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)
void	conv_single_depth_valid (std::vector< std::vector< float > > &y, std::vector< std::vector< float > > const &im, std::vector< std::vector< float > > const &k)
void	conv_single_depth_same (std::vector< std::vector< float > > &y, std::vector< std::vector< float > > const &im, std::vector< std::vector< float > > const &k)

Function Documentation

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

Definition at line 273 of file keras_model.cc.

{
  size_t k1_size = k.size(), k2_size = k[0].size();
  unsigned int st_x = (k1_size - 1) >> 1;
  unsigned int st_y = (k2_size - 1) >> 1;

  size_t max_imc = im.size() - 1;
  size_t max_imr = im[0].size() - 1;

  for(unsigned int i = 0; i < im.size(); ++i) {
    for(unsigned int j = 0; j < im[0].size(); ++j) {

      float sum = 0;
      for(unsigned int k1 = 0; k1 < k1_size; ++k1) {
        //const float * k_data = k[k1_size-k1-1].data();
        //const float * im_data = im[i-st_x+k1].data();
        for(unsigned int k2 = 0; k2 < k2_size; ++k2) {
          if(i+k1 < st_x) continue;
          if(i+k1 > max_imc+st_x) continue;
          if(j+k2 < st_y) continue;
          if(j+k2 > max_imr+st_y) continue;

          //sum += k_data[k2_size-k2-1] * im_data[j-st_y+k2];
          sum += k[k1_size-k1-1][k2_size-k2-1] * im[i-st_x+k1][j-st_y+k2];
        }
      }
      y[i][j] += sum;
    }
  }
}

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

Definition at line 246 of file keras_model.cc.

{
  size_t k1_size = k.size(), k2_size = k[0].size();
  unsigned int st_x = (k1_size - 1) >> 1;
  unsigned int st_y = (k2_size - 1) >> 1;

  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) {

      float sum = 0;
      for(unsigned int k1 = 0; k1 < k1_size; ++k1) {
        //const float * k_data = k[k1_size-k1-1].data();
        //const float * im_data = im[i-st_x+k1].data();
        for(unsigned int k2 = 0; k2 < k2_size; ++k2) {
          //sum += k_data[k2_size-k2-1] * im_data[j-st_y+k2];
          sum += k[k1_size-k1-1][k2_size-k2-1] * im[i-st_x+k1][j-st_y+k2];
        }
      }
      y[i-st_x][j-st_y] += sum;
    }
  }
}

void keras::missing_activation_impl

(

const std::string &

act

)

Definition at line 175 of file keras_model.cc.

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

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

Definition at line 11 of file keras_model.cc.

                                                                {
  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;
}