MATLAB: Understand number of weights of Neural Network

annMATLABneural network

I have a Mx120 validation dataset (A), and a Nx120 training dataset (B).
The results look promising, but I am struggling to understand how the weights relate to the training dataset.
using the following code
A = xlsread('Validation.csv');
B = xlsread('Training.csv');
net = fitnet([]);
% Setup Division of Data for Training, Validation, Testing
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;
% Train the Network
[net, tr] = train(net, B, A);
And the following line to inspect the weights
% View the weights
net.IW{1,1}
I see that the number of weights are N-1 – i.e. the number of variables in the training dataset minus one.
What I would like to be able to do is to understand the relative importance of each of the variables in the training dataset. Is this possible?
Apologies if this has been asked before. I did not manage to find a matching answer, but may very well have missed something. If so, please do point me in the right direction.
Thank you in anticipation.

Best Answer

You are THOROUGHLY CONFUSED!
You do not understand MANY fundamental concepts.
1. [ trainednet, trainingrecord] = train(untrainednet, input, target)
   where
   target = desiredoutput
2. output = trainednet(input);
3. error = target - output;
4. input, target, output and error with length N are all divided into 3 INTERMINGLED parts: TRAINING, VALIDATION AND TESTING with number of points Ntrn, Nval and Ntst ,respectively with
     N = Ntrn + Nval + Ntst
5. a. trn used to calculate weights
   b. val used to stop training when val error 
      CONTINUALLY INCREASES FOR A SPECIFIED NUMBER 
      OF EPOCHS (eg, 5)
   c. tst used to obtain an unbiased 
      (i.e., nondesign) error estimate
Hope this helps.
Thank you for formally accepting my answer
Greg
Related Question