Subversion Repositories artificial_neural_network

function wb_gen(NET)

[wbMat NLayers] = getwbc(NET);

MaxNeuronCnt = max_dim(wbMat);

fileid = fopen ('wb_init.vhd','w');

% addr weights:

% |_ _|  |0|  |_ _ _ _ _| |_ _ _ _ _ _|

% layer  bias    neuron      input

% *2^12+  0   + *2^5    +   *2^0

% addr biases:

% |_ _|  |1|  |0 0 0 0 0 0| |_ _ _ _ _|

% layer  bias                 neuron

% *2^12+ 2^11 +     0      +   *2^0

fprintf(fileid,'library ieee;\n');

fprintf(fileid,'use ieee.std_logic_1164.all;\n');

fprintf(fileid,'use ieee.numeric_std.all;\n');

fprintf(fileid,'library work;\n');

fprintf(fileid,'use work.support_pkg.all;\n');

fprintf(fileid,'use work.layers_pkg.all;\n');

fprintf(fileid,'package wb_init is\n');

fprintf(fileid,'  type ramd_type is array (%i downto 0) of std_logic_vector(NbitW-1 downto 0);\n',MaxNeuronCnt);

fprintf(fileid,'  type layer_ram is array (%i downto 0) of ramd_type;\n',MaxNeuronCnt);

fprintf(fileid,'  type w_ram  is array (integer range <>) of layer_ram;\n');

fprintf(fileid,'  type b_type is array (integer range <>) of ramd_type;\n');

fprintf(fileid,'  constant w_init : w_ram :=\n');

fprintf(fileid,'  (\n');

for(k=1:NLayers)

  fprintf(fileid,'    %i => (\n',k-1);

  for(i=1:size(cell2mat(wbMat(k,1)),2))  % neurons

    fprintf(fileid,'      %i => (\n',i-1);

    for(j=1:size(cell2mat(wbMat(k,1)),1))    % inputs

      fprintf(fileid,'        %i => real2stdlv(NbitW,%1.4f)',j-1, cell2mat(wbMat(k,1))(j,i));

      if j != size(cell2mat(wbMat(k,1)),1)

        fprintf(fileid,',\n');

      else

        fprintf(fileid,',\n        others =>(others => ''0'')\n');

      end;

    end;

    if i != size(cell2mat(wbMat(k,1)),2)

      fprintf(fileid,'      ),\n');

    else

      fprintf(fileid,'      ),\n      others=>(others =>(others => ''0''))\n');

    end;

  end;

  if k != NLayers

    fprintf(fileid,'    ),\n');

  else

    fprintf(fileid,'    )\n');

  end;

end;

fprintf(fileid,'  );\n\n');

fprintf(fileid,'  constant b_init : b_type :=\n');

fprintf(fileid,'  (\n');

for(k=1:NLayers)

  fprintf(fileid,'    %i => (\n',k-1);

  for(j=1:length(cell2mat(wbMat(k,2))))    % inputs

    fprintf(fileid,'      %i => real2stdlv(NbitW,(2.0**LSB_OUT)*(%1.4f))',j-1, cell2mat(wbMat(k,2))(j));

    if j != length(cell2mat(wbMat(k,2)))

      fprintf(fileid,',\n');

    else

      fprintf(fileid,',\n      others =>(others => ''0'')\n');

    end;

  end;

  if k != NLayers

    fprintf(fileid,'    ),\n');

  else

    fprintf(fileid,'    )\n');

  end;

end;

fprintf(fileid,'  );\n');

% fprintf(fileid,'    constant b0_init : ramd_type0 :=\n');

% fprintf(fileid,'    (\n');

% for(i=1:length(nn_data.b1))

%     fprintf(fileid,'      %i => real2stdlv(NbitW,(2.0**LSB_OUT)*(%.4f))',i-1,nn_data.b1(i));

%     if i != length(nn_data.b1)

%       fprintf(fileid,',\n');

%     else

%       fprintf(fileid,'\n');

%     end;

% end;

% fprintf(fileid,'    );\n');

% fprintf(fileid,'    constant b1_init : ramd_type1 :=\n');

% fprintf(fileid,'    (\n');

% for(i=1:length(nn_data.b2))

%     fprintf(fileid,'      %i => real2stdlv(NbitW,(2.0**LSB_OUT)*(%.4f))',i-1,nn_data.b2(i));

%     if i != length(nn_data.b2)

%       fprintf(fileid,',\n');

%     else

%       fprintf(fileid,'\n');

%     end;

% end;

% fprintf(fileid,'    );\n');

% fprintf(fileid,'    constant b2_init : ramd_type2 :=\n');

% fprintf(fileid,'    (\n');

% for(i=1:length(nn_data.b3))

%     fprintf(fileid,'      %i => real2stdlv(NbitW,(2.0**LSB_OUT)*(%.4f))',i-1,nn_data.b3(i));

%     if i != length(nn_data.b3)

%       fprintf(fileid,',\n');

%     else

%       fprintf(fileid,'\n');

%     end;

% end;

% fprintf(fileid,'    );\n');

fprintf(fileid,'end wb_init;\n');

fclose(fileid);