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/*!
* Copyright (c) 2015 by Contributors
* \file l2_normalization.cc
* \brief l2 normalization operator
*/
#include "./l2_normalization-inl.h"
namespace mxnet {
namespace op {
template<>
Operator* CreateOp<cpu>(L2NormalizationParam param) {
return new L2NormalizationOp<cpu>(param);
}
// DO_BIND_DISPATCH comes from static_operator_common.h
Operator* L2NormalizationProp::CreateOperator(Context ctx) const {
DO_BIND_DISPATCH(CreateOp, param_);
}
DMLC_REGISTER_PARAMETER(L2NormalizationParam);
MXNET_REGISTER_OP_PROPERTY(L2Normalization, L2NormalizationProp)
.describe(R"code(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]]]
)code" ADD_FILELINE)
.add_argument("data", "NDArray-or-Symbol", "Input array to normalize.")
.add_arguments(L2NormalizationParam::__FIELDS__());
} // namespace op
} // namespace mxnet