Search All of the Math Forum:
Views expressed in these public forums are not endorsed by
Drexel University or The Math Forum.



Re: Neural Network  Pattern Recognition....!
Posted:
Jan 4, 2013 6:22 PM


"Greg Heath" <heath@alumni.brown.edu> wrote in message <kc6bvc$9mu$1@newscl01ah.mathworks.com>... > "Sriram " <sriramr@live.in> wrote in message > <kc3b9p$q3k$1@newscl01ah.mathworks.com>... > > I used the neural network > toolbox ( nprtool ) for classifying my detected objects into either of 3 > classes. I used 14 parameters (image moments) for all the 3 classes of > input for training. As of now, I was able to collect only few data for > each classes say around > > > Class 1  17 * 14 > > > Class 2  11 * 14 > > >Class 3  48 * 14 > > > Total Inputclass  14*76 && Outputclass  3 *76 > > > I arranged these data in column wise to feed into nprtool box.... > I used the default toolbox functions like  hidden as 10 , training  70% > (54 samples) , validation  15% (11 samples), testing  15% (11 samples) > and started the training. > > > Total number of Iterations was  20 > > >Performance (MSE) was  0.0731 > > > Gradient was 0.0617 > > > Validation checks was  6 > > > Once the net has been created, I tried to use some data in "sim(net,input)" to > > >check my networks performance. For certain inputs from the trained data set, the > > >network's >performance was fine but for many it was very bad. (unexpected results). > > Neither nonquantitative terms like "fine" and "bad" nor the quantitative > result MSE = 0.0731 helps much without references. With target matrix > > t = [ repmat( [1 0 0 ]',1,17) repmat( [0 1 0 ]',1,11) repmat( [0 0 1 ]',1,48) ]; > > a reasonable reference is MSE00 = mean(var(t',1)) = 0.17671 > so that the normalized MSE is NMSE = MSE/MSE00 = 0.41368 and the > resulting R^2 statistic ( see "coefficientofdetermination in Wikipedia) > is R2 = 1MSE = 0.58632 which is not necessarily good. > > Even with a reference MSE, a classifier should be graded on error rate; not MSE. > > What is needed is NMSE and PCTerr for each class (13) and each data > split (trn/val/tst) > > > >This is my status and problem. Now I need suggestions  > > > In what all ways I can improve the performance of the network. > > 1. Duplicate copies of class1 and class2 so that you have 48 vectors of > each class (N = 3*48) = 142
144 > 2. Standardize the I = 14 input variables to zeromean/unitvariance (zscore or > mapstd) > 3. The columns of the target matrix should be columns of the O=3dimensional > unit matrix eye(3). For example, t = repmat( eye(3), 1, 48 ). The corresponding > classindices are obtained from classind = vec2ind(t); > 4. For a 0.7/0.15/0.15 data split Ntrn = Nround(0.15*N) = 100 resulting in > Ntrneq = Ntrn*O = 300 training equations
Ntrn = 122, Ntrneq = 366
> 5. For an IHO network topology, there will be Nw =(I+1)*H+(H+1)*O = 3 + 18*H > unknown weights to estimate with Ntrneq equations. This results in Ndof = NtrneqNw > estimation degrees of freedom. It is desirable that Ntrneq >> Nw or H << 297/18 = > 16.5.
It is desirable that Ntrneq > Nw or H < floor(363/18) = 20
> However, larger values of H can be tolerated by using validation stopping > or regularization using trainbr. > 6. The normalizing factor for the MSE should be the average variance of the target > matrix variables > > MSE00 = mean(var(t',1)) % Biased (divided by N*O) or > MSE00a = mean(var(t',0)) % "a"djusted to be unbiased (divided by (N1)*O) > For the output y, the biased and unbiased training MSEs are
MSE = sse(ty)/Ntrneq, NMSE = MSE/MSE00 MSEa = sse(ty)/Ndof, NMSEa = MSEa/MSE00a
resulting in
R2 = 1NMSE R2a= 1NMSEa > > 7. The recommended training function for classification is 'trainscg'. A reasonable
training goal is R2a > 0.99 or MSEgoal = 0.01*(Ndof/Ntrneq)*MSE00a > 8. Try a number of values for H and run 10 or more random weight initialization trials > for each value of H.. > > >> 1. Increasing the inputclass database will improve but suggest me something other than that. > > > 2. Increasing the number of hidden layers from 10 to many doesn't seem to make much difference :(.... > > > Through the documentation of Neural Network toolbox  I found the default > nprtool in Matlab take cares of input and output processing (ex: mapminmax) and > also it uses trainscg function for training.... Should I use some other > efficient training algorithms such as trainlm ? But here how can I decide > logically (not by trying all algorithms) which training function will be > useful for me.? > > > I have just started to work on neural network after > exploring some basics...Kindly help me on improving it  making me a > transition from advance beginner to expert :P > > > Thanks for yourtime....! > > Hope this helps > > Greg



