cvap_rha.cpp

#include "SparseConvNet.h"
#include "SpatiallySparseDatasetCVAP_RHA.h"

int epoch=0;
int cudaDevice=-1;
int batchSize=1;


class CNN : public SparseConvNet {
public:
  CNN (int dimension, int l, ActivationFunction fn, int nInputFeatures, int nClasses, float p=0.0f, int cudaDevice=-1, int nTop=1) : SparseConvNet(dimension,nInputFeatures, nClasses, cudaDevice, nTop){
    for (int i=0;i<=l;i++)
      addLeNetLayerMP(32*(i+1) + 32*std::max(i-3,0),
                      2,
                      1,
                      (i<l)?3:1,
                      (i<l)?2:1,
                      fn,
                      p*i*1.0f/l);
    addSoftmaxLayer();
  }
};

int main(int lenArgs, char *args[]) {
  std::string baseName="weights/CVAP_RHA";
  SpatiallySparseDataset trainSet=CVAP_RHA_TrainSet();
  SpatiallySparseDataset validationSet=CVAP_RHA_ValidationSet();
  SpatiallySparseDataset testSet=CVAP_RHA_TestSet();
  trainSet.summary();
  validationSet.summary();
  testSet.summary();

  CNN cnn(3,6,VLEAKYRELU,trainSet.nFeatures,trainSet.nClasses,0.2,cudaDevice);
  if (epoch>0) {
    cnn.loadWeights(baseName,epoch);
  }
  for (epoch++;epoch<=100;epoch++) {
    std::cout <<"epoch:" << epoch << ":\n";
    for (int i=0;i<20;++i)
      cnn.processDataset(trainSet, batchSize,0.003*exp(-0.05 * epoch),0.9999);
    cnn.saveWeights(baseName,epoch);
    cnn.processDatasetRepeatTest(validationSet,  batchSize, 3);
  }
  cnn.processDatasetRepeatTest(testSet,  batchSize, 3);
}