MATLAB: Abnormal outputs in Neural Networks Blind tests (new tests after the net is trained).

neural networksnn toolbox

The data divisions in my ANN model is trn 60 – val 20 – tst 20 . All of my input and target values are in between 0 – 1 (numeric values). The problem that i am facing with is >> when the network is trained, i insert new data set (five new input variables but samples number are 35) to estimate the output variable, as it was already trained with the target variable. Unfortunately the output becomes exactly same for all the 35 samples. It must not happen, there is something wrong that i might have done. Could you please light some shed on these. I am using Matlab R2012b version. Matlab codes are given as follows:
Inp_var1 = xlsread('Training data.xlsx','B2:B165241');
Inp_var2 = xlsread('Training data.xlsx','D2:D165241');
Inp_var3 = xlsread('Training data.xlsx','C2:C165241');
Inp_var4 = xlsread('Training data.xlsx','E2:E165241');
Inp_var5 = xlsread('Training data.xlsx','F2:F165241');
Tar_var1 = xlsread('Training data.xlsx','K2:K165241');
Input(1,:) = Inp_var1;
Input(2,:) = Inp_var2;
Input(3,:) = Inp_var3;
Input(4,:) = Inp_var4; 
Input(5,:) = Inp_var5;
Target(1,:) = Tar_var1;
net = feedforwardnet; 
net = configure(net,Input,Target);
net.layers{1}.transferFcn = 'tansig';
net.layers{1}.initFcn = 'initnw';
net.layers{2}.transferFcn = 'purelin';
net.layers{2}.initFcn = 'initnw';
net = init(net);
net.IW{1,1}
net.b{1}
net.adaptFcn = 'adaptwb';
net.inputWeights{1,1}.learnFcn = 'learnp';
net.biases{1}.learnFcn = 'learnp';
inputs = Input;
targets = Target;
hiddenLayerSize = 3; % number of hidden neurons
net = fitnet(hiddenLayerSize);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax','mapstd'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax','mapstd'};
net.divideFcn = 'dividerand';
net.divideMode = 'sample';  
net.divideParam.trainRatio = 60/100;
net.divideParam.valRatio = 20/100;
net.divideParam.testRatio = 20/100;
net.trainFcn = 'trainlm'; 
net.performFcn = 'mse';
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotregression', 'plotfit'};
net.efficiency.memoryReduction = 1;
[net,tr] = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);
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)
net.trainParam.epochs;
net.trainParam.time;
net.trainParam.goal;
net.trainParam.min_grad;
net.trainParam.mu_max;
net.trainParam.max_fail;
net.trainParam.show;
end

Best Answer

Most of your code is useless. It is equivalent to
 Input(1,:) = xlsread('Training data.xlsx','B2:B165241');
Input(2,:)  = xlsread('Training data.xlsx','D2:D165241');
Input(3,:) = xlsread('Training data.xlsx','C2:C165241');
Input(4,:) = xlsread('Training data.xlsx','E2:E165241');
Input(5,:) = xlsread('Training data.xlsx','F2:F165241');
Target(1,:) = xlsread('Training data.xlsx','K2:K165241');
 hiddenLayerSize = 3; % number of hidden neurons
net = fitnet(hiddenLayerSize);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax','mapstd'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax','mapstd'};
net.divideParam.trainRatio = 60/100;
net.divideParam.valRatio = 20/100;
net.divideParam.testRatio = 20/100;
 [net,tr] = train(net,inputs,targets);
outputs = net(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs);
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)
Make sure the input summary statistics (mean/covariance-matrix) for the original and new data are close enough to be assumed to be drawn from the same probability distribution.
Thank you for formally accepting my answer
Greg
Related Question