Hi All
I have a code , I am just checking how it works , my input matrice is :
input = [0.0600000000000000 0.00100000000000000 45 0.0508000000000000 0.0127000000000000]
and the target is a 6 by 6 matrix
so using this code bellow , I get the mentioned error : Inputs and targets have different numbers of samples ,
Error in Neural (line 17) , [net,tr] = train(net,xn_tr,yn_tr);
here is the full code :
clc clear clear all load('input.txt') %load input load ('taget.txt') %normalizing data [xn_tr,xs_tr] = mapstd(input); [yn_tr,ys_tr] = mapstd(taget); %%network net=newff(xn_tr,yn_tr,[7 7],{'tansig'},'traingda');%7 hidden tanh layer gradian descent adaptive net.trainParam.epochs =70; net.trainParam.lr = 0.05; net.trainParam.lr_inc = 1.05; %training network net.trainFcn='traingda'; [net,tr] = train(net,xn_tr,yn_tr); %randomizing initial value f weight matrix net = init(net); net.trainParam.show = NaN; u_t=mapstd('apply',x,xs_tr); %simulating output y_hat=sim(net,u_t); %plotting performance plotperform(tr) mse=mse(y-y_hat)
ANSWER
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Here is a simplified example using the NEWFF example in the help and doc documentation. I omitted
- Using an inner for loop over multiple random weight initializations and data divisions. To see those type examples search on greg Ntrials
- Extracting the individual trn/val/tst performances via using the training record tr to obtain the corresponding indices.
% >> help newpr % load simpleclass_dataset % net = newpr(simpleclassInputs,simpleclassTargets,20); % net = train(net,simpleclassInputs,simpleclassTargets); % simpleclassOutputs = net(simpleclassInputs); close all, clear all, clc, plt = 0 [ x, t ] = simpleclass_dataset; [ I N ] = size(x) % [ 2 1000 ] [ O N ] = size(t) % [ 4 1000 ] trueclass = vec2ind(t); class1 = find(trueclass==1); class2 = find(trueclass==2); class3 = find(trueclass==3); class4 = find(trueclass==4); N1 = length(class1) % 243 N2 = length(class2) % 247 N3 = length(class3) % 233 N4 = length(class4) % 277 x1 = x(:,class1); x2 = x(:,class2); x3 = x(:,class3); x4 = x(:,class4); plt = plt + 1
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