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#include "MarginPerceptronCostVariable.h"
00045
00046
namespace PLearn {
00047
using namespace std;
00048
00051
PLEARN_IMPLEMENT_OBJECT(
00052 MarginPerceptronCostVariable,
00053
"Compute sigmoid of its first input, and then computes the negative "
00054
"cross-entropy cost",
00055
"NO HELP");
00056
00058
00060 MarginPerceptronCostVariable::MarginPerceptronCostVariable(
Variable* output,
Variable* target,
real m)
00061 :
inherited(output,target,1,1),margin(m)
00062 {
00063
build_();
00064 }
00065
00066
void
00067 MarginPerceptronCostVariable::build()
00068 {
00069 inherited::build();
00070
build_();
00071 }
00072
00073
void
00074 MarginPerceptronCostVariable::build_()
00075 {
00076
00077
if (input2 && input2->size() != 1)
00078
PLERROR(
"In MarginPerceptronCostVariable: target represents a class (0...n_classes-1) and must be a single integer");
00079 }
00080
00081
void
00082 MarginPerceptronCostVariable::declareOptions(
OptionList &ol)
00083 {
00084
declareOption(ol,
"margin", &MarginPerceptronCostVariable::margin, OptionBase::buildoption,
"");
00085 inherited::declareOptions(ol);
00086 }
00087
00089
00091 void MarginPerceptronCostVariable::recomputeSize(
int& l,
int& w)
const
00092
{ l=1, w=1; }
00093
00095
00097 void MarginPerceptronCostVariable::fprop()
00098 {
00099
real cost = 0.0;
00100
int target =
int(input2->valuedata[0]);
00101
for (
int i=0; i<input1->size(); i++)
00102 {
00103
real output = input1->valuedata[i];
00104
int signed_target = input1->size()==1?target*2-1:(target==i) - (target!=i);
00105
real diff =
margin - signed_target * output;
00106
if (diff>0)
00107 cost += diff;
00108 }
00109 valuedata[0] = cost;
00110 }
00111
00113
00115 void MarginPerceptronCostVariable::bprop()
00116 {
00117
real gr = *gradientdata;
00118
int target =
int(input2->valuedata[0]);
00119
for (
int i=0; i<input1->size(); i++)
00120 {
00121
real output = input1->valuedata[i];
00122
int signed_target = input1->size()==1?target*2-1:(target==i) - (target!=i);
00123
real diff =
margin - signed_target * output;
00124
if (diff>0)
00125 input1->gradientdata[i] -= gr*signed_target;
00126 }
00127 }
00128
00129 }