using a trained ANN
hi all. I have trained a pattern recognition neural network and have gotten good results (87%). Although, i'm still confused as to how I actually use it in real life. For example, every time i run my network i have to train it and sometimes it takes more than a few tries to get to 87% accuracy. At times the accuracy is as bad as 26%. So my question is, how do i make sure my network remembers what it has learned? I want to save my networks memory when i get 87% accuracy. How do i do that? Second, i was wondering if i could use this network to find the class of an unknown image which i select at runtime. I've used indexing method to separate the training, validation and test data so that the network tests only the images i want it to. Thanks in advance. Have a nice day :)
John Williams answered .
2025-11-20
% FITNET REUSE EXAMPLE: % Train in workspace % Save copy to directory % Clear original from workspace % Load copy from directory to workspace % Use copy on "new" data
% If it exists, delete netg from the directory
delete netg.mat
% Clear the workspace and plot before designing netg
close all, clear all, clc [ x,t ] = simplefit_dataset; [ I N ] = size(x) %[1 94] [ O N ] = size(t) %[1 94] MSE00 = mean(var(t',1))% 8.3378 subplot(2,1,1), hold on plot(x,'k'), plot(t,'b') subplot(2,1,2), hold on plot(x,t,'b')
% NOTE: t has 4 local extrema
netg = fitnet(4); rng(4151941) [ netg tr y e ] = train(netg,x,t); % y = netg(x); e = t-y; stopcriteria = tr.stop % Validation stop NMSE = mse(e)/MSE00 % 5.8958e-3 R2 = 1-NMSE % 0.9941 plot(x,y,'r')
' netg is in workspace'
whos netg
'netg is not in directory'
dir netg dir netg.mat
'Save copy of netg to directory. Becomes netg.mat'
save( 'netg') dir netg.mat
'Next clear original netg from workspace'
whos netg clear netg whos netg
'Then load copy of netg from directory to workspace'
load netg whos netg
'Delete copy of netg from directory'
dir netg.mat delete netg.mat dir netg.mat
'Apply netg copy in workspace to "new" data'
ylr = netg(fliplr(x)); diffy = minmax(ylr-fliplr(y)) % [ 0 0 ]