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<!--This HTML is auto-generated from an m-file.Your changes will be overwritten.--><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:#990000; font-weight:bold; font-size:x-large">Training a Linear Neuron</p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">A linear neuron is trained to respond to specific inputs with target outputs.</p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">Copyright 1992-2002 The MathWorks, Inc.$Revision: 1.14 $ $Date: 2002/03/29 19:36:18 $</p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:#990000; font-weight:bold; font-size:medium; page-break-before: auto;"><a name=""></a></p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">P defines two 1-element input patterns (column vectors). T defines associated1-element targets (column vectors). A single input linear neuron with a biascan be used to solve this problem.</p><pre xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="position: relative; left:30px">P = [1.0 -1.2];T = [0.5 1.0];</pre><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:#990000; font-weight:bold; font-size:medium; page-break-before: auto;"><a name=""></a></p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">ERRSURF calculates errors for a neuron with a range of possible weight andbias values. PLOTES plots this error surface with a contour plot underneath.The best weight and bias values are those that result in the lowest point onthe error surface.</p><pre xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="position: relative; left:30px">w_range = -1:0.2:1; b_range = -1:0.2:1;ES = errsurf(P,T,w_range,b_range,<span style="color:#B20000">'purelin'</span>);plotes(w_range,b_range,ES);</pre><img xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" src="demolin2_img03.gif"><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:#990000; font-weight:bold; font-size:medium; page-break-before: auto;"><a name=""></a></p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">MAXLINLR finds the fastest stable learning rate for training a linear network.For this demo, this rate will only be 40% of this maximum. NEWLIN creates alinear neuron. NEWLIN takes these arguments: 1) Rx2 matrix of min and maxvalues for R input elements, 2) Number of elements in the output vector, 3)Input delay vector, and 4) Learning rate.</p><pre xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="position: relative; left:30px">maxlr = 0.40*maxlinlr(P,<span style="color:#B20000">'bias'</span>);net = newlin([-2 2],1,[0],maxlr);</pre><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:#990000; font-weight:bold; font-size:medium; page-break-before: auto;"><a name=""></a></p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">Override the default training parameters by setting the performance goal.</p><pre xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="position: relative; left:30px">net.trainParam.goal = .001;</pre><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:#990000; font-weight:bold; font-size:medium; page-break-before: auto;"><a name=""></a></p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">To show the path of the training we will train only one epoch at a time andcall PLOTEP every epoch. The plot shows a history of the training. Each dotrepresents an epoch and the blue lines show each change made by the learningrule (Widrow-Hoff by default).</p><pre xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="position: relative; left:30px"><span style="color:green">% [net,tr] = train(net,P,T);</span>net.trainParam.epochs = 1;net.trainParam.show = NaN;h=plotep(net.IW{1},net.b{1},mse(T-sim(net,P))); [net,tr] = train(net,P,T); r = tr;epoch = 1;<span style="color:blue">while</span> true epoch = epoch+1; [net,tr] = train(net,P,T); <span style="color:blue">if</span> length(tr.epoch) > 1 h = plotep(net.IW{1,1},net.b{1},tr.perf(2),h); r.epoch=[r.epoch epoch]; r.perf=[r.perf tr.perf(2)]; r.vperf=[r.vperf NaN]; r.tperf=[r.tperf NaN]; <span style="color:blue">else</span> <span style="color:blue">break</span> <span style="color:blue">end</span><span style="color:blue">end</span>tr=r;</pre><img xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" src="demolin2_img06.gif"><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:#990000; font-weight:bold; font-size:medium; page-break-before: auto;"><a name=""></a></p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">The train function outputs the trained network and a history of the trainingperformance (tr). Here the errors are plotted with respect to trainingepochs: The error dropped until it fell beneath the error goal (the blackline). At that point training stopped.</p><pre xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="position: relative; left:30px">plotperf(tr,net.trainParam.goal);</pre><img xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" src="demolin2_img07.gif"><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:#990000; font-weight:bold; font-size:medium; page-break-before: auto;"><a name=""></a></p><p xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd">Now use SIM to test the associator with one of the original inputs, -1.2, andsee if it returns the target, 1.0. The result is very close to 1, the target.This could be made even closer by lowering the performance goal.</p><pre xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="position: relative; left:30px">p = -1.2;a = sim(net, p)</pre><pre xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" style="color:gray; font-style:italic;">a = 0.9817</pre><originalCode xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd" code="%% Training a Linear Neuron
% A linear neuron is trained to respond to specific inputs with target outputs.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.14 $ $Date: 2002/03/29 19:36:18 $

%%
% P defines two 1-element input patterns (column vectors). T defines associated
% 1-element targets (column vectors). A single input linear neuron with a bias
% can be used to solve this problem.

P = [1.0 -1.2];
T = [0.5 1.0];

%%
% ERRSURF calculates errors for a neuron with a range of possible weight and
% bias values. PLOTES plots this error surface with a contour plot underneath.
% The best weight and bias values are those that result in the lowest point on
% the error surface.

w_range = -1:0.2:1; b_range = -1:0.2:1;
ES = errsurf(P,T,w_range,b_range,'purelin');
plotes(w_range,b_range,ES);

%%
% MAXLINLR finds the fastest stable learning rate for training a linear network.
% For this demo, this rate will only be 40% of this maximum. NEWLIN creates a
% linear neuron. NEWLIN takes these arguments: 1) Rx2 matrix of min and max
% values for R input elements, 2) Number of elements in the output vector, 3)
% Input delay vector, and 4) Learning rate.

maxlr = 0.40*maxlinlr(P,'bias');
net = newlin([-2 2],1,[0],maxlr);

%%
% Override the default training parameters by setting the performance goal.

net.trainParam.goal = .001;

%%
% To show the path of the training we will train only one epoch at a time and
% call PLOTEP every epoch. The plot shows a history of the training. Each dot
% represents an epoch and the blue lines show each change made by the learning
% rule (Widrow-Hoff by default).

% [net,tr] = train(net,P,T);
net.trainParam.epochs = 1;
net.trainParam.show = NaN;
h=plotep(net.IW{1},net.b{1},mse(T-sim(net,P))); 
[net,tr] = train(net,P,T); 
r = tr;
epoch = 1;
while true
 epoch = epoch+1;
 [net,tr] = train(net,P,T);
 if length(tr.epoch) > 1
 h = plotep(net.IW{1,1},net.b{1},tr.perf(2),h);
 r.epoch=[r.epoch epoch]; 
 r.perf=[r.perf tr.perf(2)];
 r.vperf=[r.vperf NaN];
 r.tperf=[r.tperf NaN];
 else
 break
 end
end
tr=r;

%%
% The train function outputs the trained network and a history of the training
% performance (tr). Here the errors are plotted with respect to training
% epochs: The error dropped until it fell beneath the error goal (the black
% line). At that point training stopped.

plotperf(tr,net.trainParam.goal);

%%
% Now use SIM to test the associator with one of the original inputs, -1.2, and
% see if it returns the target, 1.0. The result is very close to 1, the target.
% This could be made even closer by lowering the performance goal.

p = -1.2;
a = sim(net, p)
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