Normalizing data for neural networks
显示 更早的评论
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
采纳的回答
Chandra Kurniawan
2012-1-10
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
4 个评论
Greg Heath
2012-1-11
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
2015-7-4
i want to find out my output in original form how can i find it ? please help me
Greg Heath
2015-7-10
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
murat tuna
2019-3-22
Sorry, where is NEWSGROUP?
更多回答（4 个）
Greg Heath
2012-1-11
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
4 个评论
John
2012-1-11
owr
2012-1-11
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
2012-1-12
Greg Heath
2012-1-13
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
Greg Heath
2012-1-14
1 个投票
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
4 个评论
John
2012-1-16
fehmi zarzoum
2017-5-24
hi, Undefined function or variable 'pmin'.
Greg Heath
2017-5-31
Yeah, should be minp.
electronx engr
2017-11-4
plz can u help me in this that after training with normalized data, how can I get the network (using gensim command) that works on unnormalized input, since I have created and trained the network using normalized input and output?
Imran Babar
2013-5-8
0 个投票
mu_input=mean(trainingInput); std_input=std(trainingInput); trainingInput=(trainingInput(:,:)-mu_input(:,1))/std_input(:,1);
I hope this will serve your purpose
2 个评论
Greg Heath
2013-5-10
Not valid for matrix inputs
Abul Fujail
2013-12-12
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.
类别
在 帮助中心 和 File Exchange 中查找有关 Deep Learning Toolbox 的更多信息
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!
Translated by 
