how to turn matlab table into appropriate input for neural net package

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Richard Palmer
Richard Palmer on 23 Feb 2017
Edited: Salma Hassan on 14 Jan 2018
I have a matlab table (T) with numeric and nominal variables. Other matlab documentation shows a "dummytable" function that turns the nominal variables in T into a table with nominal variables changed to dummy variables and leaves the numeric variables "as is". However, I cannot figure how to get this table into a form that can be accessed and read by the neural network input functions. Surely, there must be a straightforward way to do this. I just do not see it.

Answers (2)

Faiz Gouri
Faiz Gouri on 1 Mar 2017
You can refer the code below in order to convert table into Neural Network function input-
cars = table(MPG,Weight,Model_Year);
cars.Model_Year = categorical(cars.Model_Year);
% now cars has Model_Year categorical(nominal) variable
% encode categorical variable
D = dummyvar(cars.Model_Year);
D = array2table(D);
% add new variable to cars
cars = [cars ,D];
cars.Model_Year =[];
% convert table to matrix as input to train function is R-by-Q matrix and
% U-by-Q matrix
inputs = table2array(cars(:,2:5))'; % R-by-Q
targets = cars.MPG'; % U-by-Q matrix
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize);
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
[net,tr] = train(net,inputs,targets);

Salma Hassan
Salma Hassan on 14 Jan 2018
Edited: Salma Hassan on 14 Jan 2018
imds=' D:\ ';
imds=imageDatastore(imds,'IncludeSubfolders',true,'FileExtensions','.png','LabelSource','foldernames'); [trainingimages,valDigitData]=splitEachLabel(imds,0.8,'randomize');

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