Could you please help me in Artificial neural network - supervised learning?
Artificial neural network I have a data set and I like to know the best NN topology to use (# of hidden layers and # of nodes – currently I am using [30 50 30]). I have about 1000 samples with 20 input variables and one output. I learned using the following code; but my test(with new data set-never seen by ANN) didn’t give me desirable output. Could your please varify my method? %load data inputs_bn, targets_bn; %Normalize - Do i have to normalize the data? [inputs,ps] = mapminmax(inputs_bn); [targets,ts] = mapminmax(targets_bn); HL=[30 50 30]; %inputs %targets % Create a Fitting Network hiddenLayerSize = HL; net=fitnet(hiddenLayerSize,'traingdx'); % Is this used for predictions? % Choose Input and Output Pre/Post-Processing Functions % For a list of all processing functions type: help nnprocess net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'}; % Setup Division of Data for Training, Validation, Testing % For a list of all data division functions type: help nndivide net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'sample'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; net.trainFcn = 'trainlm'; % Levenberg-Marquardt net.trainParam.min_grad=1e-8; % Choose a Performance Function %change from %net.performFcn = 'mse'; % Mean squared error %change from %change to net.performFcn='msereg'; net.performParam.ratio=0.5; %change to % Choose Plot Functions % For a list of all plot functions type: help nnplot net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression', 'plotfit'}; % Train the Network [net,tr] = train(net,inputs,targets,'useParallel','yes','showResources','yes'); %trainr gave bad results % Test the Network outputs11 = net(inputs); outputs=mapminmax('reverse',outputs11,ts); errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs) % Recalculate Training, Validation and Test Performance trainTargets = targets .* tr.trainMask{1}; valTargets = targets .* tr.valMask{1}; testTargets = targets .* tr.testMask{1}; trainPerformance = perform(net,trainTargets,outputs) valPerformance = perform(net,valTargets,outputs) testPerformance = perform(net,testTargets,outputs) % View the Network %view(net) % Plots % Uncomment these lines to enable various plots. %figure, plotperform(tr) %figure, plottrainstate(tr) %figure(1), plotfit(net,inputs,targets) %figure, plotregression(targets,outputs) figure(111), ploterrhist(errors) %%%%%%%% %%%%Load Test DATA % Target_output outputs_Test = sim(net,input_Test); outputs_Test=mapminmax('reverse',ooutputs_Test,ts); errors = outputs_Test - Target_output; plot(errors)
Prashant Kumar answered .
2025-11-20
a. That many inputs b. More than 1 hidden layer c. Anywhere near that many hidden nodes.
a. Add squares and/or cross-products to the linear (in coefficients) model b. Use functions STEPWISE and/or STEPWISEFIT
3. PLEASE
a. Do not post commands that assign default values.
b. Include results of applying your code to an accessible data set so
that we know we are on the same page.
c. Instead of posting your huge dataset, just pick one of the MATLAB
example sets
help nndatasets
doc nndatasets
Nw = (I+1)*H+(H+1)*O
to be much less than the number of training equations
Ntrneq = round(0.7*N*O) % default approx.
A necessary condition is
H <= Hub = floor((Ntrneq-O)/(I + O +1))
However H << Hub is preferable.
With I = 20, O = 1, N = 1000
Ntrneq = Ntrn = 700 Hub = 45
7. My tutorials will explain how to perform a double loop search for
a. No. of hidden nodes b. Initial RNG state (reproducible initial weights & datadivision).
8. For regression, search on subsets of
greg fitnet tutorial Ntrials
Hope this helps.