MATLAB: Validation error in neural network

Deep Learning Toolboxmlpneural networktrainvalidation error

Dear friends I have tried to train a mlp using newff and train functions, but after few stages of training, the validation error stops the training procedure, so I wanna ask for any solution or alternative to prevent the validation error
regards

Best Answer

% 0. One hidden layer is sufficient provided there are enough hidden nodes.
% 1. Use t for target and y for output
% 2. For regression use the current FITNET instead of the obsolete NEWFF.
% 3. As indicated below, Validation Stopping is useful for preventing highly biased training data performance from influencing the design of a net which is ultimately created for use on NONDESIGN data. This is especially important when the number of training equations Ntrneq = Ntrn*O is not sufficiently greater than the number of unknown weights Nw = (I+1)*H+(H+1)*O.
% 4. Data divisions
DATA        = DESIGN + NONDESIGN
DESIGN      = TRAINING + VALIDATION  
NONDESIGN   = TEST + UNAVAILABLE
NONTRAINING = VALIDATION  + NONDESIGN
% 5. Use the DESIGN TRAINING data to estimate weights. Using the same data to estimate performance can result in highly optimistic biased estimates.
% 6. Use the DESIGN VALIDATION data to obtain less biased performance estimates to
     a. Prevent worse performance on NONDESIGN data.
     b. Rank the performances of multiple designs
     c. Choose the multiple designs used to estimate summary performance statistics
% 7. Assume the UNBIASED NONDESIGN TEST data performance is representative of the performance on UNAVAILABLE data.
% 8. Evaluate performance via the summary statistics of the UNBIASED performance on NONDESIGN (TEST + UNAVAILABLE) data chosen in 6c.
 close all, clear all, clc, plt=0
x = -5:.05:5;
t = (2*sin(x).*cos(3*x)+cos(10*x)).*sin(x);
[ I N ]   = size(x)         % [ 1 201]

[ O N ] = size(t)           % [ 1 201]
 Ntst = round(0.15*N)       % 30

 Nval = Ntst                % 30
 Ntrn = N-Nval-Ntst         % 141
 plt = plt+1, figure(plt)
 plot( x, t, 'LineWidth', 2 )
%17 local minima / 18 local maxima => 36 hidden nodes
 H    = 36
net   = newff( x, t, H );
MSE00 = var(t',1)                     % 1.0041 Reference MSE
rng(4151941)                          % For duplicating the design
[ net tr y e ] = train (net, x, t );
hold on 
plot( x, y, 'r', 'LineWidth', 2 )
NMSE  = mse(e)/MSE00                  % 2.5414e-3
R2        = 1-NMSE                    % 0.99746   Rsquared (See Wikipedia)
tr          = tr                      % No semicolon
stopcrit = tr.stop                    % Validation stop
R2trn    = 1-tr.best_perf/MSE00       % 0.99889
R2val    = 1-tr.best_vperf/MSE000     % 0.99542
R2tst    = 1-tr.best_tperf/MSE00      % 0.99277
% Using MSEtrn00 instead of MSE00 shouldn't make much difference (Use tr.trainInd to check if you don't believe me)
Hope this helps.
Thank you for formally accepting my answer
Greg
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