How to resolve out of memory error?

I've a problem that occurs when I train the neural network using the code

                        net = train(net,x,t);

I have got the error "Out of memory. Type HELP MEMORY for your options." Do anybody have idea how to solve this kind of problems?

Thanks

[Greg Heath]

size(x), size(t), No.of hidden nodes, sample code,...?

[Minnu]

 size(x) is-  830x27
 size(t) is- 12x27
 no of hidden nodes:20

Answer 1

[Greg Heath]

Unfortunately, your problem is not well posed.

You have 27 I/O pairs. The 27 inputs define, at most, a 26-dim subspace. However, you have 830 "components". You need to transform to a smaller space with at most 26 dimensions.

The simplest way is to use a principal components transformation. You can do this separately or use the PROCESSPCA input processing option of fitnet (regression) or patternnet (classification).

The number of equations that you have is

Neq = prod(size(t)) = 12*17 = 204

With H hidden nodes, the number of unknowns(weights) to estimate are

Nw = (I+1)*H+(H+1)*O

where { I N ] = size(x) and [O N ] = size(t)

If you reduce I to 26,

Nw = O + (I+O+1)*H = 12+39*H

and if you wish to have more equations than unknowns,

H <= Hub = -1 + ceil( Neq-O)/(I+O+1) ) = 4

In addition, if you replace 26 with 830, you see the problem.

Fortunately, there are ways to obtain stable, accurate solutions using a variety of methods. The NNTBX offers validation stopping and objective function regularization. However, with only 27 cases, I don't think you should use any for validation. Therefore,

1. Reduce your input dimensions below 27

help/doc processpca

2. Use the regularization training function 'trainbr'.

help/doc trainbr

3. Search for the minimum H that gives you satisfactory results. 10 different weight initialization trials for each value of H = 1:10 may be sufficient.

4. Once you have determined Hopt, you can obtained less biased performance estimates on nontraining data by using 10 repetitions of 9-fold cross-validation with 24 training cases and 3 testing cases. Although there are 27*26*25 = 17,550 ways that you can choose the 3-member test set, it is hard to believe that 10 repetitions of 9-fold cross-validation won't be more than sufficient.

Hope this helps.

Thank you for formally accepting my answer

Greg

Answer 2

[Jan]

As you will find as answer for dozens or euiqvalent questions in this forum, when you search for them:

• Install more RAM
• Close other applications
• Install even more RAM
• clear variables, which are not used anymore
• Use a 64 bit version of OS and Matlab, such that it is useful to:
• Install much more RAM
• Increase the virtual memory, when it does not matter if the program need 100 times longer.

[Minnu]

When i run my code on laptop ,pc same error occurs .how to resolve it

[Jan]

@Minnu: If I wasn't clear enought already:

 Installing more RAM solves the problem, that the installed RAM is exhausted.