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).
Returns | |
---|---|
A Tensor . Has the same type as input .
|