int main(int argc, char** argv) { Cvector v(6, Random()); Vectorv v1(v); Vectorl v2(v); Vectorh v3(v); cout << v.str(Dense()) << endl; cout << v1.str(Dense()) << endl; cout << v2.str(Dense()) << endl; cout << v3.str(Dense()) << endl; cout << Vectorv(v1) << endl; for (auto& p : v1) cout << p.first << " " << p.second << endl; cout << endl; for (auto& p : v2) cout << p.first << " " << p.second << endl; cout << endl; for (auto& p : v3) cout << p.first << " " << p.second << endl; cout << endl; v1.insert(3, 74); v1(2) = 0; cout << v1 << endl; v1.tidy(); cout << v1 << endl; v1.foreach([](int i, FIELD& v) {cout<<"ff:"<<i<<" "<<v<<endl;}); }
int main(int argc, char* argv[]) { int input_size = 10; int dense_size1 = 5; int dense_size2 = 3; Container model; model.add(Dense(dense_size1, input_size)); model.add(Dense(dense_size2)); auto layer = model.get_layer(); for (unsigned int i = 0; i < layer.size(); ++i) { auto dense = layer[i]; printf("layer : %d\tdense_size : %d\t prev_dense_size : %d\n", i, dense.dense_size(), dense.get_cell(0).edge_size()); for(unsigned int j = 0; j < dense.dense_size(); ++j) { auto cell = dense.get_cell(j); auto weight = cell.get_weight(); auto bias = cell.get_bias(); for(unsigned int k = 0; k < weight.size(); ++k) { printf("(%d,%d) : %lf\t%lf\n", j, k, weight[k], bias); } } } return 0; }
int main(int argc, char** argv) { int nrows = 6; if (argc >= 2) sscanf(argv[1], "%d", &nrows); int ncols = 6; if (argc >= 3) sscanf(argv[2], "%d", &ncols); Cmatrix Md = Cmatrix::Random(nrows, ncols); MatrixXv Mv(Md); MatrixXl Ml(Md); MatrixXh Mh(Md); cout << Md.str(Dense()) << endl; cout << Mv.str(Dense()) << endl; cout << Ml.str(Dense()) << endl; cout << Mh.str(Dense()) << endl; cout << Mh.nnz() << endl; }
string str(const Dense dummy) const { return Vector::str(Dense()); }
#include "TFile.h" #include "TTree.h" #include "TSystem.h" #include "TMVA/Factory.h" #include "TMVA/Reader.h" #include "TMVA/DataLoader.h" #include "TMVA/PyMethodBase.h" TString pythonSrc = "\ from keras.models import Sequential\n\ from keras.layers.core import Dense, Activation\n\ from keras import initializations\n\ from keras.optimizers import SGD\n\ \n\ model = Sequential()\n\ model.add(Dense(64, init=\"normal\", activation=\"tanh\", input_dim=2))\n\ model.add(Dense(1, init=\"normal\", activation=\"linear\"))\n\ model.compile(loss=\"mean_squared_error\", optimizer=SGD(lr=0.01))\n\ model.save(\"kerasModelRegression.h5\")\n"; int testPyKerasRegression(){ // Get data file std::cout << "Get test data..." << std::endl; TString fname = "./tmva_reg_example.root"; if (gSystem->AccessPathName(fname)) // file does not exist in local directory gSystem->Exec("curl -O http://root.cern.ch/files/tmva_reg_example.root"); TFile *input = TFile::Open(fname); // Build model from python file std::cout << "Generate keras model..." << std::endl; UInt_t ret;