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/TensorFlow Python

# tf.nn.local_response_normalization

### Aliases:

• `tf.nn.local_response_normalization`
• `tf.nn.lrn`
```tf.nn.local_response_normalization(
input,
bias=1,
alpha=1,
beta=0.5,
name=None
)
```

Defined in `tensorflow/python/ops/gen_nn_ops.py`.

See the guide: Neural Network > Normalization

Local Response Normalization.

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
```

#### 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).

#### Returns:

A `Tensor`. Has the same type as `input`.