Hi there,
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).
This is my status and problem. Now I need suggestions –
In what all ways I can improve the performance of the network.
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 😛
Thanks for your time….!
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