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

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mmenvo
mmenvo 2014-2-23
编辑： Greg Heath 2014-2-26
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

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采纳的回答

Greg Heath
Greg Heath 2014-2-24
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
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mmenvo
mmenvo 2014-2-26
Many thanks, Greg. I have understood your clarifications on point 1,2,3 and 4.
I would be happy if you kindly suggests the following:
5. What is your recommendation on ANN data division for a very large dataset? [i.e.Original training data samples are over 165240. I also have different dataset containing samples larger than 600000]. In my case i have 5 input neurons, and 1 output neuron.
which one would you recommend (tr-val-tst)>> i) 70-15-15, ii) 60-20-20, iii) 80-10-10, iv) 70-20-10, or v) 80-20-00
Greg Heath
Greg Heath 2014-2-26
编辑：Greg Heath 2014-2-26
With N this large you do not need val and test
net.divide.Fcn = 'dividetrain'; %100/0/0
and try to minimize H, the number of hidden nodes.
Hope this helps.

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