神经网络学习过程的实例

function [ret1,ret2,ret3,ret4]=nnd11bc(cmd,arg1,arg2,arg3,arg4,arg5) % NND11BC Backpropagation calculation demonstration. % % This demonstration requires the Neural Network Toolbox. % % NND11BC runs this demo. % % NND11BC('set',W1,b1,W2,b2) % sets the network's weight and bias values. % % [W1,b1,W2,b2] = NND11BC('get') % gets the network's weight and bias values. % Copyright 1994-2002 PWS Publishing Company and The MathWorks, Inc. % $Revision: 1.7 $ % First Version, 8-31-95. %================================================================== % CONSTANTS me = 'nnd11bc'; max_t = 0.5; w_max = 10; p_max = 2; box_len = 40; box_x = [0 1 1 0 0]*box_len; box_y = [-1 -1 1 1 -1]*10; pause_time = 1; % FLAGS change_func = 0; % DEFAULTS if nargin == 0, cmd = ''; else cmd = lower(cmd); end % FIND WINDOW IF IT EXISTS fig = nndfgflg(me); if length(get(fig,'children')) == 0, fig = 0; end % GET WINDOW DATA IF IT EXISTS if fig H = get(fig,'userdata'); fig_axis = H(1); % window axis desc_text = H(2); % handle to first line of text sequence W1_ptr = H(3); b1_ptr = H(4); W2_ptr = H(5); b2_ptr = H(6); p_name = H(7); W11_box = H(8); W11_text = H(9); W11_name = H(10); W12_box = H(11); W12_text = H(12); W12_name= H(13); b11_box = H(14); b11_text = H(15); b11_name = H(16); b12_box = H(17); b12_text = H(18); b12_name = H(19); a11_name = H(20); a12_name = H(21); W21_box = H(22); W21_text = H(23); W21_name = H(24); W22_box = H(25); W22_text = H(26); W22_name = H(27); b2_box = H(28); b2_text = H(29); b2_name = H(30); a2_name = H(31); t_name = H(32); e_name = H(33); vars = H(34+[0:10]); vals = H(45+[0:10]); fp1_marker = H(56); fp2_marker = H(57); fp3_marker = H(58); bp1_marker = H(59); bp2_marker = H(60); W1_marker = H(61); b1_marker = H(62); W2_marker = H(63); b2_marker = H(64); p_marker = H(65); t_marker = H(66); state_ptr = H(67); p_ptr = H(68); a1_ptr = H(69); a2_ptr = H(70); e_ptr = H(71); s1_ptr = H(72); s2_ptr = H(73); t_ptr = H(74); go_button = H(75); s11_name = H(76); s12_name = H(77); s2_name = H(78); blip_ptr = H(79); bloop_ptr = H(80); blp_ptr = H(81); state1 = H(82); state2 = H(83); state3 = H(84); state4 = H(85); go_box = H(86); last_text = H(87); p_edit = H(88); state = get(state_ptr,'userdata'); blip = get(blip_ptr,'userdata'); bloop = get(bloop_ptr,'userdata'); blp = get(blp_ptr,'userdata'); end %================================================================== % Activate the window. % % ME() or ME('') %================================================================== if strcmp(cmd,'') if fig figure(fig) set(fig,'visible','on') else feval(me,'init') end %================================================================== % Close the window. % % ME() or ME('') %================================================================== elseif strcmp(cmd,'close') & (fig) delete(fig) %================================================================== % Initialize the window. % % ME('init') %================================================================== elseif strcmp(cmd,'init') & (~fig) % CHECK FOR NNT if ~nntexist(me), return, end % CONSTANTS W1 = [-0.27; -0.41]; b1 = [-0.48; -0.13]; W2 = [0.09 -0.17]; b2 = [0.48]; %%%%%%%% Copied from NNDEMOF2 s2 = 'DESIGN'; s3 = 'Backpropagation Calculation'; s4 = ''; s5 = 'Chapter 11'; fig = nnbg(me); set(fig,'nextplot','add') H = get(fig,'userdata'); h1 = H(1); text(25,380,'Neural Network', ... 'color',nnblack, ... 'fontname','times', ... 'fontsize',16, ... 'fontangle','italic', ... 'fontweight','bold'); text(135,380,s2, ... 'color',nnblack, ... 'fontname','times', ... 'fontsize',16, ... 'fontweight','bold'); text(415,380,s3,... 'color',nnblack, ... 'fontname','times', ... 'fontsize',16, ... 'fontweight','bold',... 'HorizontalAlignment','right'); nndrwlin([0 415],[365 365],4,nndkblue); h2 = text(390,315,'',... 'color',nnblack, ... 'fontname','helvetica', ... 'fontsize',10); text1 = h2; for i=1:30 text2 = text(390,315-6*i,'',... 'color',nnblack, ... 'fontname','helvetica', ... 'fontsize',10); set(text1,'userdata',text2); text1 = text2; end set(text1,'userdata','end'); text(410,54,s4, ... 'color',nnblack, ... 'fontname','times', ... 'fontsize',12, ... 'fontweight','bold'); text(410,38,s5, ... 'color',nnblack, ... 'fontname','times', ... 'fontsize',12, ... 'fontweight','bold'); nndrwlin([410 501],[24 24],4,nndkblue); set(fig,'userdata',[h1 h2]) set(fig,'color',nndkgray,'color',nnltgray) %%%%%%%% set(fig, ... 'windowbuttondownfcn',nncallbk(me,'down'), ... 'BackingStore','off'); H = get(fig,'userdata'); fig_axis = H(1); desc_text = H(2); % ICON nndicon(11,458,363,'shadow') % NETWORK POSITIONS x1 = 30; % input x2 = x1+85; % 1st layer sum x3 = x2+70; % 1st layer transfer function x4 = x3+125; % 2nd layer sum x5 = x4+55; % 2nd layer transfer function x6 = x5+50; % output y1 = 305; % top neuron y2 = y1-20; % input & output neuron y3 = y1-40; % bottom neuron sz = 15; % size of icons wx = 50; % weight horizontal offset (from 1st layer) wy = 40; % weight vertical offset (from middle) % NETWORK INPUT p_name = nndtext(x1-10,y2,'p'); set(p_name,'fontsize',10); % TOP NEURON: WEIGHT plot([x1 x1+20],[y2 y1],'linewidth',2,'color',nnred); W11_box = fill(x1+20+box_x,y1+box_y,nnltgray,... 'linewidth',2,... 'edgecolor',nnred,... 'erasemode','none'); W11_text = nndtext(x1+20+box_len/2,y1,sprintf('%5.3f',W1(1))); set(W11_text,'fontsize',10); plot([x1+20+box_len x2-sz],[y1 y1],'linewidth',2,'color',nnred); W11_name = nndtext(x2-wx,y2+wy,'W1(1,1)'); set(W11_name,'fontsize',10); % TOP NEURON: BIAS plot([x2 x2 x3],[y1+sz*2 y1 y1],'linewidth',2,'color',nnred); b11_box = fill(x2-box_len/2+box_x,y1+sz*2+10+box_y,nnltgray,... 'linewidth',2,... 'edgecolor',nnred,... 'erasemode','none'); b11_text = nndtext(x2,y1+sz*2+10,sprintf('%5.3f',b1(1))); set(b11_text,'fontsize',10); b11_name = nndtext(x2+25,y1+sz*2+10,'b1(1)','left'); set(b11_name,'fontsize',10); % TOP NEURON: BODY nndsicon('sum',x2,y1,sz) n11_name = nndtext(x2+sz+20,y1+10,'n1(1)'); set(n11_name,'fontsize',10); nndsicon('logsig',x3,y1,sz) s11_name = nndtext(x2+sz+75,y1+40,'s1(1)'); set(s11_name,'fontsize',10); plot(x2+sz+[30 60],y1+[18 32],'--',... 'color',nnred,... 'linewidth',2,... 'erasemode','none') a11_name = nndtext(x3+sz+20,y1+10,'a1(1)'); set(a11_name,'fontsize',10); % BOTTOM NEURON: WEIGHT plot([x1 x1+20],[y2 y3],'linewidth',2,'color',nnred); W12_box = fill(x1+20+box_x,y3+box_y,nnltgray,... 'linewidth',2,... 'edgecolor',nnred,... 'erasemode','none'); W12_text = nndtext(x1+20+box_len/2,y3,sprintf('%5.3f',W1(2))); set(W12_text,'fontsize',10); plot([x1+20+box_len x2-sz],[y3 y3],'linewidth',2,'color',nnred); W12_name = nndtext(x2-wx,y2-wy,'W1(2,1)'); set(W12_name,'fontsize',10); % BOTTOM NEURON: BIAS plot([x2 x2 x3],[y3-sz*2 y3 y3],'linewidth',2,'color',nnred); b12_box = fill(x2-box_len/2+box_x,y3-sz*2-10+box_y,nnltgray,... 'linewidth',2,... 'edgecolor',nnred,... 'erasemode','none'); b12_text = nndtext(x2,y3-sz*2-10,sprintf('%5.3f',b1(2))); set(b12_text,'fontsize',10); b12_name = nndtext(x2+25,y3-sz*2-10,'b1(2)','left'); set(b12_name,'fontsize',10); % BOTTOM NEURON: BODY nndsicon('sum',x2,y3,sz) n12_name = nndtext(x2+sz+20,y3-10,'n1(2)'); set(n12_name,'fontsize',10); nndsicon