mx.symbol.L2Normalization
¶
Description¶
Normalize the input array using the L2 norm.
For 1-D NDArray, it computes:
out = data / sqrt(sum(data ** 2) + eps)
For N-D NDArray, if the input array has shape (N, N, ..., N),
with ``mode`` = ``instance``, it normalizes each instance in the multidimensional
array by its L2 norm.:
for i in 0...N
out[i,:,:,...,:] = data[i,:,:,...,:] / sqrt(sum(data[i,:,:,...,:] ** 2) + eps)
with mode
= channel
, it normalizes each channel in the array by its L2 norm.:
for i in 0...N
out[:,i,:,...,:] = data[:,i,:,...,:] / sqrt(sum(data[:,i,:,...,:] ** 2) + eps)
with ``mode`` = ``spatial``, it normalizes the cross channel norm for each position
in the array by its L2 norm.:
for dim in 2...N
for i in 0...N
out[.....,i,...] = take(out, indices=i, axis=dim) / sqrt(sum(take(out, indices=i, axis=dim) ** 2) + eps)
-dim-
Example:
x = [[[1,2],
[3,4]],
[[2,2],
[5,6]]]
L2Normalization(x, mode='instance')
=[[[ 0.18257418 0.36514837]
[ 0.54772252 0.73029673]]
[[ 0.24077171 0.24077171]
[ 0.60192931 0.72231513]]]
L2Normalization(x, mode='channel')
=[[[ 0.31622776 0.44721359]
[ 0.94868326 0.89442718]]
[[ 0.37139067 0.31622776]
[ 0.92847669 0.94868326]]]
L2Normalization(x, mode='spatial')
=[[[ 0.44721359 0.89442718]
[ 0.60000002 0.80000001]]
[[ 0.70710677 0.70710677]
[ 0.6401844 0.76822126]]]
Usage¶
mx.symbol.L2Normalization(...)
Arguments¶
Argument |
Description |
---|---|
|
NDArray-or-Symbol. Input array to normalize. |
|
float, optional, default=1.00000001e-10. A small constant for numerical stability. |
|
{‘channel’, ‘instance’, ‘spatial’},optional, default=’instance’. Specify the dimension along which to compute L2 norm. |
|
string, optional. Name of the resulting symbol. |
Value¶
out
The result mx.symbol
Link to Source Code: http://github.com/apache/incubator-mxnet/blob/1.6.0/src/operator/l2_normalization.cc#L196