Esempio n. 1
0
  void matlab::read_cast_matrix(mxArray *var, idx<T> &m) {
#ifdef __MATLAB__
    // allocate a temporary matrix with same type as original matrix type
    idx<Tmatlab> tmp(m.get_idxdim());
    // load data
    void *data = mxGetData(var);
    // copy to idx 
    memcpy(m.idx_ptr(), (Tmatlab*) data, m.nelements() * sizeof (Tmatlab));
    // copy-cast
    idx_copy(tmp, m);
#endif
  }
Esempio n. 2
0
File: main.cpp Progetto: athuls/gsra
  generic_conv_net(parameter &trainableParam, 
		   intg output_size) 
    : layers_n<state_idx>(true) { // owns modules  
    cout << "Initializing ConvNet..." << endl;
    
    //! Define the number of feature maps per layer (C0, C1, C2)
    intg featureMaps0 = 6;
    intg featureMaps1 = 12;
    intg featureMaps2 = 40;

    //! Define tables of connections between layers.
    //! These two are fully connected layer, i.e. each feature map in a layer
    //! is connected to every feature map in the previous layer
    table0 = full_table(1, featureMaps0); //! from input to C0
    table2 = full_table(featureMaps1, featureMaps2); //! from S1 to C2

    //! ... whereas the connections there are sparse (S0 to C1):
    table1 = idx<intg>(44, 2); //! from S0 to C1
    intg tbl[44][2] =
      {{0, 0},  {1, 0},  {2, 0}, //! 0,1,2 in S0 connected to 0 in C1
       {1, 1},  {2, 1},  {3, 1}, //! and so on...
       {2, 2},  {3, 2},  {4, 2},
       {3, 3},  {4, 3},  {5, 3},
       {4, 4},  {5, 4},  {0, 4},
       {5, 5},  {0, 5},  {1, 5},

       {0, 6},  {1, 6},  {2, 6},  {3, 6},
       {1, 7},  {2, 7},  {3, 7},  {4, 7},
       {2, 8},  {3, 8},  {4, 8},  {5, 8},
       {3, 9},  {4, 9},  {5, 9},  {0, 9},
       {4, 10}, {5, 10}, {0, 10}, {1, 10},

       {0, 11}, {1, 11}, {2, 11}, {3, 11}, {4, 11}, {5, 11}};
    memcpy(table1.idx_ptr(), tbl, table1.nelements() * sizeof (intg));


    //! Finally we initialize the architecture of the ConvNet.
    //! In this case we create a CSCSCF network.
    //! It's easy to change the architecture, by simply removing/adding a call
    //! to addModule(...)

    //! C0 Layer
    add_module(new nn_layer_convolution(trainableParam, //! Shared weights
					7, 7, //! Dim of kernel 
					1, 1, //! size of subsampling
					table0), //! Conx btwn input layer and C0 
	       //! state_idx holds the feature maps of C0
	       new state_idx(featureMaps0,1,1));
    //! S0 Layer
    add_module(new nn_layer_subsampling(trainableParam, 
					2, 2,  //! Dim of stride
					2, 2,  //! Dim of subsampling mask
					featureMaps0),
	       new state_idx(featureMaps0,1,1));
    //! C1 Layer
    add_module(new nn_layer_convolution(trainableParam, 
					7, 7,
					1, 1, 
					table1), 
	       new state_idx(featureMaps1,1,1));
    //! S1 Layer
    add_module(new nn_layer_subsampling(trainableParam, 
					2, 2, 
					2, 2, 
					featureMaps1),
	       new state_idx(featureMaps1,1,1));
    //! C2 Layer
    add_module(new nn_layer_convolution(trainableParam, 
					7, 7,
					1, 1, 
					table2), 
	       new state_idx(featureMaps2,1,1));
    //! F Layer
    add_last_module(new nn_layer_full(trainableParam, 
				      featureMaps2, 
				      output_size));

  }