Main aliases**

[`tf.nn.lrn`](https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization)
**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.nn.local_response_normalization`](https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization), [`tf.compat.v1.nn.lrn`](https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization)

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    tf.nn.local_response_normalization(
        input, depth_radius=5, bias=1, alpha=1, beta=0.5, name=None
    )

The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last
dimension), and each vector is normalized independently. Within a given vector,
each component is divided by the weighted, squared sum of inputs within
`depth_radius`. In detail,

    sqr_sum[a, b, c, d] =
        sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
    output = input / (bias + alpha * sqr_sum) ** beta

For details, see [Krizhevsky et al., ImageNet classification with deep
convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).

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| ## Args ||
|---|---|
| `input` | A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. 4-D. |
| `depth_radius` | An optional `int`. Defaults to `5`. 0-D. Half-width of the 1-D normalization window. |
| `bias` | An optional `float`. Defaults to `1`. An offset (usually positive to avoid dividing by 0). |
| `alpha` | An optional `float`. Defaults to `1`. A scale factor, usually positive. |
| `beta` | An optional `float`. Defaults to `0.5`. An exponent. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `input`. ||

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