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algorithmmatlabneural-networkbackpropagation

Performance comparison plotting for different Back propagation algorithms

I am implementing various Backpropagation algorithms for the same dataset and trying to compare the performance. I got a help from the following tutorial for the same.

https://nl.mathworks.com/help/nnet/ug/choose-a-multilayer-neural-network-training-function.html

I tried to plot:

  1. mean square error versus execution time for each algorithm
  2. time required to converge versus the mean square error convergence goal for each algorithm

Have used the following code, to create my neural network and willing to know how can i implement the above two plots. 

%Data
x=0:0.2:6*pi; y=sin(x);
p=con2seq(x); t=con2seq(y);

% Networks
net1=feedforwardnet(20,'trainlm');
net2=feedforwardnet(20,'traingd');
net2.iw{1,1}=net1.iw{1,1};  %set the same weights and biases for the networks 
net2.lw{2,1}=net1.lw{2,1};
net2.b{1}=net1.b{1};
net2.b{2}=net1.b{2};

%training and simulation
net1.trainParam.epochs=1;  % set the number of epochs for the training 
net2.trainParam.epochs=1;
net1=train(net1,p,t);   % train the networks
net2=train(net2,p,t);
a11=sim(net1,p); a21=sim(net2,p);  % simulate the networks with the input vector p


net1.trainParam.epochs=14;
net2.trainParam.epochs=14;
net1=train(net1,p,t);
net2=train(net2,p,t);
a12=sim(net1,p); a22=sim(net2,p);

net1.trainParam.epochs=985;
net2.trainParam.epochs=985;
net1=train(net1,p,t);
net2=train(net2,p,t);
a13=sim(net1,p); a23=sim(net2,p);

%plots
figure
subplot(3,3,1);
plot(x,y,'bx',x,cell2mat(a11),'r',x,cell2mat(a21),'g'); % plot the sine function and the output of the networks
title('1 epoch');
legend('target','trainlm','traingd');
subplot(3,3,2);
postregm(cell2mat(a11),y); % perform a linear regression analysis and plot the result
subplot(3,3,3);
postregm(cell2mat(a21),y);

%
subplot(3,3,4);
plot(x,y,'bx',x,cell2mat(a12),'r',x,cell2mat(a22),'g');
title('15 epochs');
legend('target','trainlm','traingd');
subplot(3,3,5);
postregm(cell2mat(a12),y);
subplot(3,3,6);
postregm(cell2mat(a22),y);

%
subplot(3,3,7);
plot(x,y,'bx',x,cell2mat(a13),'r',x,cell2mat(a23),'g');
title('1000 epochs');
legend('target','trainlm','traingd');
subplot(3,3,8);
postregm(cell2mat(a13),y);
subplot(3,3,9);
postregm(cell2mat(a23),y);
Solution

  • Note that MSE is used by default when no measure of error is specified.

    When training you can do something like:

    [net tr] = train(net, x, t);

    Then plot tr.perf or tr.tperf or tr.vperf