mxnet.ndarray.LRN¶
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mxnet.ndarray.LRN(data=None, alpha=_Null, beta=_Null, knorm=_Null, nsize=_Null, out=None, name=None, **kwargs)¶ Applies local response normalization to the input.
The local response normalization layer performs “lateral inhibition” by normalizing over local input regions.
If \(a_{x,y}^{i}\) is the activity of a neuron computed by applying kernel \(i\) at position \((x, y)\) and then applying the ReLU nonlinearity, the response-normalized activity \(b_{x,y}^{i}\) is given by the expression:
\[b_{x,y}^{i} = \frac{a_{x,y}^{i}}{\Bigg({k + \frac{\alpha}{n} \sum_{j=max(0, i-\frac{n}{2})}^{min(N-1, i+\frac{n}{2})} (a_{x,y}^{j})^{2}}\Bigg)^{\beta}}\]where the sum runs over \(n\) “adjacent” kernel maps at the same spatial position, and \(N\) is the total number of kernels in the layer.
Defined in src/operator/nn/lrn.cc:L164
- Parameters
data (NDArray) – Input data to LRN
alpha (float, optional, default=0.0001) – The variance scaling parameter \(lpha\) in the LRN expression.
beta (float, optional, default=0.75) – The power parameter \(eta\) in the LRN expression.
knorm (float, optional, default=2) – The parameter \(k\) in the LRN expression.
nsize (int (non-negative), required) – normalization window width in elements.
out (NDArray, optional) – The output NDArray to hold the result.
- Returns
out – The output of this function.
- Return type
NDArray or list of NDArrays