local_response_norm¶
dragon.vm.torch.nn.functional.local_response_norm(
input,
size,
alpha=0.0001,
beta=0.75,
k=1.0
)[source]¶Apply the local response normalization to input. [Krizhevsky et.al, 2012].
The normalization is defined as:
\[y_{i} = x_{i}\left(k + \frac{\alpha}{n} \sum_{j=\max(0, i-n/2)}^{\min(N-1,i+n/2)}x_{j}^2 \right)^{-\beta} \]- Parameters:
- input (dragon.vm.torch.Tensor) – The input.
- size (int, required) – The number of neighbouring channels to sum over.
- alpha (float, optional, default=0.0001) – The scale value \(\alpha\).
- beta (float, optional, default=0.75) – The exponent value \(\beta\).
- k (float, optional, default=1.) – The bias constant \(k\).
- Returns:
dragon.vm.torch.Tensor – The output tensor.
See also