Asked by John
on 10 Jan 2012

Hi,

I've read that it is good practice to normalize data before training a neural network.

There are different ways of normalizing data.

Does the data have to me normalized between 0 and 1? or can it be done using the standardize function - which won't necessarily give you numbers between 0 and 1 and could give you negative numbers.

Many thanks

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Answer by Chandra Kurniawan
on 10 Jan 2012

Accepted answer

Hi,

I've heard that the artificial neural network training data must be normalized before the training process.

I have a code that can normalize your data into spesific range that you want.

p = [4 4 3 3 4; 2 1 2 1 1; 2 2 2 4 2];

a = min(p(:)); b = max(p(:)); ra = 0.9; rb = 0.1; pa = (((ra-rb) * (p - a)) / (b - a)) + rb;

Let say you want to normalize **p** into 0.1 to 0.9.

**p** is your data.

**ra** is 0.9 and **rb** is 0.1.

Then your normalized data is **pa**

Greg Heath
on 11 Jan 2012

Demos in the comp.a.neural-nets FAQ indicate that better precision is obtained when the input data is relatively balanced about 0 AND TANSIG (instead of LOGSIG) activation functions are used in hidden layers.

Hope this helps.

Greg

Kaushal Raval
on 4 Jul 2015

i want to find out my output in original form how can i find it ? please help me

Greg Heath
on 10 Jul 2015

If you use the standard programs e.g., FITNET, PATTERNNET, TIMEDELAYNET, NARNET & NARXNET,

All of the normalization and de-normalization is done automatically (==>DONWORRIBOUTIT).

All you have to do is run the example programs in, e.g.,

help fitnet doc fitnet

If you need additional sample data

help nndatasets doc nndatasets

For more detailed examples search in the NEWSGROUP and ANSWERS. For example

NEWSGROUP 2014-15 all-time tutorial 58 2575 tutorial neural 16 127 tutorial neural greg 15 58

Hope this helps.

Greg

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Answer by Greg Heath
on 11 Jan 2012

The best combination to use for a MLP (e.g., NEWFF) with one or more hidden layers is

1. TANSIG hidden layer activation functions

2. EITHER standardization (zero-mean/unit-variance: doc MAPSTD)

OR [ -1 1 ] normalization ( [min,max] => [ -1, 1 ] ): doc MAPMINMAX)

Convincing demonstrations are available in the comp.ai.neural-nets FAQ.

For classification among c classes, using columns of the c-dimensional unit matrix eye(c) as targets guarantees that the outputs can be interpreted as valid approximatations to input conditional posterior probabilities. For that reason, the commonly used normalization to [0.1 0.9] is not recommended.

WARNING: NEWFF automatically uses the MINMAX normalization as a default. Standardization must be explicitly specified.

Hope this helps.

Greg

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owr
on 11 Jan 2012

I dont have access to the Neural Network Toolbox anymore, but if I recall correctly you should be able to generate code from the nprtool GUI (last tab maybe?). You can use this code to do your work without the GUI, customize it as need be, and also learn from it to gain a deeper understanding.

What I think Greg is referring to above is the fact that the function "newff" (a quick function to initialize a network) uses the built in normalization (see toolbox function mapminmax). If you want to change this, you'll have to make some custom changes. I dont recall if the nprtool uses newff - this can be verified by generating and viewing the code.

This is all from memory as I dont have access to the toolbox anymore - so take my comments as general guidelines, not as absolute.

Good luck.

John
on 12 Jan 2012

Thank you

Greg Heath
on 13 Jan 2012

Standardization means zero-mean/unit-variance.

My preferences:

1. TANSIG in hidden layers

2. Standardize reals and mixtures of reals and binary.

3. {-1,1} for binary and reals that have bounds imposed by math or physics.

Hope this helps.

Greg

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Answer by Greg Heath
on 14 Jan 2012

In general, if you decide to standardize or normalize, each ROW is treated SEPARATELY.

If you do this, either use MAPSTD, MAPMNMX, or the following:

[I N] = size(p)

%STANDARDIZATION

meanp = repmat(mean(p,2),1,N);

stdp = repmat(std(p,0,2),1,N);

pstd = (p-meanp)./stdp ;

%NORMALIZATION

minp = repmat(min(p,[],2),1,N);

maxp = repmat(max(p,[],2),1,N);

pn = minpn +(maxpn-minpn).*(p-minp)./(maxp-pmin);

Hope this helps

Greg

Answer by Sarillee
on 25 Mar 2013

y=(x-min(x))/(max(x)-min(x))

try this...

x is input....

y is the output...

Answer by Imran Babar
on 8 May 2013

mu_input=mean(trainingInput); std_input=std(trainingInput); trainingInput=(trainingInput(:,:)-mu_input(:,1))/std_input(:,1);

I hope this will serve your purpose

Greg Heath
on 10 May 2013

Not valid for matrix inputs

Abul Fujail
on 12 Dec 2013

in case of matrix data, the min and max value corresponds to a column or the whole dataset. E.g. i have 5 input columns of data, in this case whether i should choose min/max for each column and do the normalization or min/max form all over the column and calculate.

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