nnd12vl.asv

《神经网络设计》英文版的配套源代码

  uicontrol(...
    'units','points',...
    'position',[400 110 60 20],...
    'string','Contents',...
    'callback','nndtoc')
  uicontrol(...
    'units','points',...
    'position',[400 75 60 20],...
    'string','Close',...
    'callback',[me '(''close'')'])

  % DATA POINTERS
  dummy = 0;
  cont_ptr = uicontrol('visible','off','userdata',cont_h);
  option_ptr = uicontrol('visible','off','userdata',option);
  path_ptr = uicontrol('visible','off','userdata',[]);

  % SAVE WINDOW DATA AND LOCK
  H = [fig_axis desc_text lr_bar lr_text inc_bar inc_text ...
       dec_bar dec_text cont_axis cont_ptr ...
       var1 var2 var3 var4 radio1 radio2 radio3 option_ptr path_ptr];
  set(fig,'userdata',H,'nextplot','new')

  % INSTRUCTION TEXT
  feval(me,'instr');

  % LOCK WINDOW
  set(fig,'nextplot','new','color',nnltgray);

  nnchkfs;

%==================================================================
% Display the instructions.
%
% ME('instr')
%==================================================================

elseif strcmp(cmd,'instr') & (fig)
  nnsettxt(desc_text,...
    'Use the radio buttons',...
    'to select the network',...
    'parameters to train',...
    'with backpropagation.',...
    '',...
    'The corresponding',...
    'contour plot is',...
    'shown below.',...
    '',...
    'Click in the contour',...
    'graph to start the',...
    'variable learning',...
    'rate backpropagation',...
    'learning algorithm.')
    
%==================================================================
% Respond to radio buttons.
%
% ME('radio',i)
%==================================================================

elseif strcmp(cmd,'radio') & (fig) & (nargin == 2)

  % GET DATA
  option = get(option_ptr,'userdata');
  
  % ALTER TRAINABLE PARAMETERS
  if (arg1 ~= option)

    % HIGHLIGHT NEW RADIO BUTTON
    set(radios(option),'value',0)
    set(radios(arg1),'value',1)
    option = arg1;

    % CLEAR AXES
    delete(get(cont_axis,'children'))

    % CONSTANTS
    W1 = [10; 10];
    b1 = [-5;5];
    W2 = [1 1];
    b2 = [-1];
    P = -2:0.1:2;
    [R,Q] = size(P);
    %A1 = logsig(W1*P+b1*ones(1,Q));
    %T = logsig(W2*A1+b2*ones(1,Q));
    A1 = nndlogsig(W1*P+b1*ones(1,Q));
    T = nndlogsig(W2*A1+b2*ones(1,Q));

    % ERROR SURFACE & VARIABLE NAMES
    if option == 1
      load nndbp1
      set(variables([1 4]),'color',[1 1 1])
      set(variables([2 3]),'color',nndkblue)
    elseif option == 2
      load nndbp2
      set(variables([1 2]),'color',[1 1 1])
      set(variables([3 4]),'color',nndkblue)
    else
      load nndbp3
      set(variables([2 3]),'color',[1 1 1])
      set(variables([1 4]),'color',nndkblue)
    end

    set(fig,'nextplot','add')
    axes(cont_axis)
    set(get(cont_axis,'xlabel'),'string',v1)
    set(get(cont_axis,'ylabel'),'string',v2)
    set(cont_axis,'xlim',range1,'ylim',range2)
    [dummy,cont_h] = contour(x2,y2,E2,levels);
    set(cont_h,'erasemode','none');
    plot3(range1([1 2 2 1 1]),range2([1 1 2 2 1]),1000*ones(1,5),...
      'color',nndkblue);
    cont_h2 = plot(optx,opty,'+','color',nnred);
    cont_h = [cont_h; cont_h2];
    view(2)

    % STORE DATA
    set(cont_ptr,'userdata',cont_h);
    set(path_ptr,'userdata',[]);
    set(option_ptr,'userdata',option);
  end

%==================================================================
% Respond to learning rate slider.
%
% ME('lr')
%==================================================================

elseif strcmp(cmd,'lr')
  
  lr = get(lr_bar,'value');
  set(lr_text,'string',sprintf('%4.1f',round(lr*10)*0.1))

%==================================================================
% Respond to learning rate increase slider.
%
% ME('inc')
%==================================================================

elseif strcmp(cmd,'inc')
  
  inc = get(inc_bar,'value');
  set(inc_text,'string',sprintf('%4.2f',round(inc*100)*0.01))

%==================================================================
% Respond to learning rate decrease slider.
%
% ME('dec')
%==================================================================

elseif strcmp(cmd,'dec')
  
  dec = get(dec_bar,'value');
  set(dec_text,'string',sprintf('%4.2f',round(dec*100)*0.01))

%==================================================================
% Respond to mouse down.
%
% ME('down')
%==================================================================

elseif strcmp(cmd,'down') & (fig) & (nargin == 1)

  pt = get(cont_axis,'currentpoint');

  x = pt(1);
  y = pt(3);
  xlim = get(cont_axis,'xlim');
  ylim = get(cont_axis,'ylim');

  if (x > xlim(1) & x < xlim(2) & y > ylim(1) & y < ylim(2))

    % GET DATA
    option = get(option_ptr,'userdata');
    path = get(path_ptr,'userdata');
    cont_h = get(cont_ptr,'userdata');

    % REMOVE PREVIOUS PATH
    set(fig,'nextplot','add')
    delete(path);

    % INITIAL VALUES
    W1 = [10; 10];
    b1 = [-5;5];
    W2 = [1 1];
    b2 = [-1];
    P = -2:0.1:2;
    [R,Q] = size(P);
    %A1 = logsig(W1*P+b1*ones(1,Q));
    %T = logsig(W2*A1+b2*ones(1,Q));
    A1 = nndlogsig(W1*P+b1*ones(1,Q));
    T = nndlogsig(W2*A1+b2*ones(1,Q));

    % PLOT START POINT
    dkblue = nndkblue;
    red = nnred;
    axes(cont_axis);
    path = [...
      plot(x,y,'o','color',dkblue,'markersize',8,'erasemode','none');
      plot(x,y,'o','color',[1 1 1],'markersize',10,'erasemode','none');
      plot(x,y,'o','color',dkblue,'markersize',12,'erasemode','none')];
    drawnow;

    % PLOT PATH
    set(fig,'pointer','watch')

    % INITIALIZE TRAINING
    if option == 1
      W1(1,1) = x;
      W2(1,1) = y;
    elseif option == 2
      W1(1,1) = x;
      b1(1) = y;
    else
      b1(1) = x;
      b1(2) = y;
    end
    ep = 100;
    lr = get(lr_bar,'value');
    inc = get(inc_bar,'value');
    dec = get(dec_bar,'value');

    A1 = logsig(W1*P+b1*ones(1,Q));
    A2 = logsig(W2*A1+b2*ones(1,Q));
    E = T-A2;

    % BACKPROPAGATION PHASE
    D2 = A2.*(1-A2).*E;
    D1 = A1.*(1-A1).*(W2'*D2);
    SSE = sum(sum(E.*E)); %sumsqr(E);

    dW1 = 0;
    db1 = 0;
    dW2 = 0;
    db2 = 0;

    xx = [x zeros(1,ep)];
    yy = [y zeros(1,ep)];

    MC = mc;

    for i=2:(ep+1)

      % LEARNING PHASE
      dW1 = MC*dW1 + (1-MC)*D1*P'*lr;
      db1 = MC*db1 + (1-MC)*D1*ones(Q,1)*lr;
      dW2 = MC*dW2 + (1-MC)*D2*A1'*lr;
      db2 = MC*db2 + (1-MC)*D2*ones(Q,1)*lr;
      MC = mc;
      new_W1 = W1; new_b1 = b1;
      new_W2 = W2; new_b2 = b2;
      if (option == 1)
        newx = W1(1,1) + dW1(1,1); new_W1(1,1) = newx;
        newy = W2(1,1) + dW2(1,1); new_W2(1,1) = newy;
      elseif(option == 2)
        newx = W1(1,1) + dW1(1,1); new_W1(1,1) = newx;
        newy = b1(1)   + db1(1);   new_b1(1) = newy;
      else
        newx = b1(1) + db1(1);   new_b1(1) = newx;
        newy = b1(2) + db1(2);   new_b1(2) = newy;
      end

      % PRESENTATION PHASE
      new_A1 = logsig(new_W1*P+new_b1*ones(1,Q));
      new_A2 = logsig(new_W2*A1+new_b2*ones(1,Q));
      new_E = T-new_A2;
      new_SSE = sum(sum(new_E.*new_E)); %sumsqr(new_E);

      % MOMENTUM & ADAPTIVE LEARNING RATE PHASE
      if new_SSE > SSE*er
        lr = lr * dec;
        MC = 0;
      else
        if new_SSE < SSE
          lr = lr * inc;
        end
        W1 = new_W1; b1 = new_b1; A1 = new_A1;
        W2 = new_W2; b2 = new_b2; A2 = new_A2;
        x = newx;
        y = newy;
        E = new_E; SSE = new_SSE;
    
        % BACKPROPAGATION PHASE
        D2 = A2.*(1-A2).*E;
        D1 = A1.*(1-A1).*(W2'*D2);
      end

      % TRAINING RECORD
      xx(i) = x;
      yy(i) = y;
    end

    % CONTOUR PLOT
    path = [path; plot(xx,yy,'color',nnred); plot(xx,yy,'o','color',nnred,'markersize',6)];
    set(fig,'nextplot','new')
    
    % SAVE DATA
    set(path_ptr,'userdata',path);
    set(fig,'pointer','arrow')

  end
end