LocalResponseNorm¶
- class
dragon.vm.torch.nn.
LocalResponseNorm
(
size,
alpha=0.0001,
beta=0.75,
k=1.0
)[source]¶ Apply the local response normalization. [Krizhevsky et.al, 2012].
The normalization is defined as:
\[y_{i} = x_{i}\left(k + \frac{\alpha}{n} \sum_{j=\max(0, i-n/2)}^{\min(N-1,i+n/2)}x_{j}^2 \right)^{-\beta} \]Examples:
m = torch.nn.LocalResponseNorm(5) x = torch.randn(2, 4) y = m(x)
__init__¶
LocalResponseNorm.
__init__
(
size,
alpha=0.0001,
beta=0.75,
k=1.0
)[source]¶Create a
GroupNorm
module.- Parameters:
- size (int, required) – The number of neighbouring channels to sum over.
- alpha (float, optional, default=0.0001) – The value to \(\alpha\).
- beta (float, optional, default=0.75) – The value to \(\beta\).
- k (float, optional, default=1.) – The value to \(k\).