int main()
{
// Get data prepared
double **trainingData, **testData;
//readInData(&trainingData, &testData);
int minTestPicNum = 100;
readInMiniData(&trainingData, minTestPicNum);
// Get the network
struct SimpleNet myNet;
int inputSize = 724;
int layerNum = 2;
int layerSizes[2] = {100, 10};
initNetWork(&myNet, inputSize, layerNum, layerSizes);
// Simple test of the forward pass of the network
TestForwardPass(trainingData, minTestPicNum, &myNet);
// Params for learning
//values below are kind of hand-tuned with no math directions which need improving
double stepFactor = 0.003, minorDiff = 0.0001; //Set the parameter for M(i,j) = M(i,j) - stepParam*(Partial Derivative)
int maxIteration = 50; //As they always set it to 50 in Currennt
//Start doing backprpagation
//printf("\n%lf\n",(double)bp(&myNet, trainingData, minTestPicNum, stepFactor));
return 0;
}
void LogSender::sendMatches(list<MatchedLogRec>* matches)
{
readSendFaild(matches);
initNetWork();
while(matches->size()>0)
{
int sfd=send(fd,&(*(matches->begin())),sizeof(MatchedLogRec),0);
if(sfd<=0)
{
cout<<"send failed!"<<endl;
break;
}
matches->erase(matches->begin());
}
close(fd);
saveSendFaild(matches);
}