TensorFlow 2 version
|
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.
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