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

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.

Answer 1

[Faiz Gouri]

You can refer the code below in order to convert table into Neural Network function input-

load('carsmall')
cars = table(MPG,Weight,Model_Year);
cars.Model_Year = categorical(cars.Model_Year);
categories(cars.Model_Year)

% now cars has Model_Year categorical(nominal) variable

% encode categorical variable
D = dummyvar(cars.Model_Year);
D = array2table(D);
D.Properties

% 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);

Answer 2

[Salma Hassan]

imds=' D:\ ';

imds=imageDatastore(imds,'IncludeSubfolders',true,'FileExtensions','.png','LabelSource','foldernames'); [trainingimages,valDigitData]=splitEachLabel(imds,0.8,'randomize');

 t=table(trainingimages.Files,trainingimages.Labels);