keras Namespace Reference

Classes
class	DataChunk
class	DataChunk2D
class	DataChunkFlat
class	KerasModel
class	Layer
class	LayerActivation
class	LayerConv2D
class	LayerDense
class	LayerFlatten
class	LayerMaxPooling

Functions
std::vector< float >	read_1d_array (std::ifstream &fin, int cols)
void	missing_activation_impl (const std::string &act)
std::vector< std::vector< float > >	conv_single_depth_valid (std::vector< std::vector< float > > const &im, std::vector< std::vector< float > > const &k)
std::vector< std::vector< float > >	conv_single_depth_same (std::vector< std::vector< float > > const &im, std::vector< std::vector< float > > const &k)

Function Documentation

std::vector< std::vector< float > > keras::conv_single_depth_same	(	std::vector< std::vector< float > > const &	im,
		std::vector< std::vector< float > > const &	k
	)

Definition at line 250 of file KerasModel.cxx.

References MECModelEnuComparisons::i, calib::j, gen_hdf5record::size, and submit_syst::y.

Referenced by keras::LayerConv2D::compute_output(), and missing_activation_impl().

{
  unsigned int st_x = (k.size() - 1) / 2;
  unsigned int st_y = (k[0].size() - 1) / 2;

  std::vector< std::vector<float> > y;
  for(unsigned int i = 0; i < im.size(); ++i) {
    y.emplace_back(vector<float>(im[0].size(), 0.0));
  }
  for(unsigned int i = 0; i < im.size(); ++i) {
    for(unsigned int j = 0; j < im[0].size(); ++j) {
      for(unsigned int k1 = 0; k1 < k.size(); ++k1) {
        for(unsigned int k2 = 0; k2 < k[0].size(); ++k2) {
          if(i+k1 < st_x) continue;
          if(i-st_x+k1 > im.size()-1) continue;
          if(j+k2 < st_y) continue;
          if(j-st_y+k2 > im[0].size()-1) continue;
          y[i][j] += k[k.size()-k1-1][k[0].size()-k2-1] * im[i-st_x+k1][j-st_y+k2];
        }
      }
    }
  }
  return y;
}

std::vector< std::vector< float > > keras::conv_single_depth_valid	(	std::vector< std::vector< float > > const &	im,
		std::vector< std::vector< float > > const &	k
	)

Definition at line 225 of file KerasModel.cxx.

References MECModelEnuComparisons::i, calib::j, gen_hdf5record::size, and submit_syst::y.

Referenced by keras::LayerConv2D::compute_output(), and missing_activation_impl().

{
  unsigned int st_x = (k.size() - 1) >> 1;
  unsigned int st_y = (k[0].size() - 1) >> 1;

  std::vector< std::vector<float> > y;
  for(unsigned int i = 0; i < im.size()-2*st_x; ++i) {
    y.emplace_back(vector<float>(im[0].size()-2*st_y, 0.0));
  }
  for(unsigned int i = st_x; i < im.size()-st_x; ++i) {
    for(unsigned int j = st_y; j < im[0].size()-st_y; ++j) {
      for(unsigned int k1 = 0; k1 < k.size(); ++k1) {
        for(unsigned int k2 = 0; k2 < k[0].size(); ++k2) {
          y[i-st_x][j-st_y] += k[k.size()-k1-1][k[0].size()-k2-1] * im[i-st_x+k1][j-st_y+k2];
        }
      }
    }
  }
  return y;
}

void keras::missing_activation_impl

(

const std::string &

act

)

Definition at line 176 of file KerasModel.cxx.

References keras::LayerActivation::compute_output(), keras::LayerConv2D::compute_output(), keras::LayerDense::compute_output(), keras::KerasModel::compute_output(), conv_single_depth_same(), conv_single_depth_valid(), om::cout, allTimeWatchdog::endl, exit(), stan::math::exp(), keras::DataChunk::get_1d(), keras::DataChunkFlat::get_1d_rw(), keras::DataChunk::get_3d(), keras::DataChunk::get_data_dim(), MECModelEnuComparisons::i, makeTrainCVSamples::int, calib::j, submit_hadd::l, CLHEP::L, m, keras::KerasModel::m_layers, confusionMatrixTree::out, keras::DataChunk::set_data(), gen_hdf5record::size, sum, tmp, w, submit_syst::x, and submit_syst::y.

Referenced by keras::LayerMaxPooling::compute_output(), and keras::LayerActivation::compute_output().

{
  std::cout << "Activation " << act << " not defined!" << std::endl;
  std::cout << "Please add its implementation before use." << std::endl;
  exit(1);
}

std::vector< float > keras::read_1d_array	(	std::ifstream &	fin,
		int	cols
	)

Definition at line 11 of file KerasModel.cxx.

References cols, and getGoodRuns4SAM::n.

Referenced by keras::DataChunk2D::read_from_file().

                                                                {
  vector<float> arr;
  float tmp_float;
  char tmp_char;
  fin >> tmp_char; // for '['
  for(int n = 0; n < cols; ++n) {
    fin >> tmp_float;
    arr.push_back(tmp_float);
  }
  fin >> tmp_char; // for ']'
  return arr;
}