how to fix constant iteration in neural networks

when i am trying to train my neural network using levenberg marquardt algorithm it shows different iteration at each times how do i fix my neural network with constant iteration period

[Star Strider]

Do you mean epochs or training time?

[Thirunavukkarasu]

epochs

[Shreeja Shetty]

I am currently facing a similar issue as mentioned above. Can someone please provide a solution.

Answer 1

[Greg Heath]

When you train your net again, the random number generator is in a different state. Therefore you will have a different trn/val/tst split AND a different set of initial weights. The training will stop according to one of several stopping rules including

 1. performance goal achieved
 2. maximum epochs reached
 3. minimum gradient achieved
 4. maximum mu reached
 5. validation stop (validation performance reaches a local maximum)

 [ net tr y e ] = train(net,x,t)  % e=t-y
 stopcriterion  = tr.stop

or, if you are training in a double for loop

 stopcriteria{i,j} = tr.stop

This is great because all are chosen to optimize your performance. That is why every time I try a new candidate for H=number of hidden nodes, I design at least Ntrials = 10 nets. So, if I am considering 10 different values for H, I will have 100 designs which I summarize in 3 10 by10 matrices for training, validation and test performance.

The best net is determined from the nontraining validation set performance (smaller values of H are preferred) and an unbiased estimate of unseen nontraining data performance is obtained from the test set performance.

Hope this helps.

Thank you for officially choosing my answer

Greg

Answer 2

[Greg Heath]

If you train multiple nets in a loop you can duplicate previous runs by keeping track of the state of the random number generator. That is why I always specify an initial random number state, before the outer loop. For examples, search on

 greg rng(0)

or

 greg rng('default')

Hope this helps.

Thank you for formally accepting my answer

Greg

[Greg Heath]

What is this code supposed to do?

[Thirunavukkarasu]

where i insert this command i have code like this

inputs = s'; targets = out'; hiddenLayerSize = 2; net = fitnet(hiddenLayerSize); net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'}; net.divideFcn = 'dividerand'; net.divideMode = 'sample'; net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; net.trainFcn = 'trainlm'; net.performFcn = 'mse'; net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression', 'plotfit'}; [net,tr] = train(net,inputs,targets); outputs = net(inputs); errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs) trainTargets = targets .* tr.trainMask{1}; valTargets = targets .* tr.valMask{1}; testTargets = targets .* tr.testMask{1}; trainPerformance = perform(net,trainTargets,outputs) valPerformance = perform(net,valTargets,outputs) testPerformance = perform(net,testTargets,outputs) view(net)

[Greg Heath]

Initialize the rng before the training command

 [ net tr ] = train(...)

which randomizes the data division and initial weights.

[Parul Singh]

rng default- net gives different outputs each time it is run

rng (variable number) - number of iterations remain the same at 1000

We want to vary the number of iterations to achieve best output and then for a constant number of iterations, we want the network to get the same output each time it is nrun.

Please help.

[Greg Heath]

Given what I have learned in 37 years of NN design, what you want to do is illogical. Please reread what I have written.

Greg

Answer 3

[Cesare Trematore]

I do not know if I fully agree. I was running a pattern recognition neural network with the trainbr option. The train perfomance kept improving up to 1000 epochs, but after about 200 epochs the test perfomance started worsening. In this cases would be useful to have the option to stop the training after a prefixed number of epochs.

[Greg Heath]

That option is available.

However, why in the world are you using trainbr for pattern recognition?

What happens when you use patternet with all defaults except number of hidden nodes and initial RNG state?

Search the NEWSGROUP and ANSWERS with

 greg patternnet

Hope this helps.

Greg