# tf.nn.local_response_normalization(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None)

### tf.nn.lrn(input, depth_radius=None, bias=None, alpha=None, beta=None, name=None)

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: float32, half. 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.

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