MATLAB: How to plot MSE for train and test

Deep Learning Toolboxneural networks

Hi,

How to plot MSE for train and test data? (need no curve for validation)Just want a comparison of these two.

Should the MSE curves for train and test meet eachother at some epoch or should they have many points in common to could say the networks is doing well?

Thanks

Best Answer

close all, clear all, clc, plt=0;

[x,t] = simplefit_dataset;

net = fitnet(10);

rng(0)

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

plt = plt+1, figure(plt), hold on;

plot(log(tr.perf),'b', 'LineWidth', 2)

% plot(log(tr.vperf),'g', 'LineWidth', 2)

plot(log(tr.tperf),'r', 'LineWidth', 2)

Thank you for formally accepting my answer

Greg

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MAPMINMAX can be inappropriate only if there are outliers.

Use MAPSTD to detect outliers that can be removed or modified.

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: NARX Tapped Delay Line

Consider this STATIC APPROXIMATOR to the OL (Open Loop) NARXNET:

 clear all, close all,clc, plt=0
[  INPUT TARGET  ] = simplenarx_dataset;
input  = cell2mat(INPUT);
target = cell2mat(TARGET);
[ I N ] = size(input)  % [ 1 100 ]

[ O N ] = size(target) % [ 1 100 ]
plt = plt+1, figure(plt), hold on
plot( input,  'k', 'LineWidth', 2 )
plot( target, 'b', 'LineWidth', 2 )
 net0 = narxnet(1:2,1:2,10); % default OL time-series configuration
net = fitnet(10);          % static OL approximator
x   = [ input(1:end-2);input(2:end-1); ...
         target(1:end-2); target(2:end-1)];
t   = target(3:end);
rng(4151941)
[ net tr y e ] = train(net,x,t );
NMSE = mse(e)/var(t',1) %  3.1731e-07
Rsq     = 1 -  NMSE     % 1
 plt = plt+1, figure(plt), hold on
plot( 1:N-2, x, 'k', 'LineWidth',2)
plot( 3:N,   t, 'b', 'LineWidth',2)
plot( 3:N,   y, 'ro', 'LineWidth',2)
legend('INPUT', 'TARGET','OUTPUT')

Hope this helps.

Thank you for formally accepting my answer

Greg

PS: It seems to me that a STEPWISE QUASI-STATIC APPROXIMATOR to the CL (Closed Loop) NARXNET should be possible by using the function ADAPT (or even TRAIN?) in a loop that uses y(n-2:n-1) to predict y(n).

Best Answer

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