Normalizing data for neural networks

63 次查看（过去 30 天）
John
John 2012-1-10
评论： murat tuna 2019-3-22
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

请先登录，再回答此问题。

采纳的回答

Chandra Kurniawan
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
显示 2更早的评论
Greg Heath
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

请先登录，再进行评论。

更多回答（4 个）

Greg Heath
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
显示 2更早的评论
Greg Heath
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

请先登录，再进行评论。

Greg Heath
Greg Heath 2012-1-14
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
显示 2更早的评论
electronx engr
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?

请先登录，再进行评论。

Sarillee
Sarillee 2013-3-25
y=(x-min(x))/(max(x)-min(x))
try this...
x is input....
y is the output...
Imran Babar
Imran Babar 2013-5-8
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
Abul Fujail
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.

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Image Data Workflows 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by