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I am using a simple feedforward neural network in MATLAB to predict the output for inputs in the range [1e-5, 0.3]. (These are the activations of another network.) I am using a sigmoid function for the hidden layer, and a linear function for the output layer. Input units are 6, hidden units are 4, and the output unit consists of one neuron. The outputs range between [58, 1696]. I normalized the outputs too and turned off the mapminmax function in order to avoid over-normalizing! Weirdly, it creates negative outputs. Could it be because of the input range? I would appreciate if anyone could tell me what is happening here. Any thoughts?

First I need to make an update : I changed the number of hidden neurons to 10. Now one time it gives me negative outputs the other time positive. I cannot find the answer except relating it to the random initialization of the weights.

Here is the excerpt of my code :

clear all;
load features;load labeldata; 

net=feedforwardnet(10);  
IPF={'fixunknowns','remconstantrows'} ; OPF={'remconstantrows'};
net.trainParam.lr=0.01;
net.trainParam.max_fail=10;
NN = train(net,features,y); %y is normalized value of labeldata so it is in range[0,1]
wb = getwb(NN);
net=NN;

%% TEST THE NET (NOT WITH NEW TEST DATA BUT WITH THE TRAINING DATA, SO WE EXPECT GOOD RESULTS)
[pred_learnedFeatures]=net(features);
% scaled_out=pred_learnedFeatures*(max(labeldata)-min(labeldata))+min(labeldata);
fprintf('MSE w/o scaling %f%\n',sum(( pred_learnedFeatures- y).^2)/size(labeldata,2));
fprintf('\n MSE w scaling %f%\n',sum(( scaled_out-labeldata).^2)/size(labeldata,2));
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  • $\begingroup$ Could you post any code where you think the problem might exist? $\endgroup$ – user27886 Jul 20 '14 at 5:48
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One reason that this could happen is that the network hasn't converged.

Another reason could be that this particular network is not the best architecture to solve this problem.

But a direct solution to this problem would be to use an activation function in the final layer that is restricted to be in [0,1] such as the logistic function -- this will never produce negative outputs. Moreover, since you've scaled your target to be in $[0,1]$ as well, the predictions will always be in the same interval as the target.

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