NEURAL NETWORK reproducibility of results using neural networks without initfcn = 'rands'
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I don't understand why each time I train my network I obtain different results. I create neural network with newff function.
net = newff(input,target,3);
i set the init funciton equal to 'initzero' as below
net.inputweights{1,1}.initfcn = 'initzero';
net.layerWeights{1,2}.initFcn = 'initzero';
net.biases{1}.initFcn = 'initzero';
net.biases{2}.initFcn = 'initzero';
then I initialize the network and train it
net = init(net);
net = train(net,input,target);
And yet each run gives me different results ! How is that possible ? Where is the random hidden ?
PS: I'm using 'trainbr' as trainFctn
net.trainFcn = 'trainbr';
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Greg Heath
2012-10-27
The answer is 'dividerand' randomizes the order of the input/target pairs.
Hope this helps.
Thank you for formally accepting my answer.
Greg
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更多回答(4 个)
faramarz sa
2013-10-22
编辑:faramarz sa
2013-10-22
Different Matlab Neural networks toolbox results is because of two reasons: 1-random data division 2-random weight initialization
For different data division problem use function "divideblock" or "divideint" instead of "dividerand" like this:
net.dividefcn='divideblock;
net.divideparam.trainratio=.7;
net.divideparam.valratio=.15;
net.divideparam.testratio=.15;
For random weight initialization problem, It seems (I'm not sure) all Matlab initialization functions ("initzero", "initlay”, "initwb”, “initnw”) are almost random. So you should force this functions produce similar results per call.
RandStream.setGlobalStream (RandStream ('mrg32k3a','Seed', 1234));
And then use one of them:
net.initFcn='initlay';
net.layers{i}.initFcn='initnw';
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Greg Heath
2011-11-26
I don't know.
Avoid the issue by intializing rand before calling newff. Then just accept the resulting default initnw weights automatically provided by newff.
Hope this helps (at least you will get reproducible results!).
Greg
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Greg Heath
2011-12-3
Try printing out the weights just before the call to TRAIN to see if they are different. Don't forget to initialize RAND before calling NEWFF.
Hope this helps.
Greg
Greg Heath
2011-12-5
Weight space contains jillions of local minima. Therefore I never expect to get a good solution the first time around. That is why I usually use a double loop over (outer) number of hidden nodes and (inner) multiple random weight initializations (Typically for i=1:10; for j=1:10; ...).
Sometimes I have to repeat this several times. Try searching the newsgroup using
heath newff Ntrials
Hope this helps.
Greg
P.S. How many good solutions of the XOR problem do you get for H = 1:5; Ntrials = 1:10?
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