tf.raw_ops.LRN | TensorFlow v2.16.1 (original) (raw)
tf.raw_ops.LRN
Stay organized with collections Save and categorize content based on your preferences.
Local Response Normalization.
View aliases
Compat aliases for migration
SeeMigration guide for more details.
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 withindepth_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. |