MATLAB: How can i define the output range in Neural Network

neural networkoutput control

I attached the following script for explanations. But, i'm getting values (negative values) from out of define range some of the times. How can i control it?

% Outputs Range net.outputs {1}.range = [ 0 0.25; 0 0.25; 0 0.25; 0 0.25; 0 0.25; 0 0.25; 0 0.25; 0 0.25; 0 0.25; 0 0.25; 0 6; 0 0.15]

% Train the Network [net,tr] = train(net,inputs,targets);

Best Answer

Change the output transfer function to tansig. Then the default normalized output bounds (-1,1) will be mapped into minmax(target)

Hope this helps.

Thank you for formally accepting my answer

Greg

Related Solutions

MATLAB: How to avoid negative values in the output of a feedforward net

1. If your targets are bounded for a physical or mathematical reason the transfer functions logsig or tansig can be used. For documentation use the help and doc commands. For example,

 help logsig
 doc logsig

2. What sizes are your input and target matrices?

 [ I N ] = size(inputs) 
 [ O N ] = size(outputs)

3. If predictions are correlated to past inputs and predictions, the best model could be a time-series network.

 help narxnet
 doc narxnet

4. If you stay with the static model, use the regression function FITNET which calls FEEDFORWARDNET but includes the helpful regression plot. If you want to know the default values just type, WITHOUT SEMICOLON

 net = fitnet

5. It is confusing when you assign default values to parameters. For examples look at the code associated with help and doc examples.

The only parameters that I tend to change are net.divideFcn, net.trainParam.goal and net.trainParam.min_grad. Increasing the latter two can reduce training time without introducing significant errors.

6. Any good real world design is going to require looking at tens or hundreds of candidates. My policy is to first use defaults, then change parameters to improve performance.

Except for the three I mentioned above, it usually just comes down to finding out the minimum number of hidden nodes that are sufficient and designing multiple candidates that only differ by initial random weights.

Hope this helps

Thank you for formally accepting my answer

Greg

MATLAB: Equation that compute a Neural Network in Matlab

% VERY SORRY FOR THE ROTTEN FORMATING. THIS EDITOR SUCKS.

Equation that compute a Neural Network in Matlab

Gianvito asked about 23 hours ago

I created a neural network matlab. This is the script:

      load dati.mat;
      inputs=dati(:,1:8)';
      targets=dati(:,9)';

% SEE DOCUMENTATION (DOC) FOR DEFAULTS OF PATTERNNET

% SINCE THIS IS PATTERN RECOGNITION WITH ONE OUTPUT,

% TARGETS SHOULD BE UNIPOLAR BINARY {0,1}, WITH NO NEED

% FOR TRANSFORMATIONS. THE MOST APPROPRIATE OUTPUT

% ACTIVATION IS LOGSIG. HOWEVER, PURELIN CAN BE USED

% EVEN THOUGH OUTPUTS ARE NOT RESTRICTED TO (0,1).

% TO SEE THE SIZE AND SCALE OF THE DATA

==> [ I N ] = size(inputs) % [ 8 612 ]

==> [ O N ] = size(targets) % [ 1 612 ]

==> minmaxin = minmax(inputs)

==> minmaxtarg = minmax(targets)

% TO ESTIMATE A REASONABLE VALUE FOR H

==> Ntrn = round(0.7*N) % 428

==> Neq = Ntrn*O % 428 No. of training equations

% Nw = O+(I+O+1)*H = 101 No. of unknown weights % For good weight estimates when training to convergence % require Neq >= Nw but desire Neq >> Nw % % Hub = floor((Neq-O)/(I+O+1)) % 42 Upper bound for H

      hiddenLayerSize = 10; % H

% H =10 ==> Neq/Nw = 428/101 is only a factor of 4. However, % not worried because using Early Stopping (aka Stopped Training) % with a validation set.

      net = patternnet(hiddenLayerSize);

%WHY ASSUME THE DEFAULT OF TRAINLM THEN CHANGE LATER? %DON'T KNOW FINAL I AND O YET BECAUSE OF CONSTANT %ROWS AND PCA DIMENSIONALITY REDUCTION

       net.inputs{1}.processFcns = {'removeconstantrows','mapminmax', 'mapstd','processpca'};
%XX: USE MAPSTD, BUT NOT MAPMINMAX WITH PROCESSPCA
% WARNING: PCA MAY NOT BE USEFUL FOR SOME PATTERN 
% RECOGNITION PROBLEMS
      net.outputs{2}.processFcns = {'removeconstantrows','mapminmax','mapstd','processpca'}; 
%XX: WHY USE PCA FOR 'ANY' OUTPUT??
%XX: WHY TRANSFORM 'THIS' OUTPUT? ISN'T IT UNIPOLAR BINARY?
     net = struct(net);
%XX: WHY IS THIS NECESSARY?... DELETE?
      net.inputs{1}.processParams{2}.ymin = 0;
%XX: DELETE, USE MAPSTD FOR INPUT
      net.outputs{2}.processParams{2}.ymin = 0;
      net.inputs{1}.processParams{4}.maxfrac = 0.02;
      net.outputs{2}.processParams{4}.maxfrac = 0.02;
%XX: DON'T USE PCA FOR OUTPUTS
      net = network(net); 
%XX INVALID (SEE DOC). SHOULD DEFINE NET TOPOLOGY.
      net.divideFcn = 'divideind';  
      net.divideMode = 'sample';  % Divide up every sample
      net.divideParam.trainInd = 1:428;
      net.divideParam.valInd = 429:520;
      net.divideParam.testInd = 521:612;
      net.trainFcn = 'trainscg';  % Scaled conjugate gradient backpropagation
% DEFINE IN CALL OF PATTERNET
      net.performFcn = 'mse';  % Mean squared error
% UNNECESSARY. DEFAULT
      net.plotFcns = {'plotperform','plottrainstate','ploterrhist', 'plotregression', 
'plotconfusion', 'plotroc'};
      net=init(net);
% AREN'T WEIGHTS ALREADY INITIALIZED?
      net.trainParam.max_fail=20;
% PROBABLY TOO HIGH
     [net,tr] = train(net,inputs,targets);      
      outputs = net(inputs);
      errors = gsubtract(targets,outputs);
      performance = perform(net,targets,outputs)
% WHAT VALUES DO YOU GET?

Now I want to save the weights and biases of the network and write the equation. I had saved the weights and biases:

      W1=net.IW{1,1};
      W2=net.LW{2,1};
      b1=net.b{1,1};
      b2=net.b{2,1};

So, I've done the data preprocessing and I wrote the following equation

      max_range=0;
%  WHY BOTHER SPECIFYING DEFAULTS?
      [y,ps]=removeconstantrows(input, max_range); 
     ymin=0;
      ymax=1;
      [y,ps2]=mapminmax(y,ymin,ymax); 
%  DO NOT USE y ON BOTH SIDES OF AN EQUATION
%XX  DELETE MINMAX TRANSFORMATION
      ymean=0;
      ystd=1;
% WHY BOTHER SPECIFYING DEFAULTS?
      y=mapstd(x,ymean,ystd);
%XX    x IS NOT DEFINED
      maxfrac=0.02;
      y=processpca(y,maxfrac);      
      in=y';      
     uscita = tansig(W2*(tansig(W1*in+b1))+b2);

% WHY NOT LOGSIG FOR OUTPUT LAYER??

But with the same input input=[1:8] I get different results.

why? What's wrong? Help me please! It's important!

I use Matlab R2010B

% HARD TO TELL. THIS CODE CANNOT RUN WITHOUT DEFINING x.

% WHAT ABOUT THE OUTPUT NORMALIZATION AND & TRANSFORMATION ?

Hope this helps.

Greg

Best Answer

Related Solutions

MATLAB: How to avoid negative values in the output of a feedforward net

MATLAB: Equation that compute a Neural Network in Matlab

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