MSE result is too high

I have a 122x499 dataset, consisting of -1, 0, and 1 value. I made a neural network using "nntool" which uses Feedforward backprop, 2 hidden layer, the first layer having 100 hidden neuron and the second as output layer. I then trained the network using these parameters:

rng('default')
network1.divideFcn = 'dividerand';
network1.divideParam.trainRatio = 0.7;
network1.divideParam.valRatio = 0.15;
network1.divideParam.testRatio = 0.15;
network1.trainParam.epochs = 10000;
network1.trainParam.lr = 0.01;
network1.trainParam.mc = 0.9;

% Train the Network
[net,tr] = train(network1,input,output);
% Test the Network
y = net(input);
e = gsubtract(output,y);
mse = perform(net,output,y)
accuracy = 1-(0.5*mean(abs(e./(abs(output)+abs(y)))))

Now, for some reason I get MSE value of 1.4188.. As far as I know MSE only goes up to 1 right? The same thing happens when I use 200 neuron, MSE value went up to 1.5594.. But when I increased the neuron to 500, MSE goes down to 0.5852.. Did I do something wrong here? Is my MSE equation wrong? Please help!

Answer 1

[Greg Heath]

 1. GUESS: fitnet ?
 2. Unnecessary assignments of default values to 
    divideFcn and ratios. 
 3. Erroneous use of "output" instead of "target"
 4. Inadvisable normalization. Normalize using the 
    mse from the simple case where the target 
    is constant and the corresponding mse is the 
    variance:

    e    = target - output;
    NMSE = mse(e)/mean(var(target',1))

% NMSE = mse(e)/var(target)   for 1-dim

which yields

      Rsquare = 1 - NMSE % R^2

See Wikipedia for an extensive discussion of Rsquare.

Hope this helps.

Thank you for formally accepting my answer

Greg

[NeverPerfecT]

Thanks for the answer Greg. Just a few uncertainties I'd like to ask..

So in this case Rsquared would be the accuracy of the network and NMSE is the correct MSE equation right?