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Re: Neural Network - Pattern Recognition....!
Posted:
Jan 4, 2013 5:47 AM
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"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 "coefficient-of-determination in Wikipedia)
is R2 = 1-MSE = 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 (1-3) 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
2. Standardize the I = 14 input variables to zero-mean/unit-variance (zscore or
mapstd)
3. The columns of the target matrix should be columns of the O=3-dimensional
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 = N-round(0.15*N) = 100 resulting in
Ntrneq = Ntrn*O = 300 training equations
5. For an I-H-O 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 = Ntrneq-Nw
estimation degrees of freedom. It is desirable that Ntrneq >> Nw or H << 297/18 =
16.5. 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) or
MSE00a = mean(var(t',0)) % "a"djusted to be unbiased (divided by N-1)
whereas the biased and unbiased training MSEs are
MSE = sse(t-y)/Ntrneq
MSEa = sse(t-y)/Ndof
7. The recommended training function for classification is 'trainscg'. A reasonable
training goal is MSEgoal = 0.01*(Ndof/Ntreq)*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
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