how to rewrite this for feedforward neural network for with 52 inputs and 2 output that predict emission rate not for digits, where i get an error of In an assignment A(:) = B, the number of elements in A and B must be the same. Error in line36

sweep=[3,5:5:50]; %parameter values to test
scores=zeros(length(sweep),1); %pre-allcation
models=cell(length(sweep),1); %pre-allcation
load('inputs.mat');   
load('outputs.mat'); 
load('newinputs.mat'); 
load('newoutputs.mat'); 
in=inputs;               % loads inputs into variable 'in' 
t=outputs;               % loads outputs into variable 't' 
in_test=newinputs;             % loads test inputs into variable 'in_test' 
t_test=newoutput;             % loads test inputs into variable 't_test' 
%training function:
net.trainFcn = 'trainlm';
%transfer functions:
net.layers{1}.transferFcn = 'tansig';
net.layers{2}.transferFcn = 'purelin';
net.trainParam.epochs = 100000;
for i=1:length(sweep)
    hiddenlayersize=sweep(i); %number of hidden layer neurons
    net=patternnet(hiddenlayersize);
    net.divideFcn = 'dividerand';  % Divide data randomly
    net.divideMode = 'sample';   % Divide up every sample
    net.divideParam.trainRatio=0.7;
    net.divideParam.valRatio=0.15;
    net.divideParam.testRatio=0.15;
    %training the network
    net = train(net,in,t); 
    %simulating the outputs 
    y=sim(net,in);
    %store the trained network
    models{i}=net; 
    %prediction to test the network
    pre=net(in_test);
    %prediction labels

    scores(i)=sum(t_test==pre)/length(t_test); %accuracy
        
end
%plot accuracy versus number of neorons in the hidden layer
figure
plot(sweep,scores,'.-')
xlabel('number of hidden neurons')
ylabel('accuracy')
title('number of hidden neurons vs.accuracy')

Answer 1

[Greg Heath]

Replace the " == " in

scores(i)=sum(t_test==pre)/length(t_test); %accuracy

with a minus sign

WHOOPS !!!

I misread the text. I was thinking of an error measure to minimize of the form

errorscore(i) = sum( abs( t_test - pre )/ length(t_test) )

Hope this helps.

*Thank you for formally accepting my answer*

Greg

[Mary Abdu]

still not work i think the problem is with the dimention of scores (i) that it is dimention is 11x1

Answer 2

[Reff]

hi,

what is means of paramater?

1. sweep=[3,5:5:50]; %parameter values to test
2. scores=zeros(length(sweep),1); %pre-allcation
3. models=cell(length(sweep),1); %pre-allcation