tf.nn.local_response_normalization
Local Response Normalization.
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).
| 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. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/nn/local_response_normalization