تبدیل عدد به رشته باینری در شبکه عصبی

[zahra.mf]

سلام من یک شبکه عصبی طراحی کردم که لایه هدف (Target) یا خروجی که یه جدول با 6 تا ستون هست و اعدادش مقادیر بین 0 تا 3 هست . میخوام مقاذیر داخل ستون ها رو به یک رشته باینری تبدیل بشه ولی نمیدونم کجا و چجوری باید این کار و بکنم
این کد من هست

clear all;close all;

clc;





%%

load('Data.mat');

 load('Target.mat');





x = Data';

t = Target';













% Create a Fitting Network

hiddenLayerSize = 20;

TF={'compet','purelin'};

net = newff(x,t,hiddenLayerSize,TF);





% Choose Input and Output Pre/Post-Processing Functions

% For a list of all processing functions type: help nnprocess

net.input.processFcns = {'removeconstantrows','mapminmax'};

net.output.processFcns = {'removeconstantrows','mapminmax'};





% Setup Division of Data for Training, Validation, Testing

% For a list of all data division functions type: help nndivide

net.divideFcn = 'dividerand';  % Divide data randomly

net.divideMode = 'sample';  % Divide up every sample

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;









% For help on training function 'trainlm' type: help trainlm

% For a list of all training functions type: help nntrain

net.trainFcn = 'trainlm';  % Levenberg-Marquardt





% Choose a Performance Function

% For a list of all performance functions type: help nnperformance

net.performFcn = 'mse';  % Mean Squared Error





% Choose Plot Functions

% For a list of all plot functions type: help nnplot

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...

    'plotregression', 'plotfit'};

net.trainParam.max_fail=100;

net.trainParam.min_grad=1e-20;

net.trainParam.mu_max=1e+40;

net.trainParam.epochs=10000;





% Train the Network

[net,tr] = train(net,x,t);





% Test the Network

y = net(x);

e = gsubtract(t,y);

performance = perform(net,t,y)





% Recalculate Training, Validation and Test Performance

trainTargets = t .* tr.trainMask{1};

valTargets = t .* tr.valMask{1};

testTargets = t .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,y)

valPerformance = perform(net,valTargets,y)

testPerformance = perform(net,testTargets,y)





% View the Network

view(net)