Local Response Normalization.

tf.raw_ops.LRN( 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).

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` . |

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Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/LRN