Hi~
I ran into an error while doing custom regression.
In short, it is a neural network that receives 8 features as input and outputs 1 output.
My code and error are as follows.
clear,clc,close alldata=readmatrix('train.csv');inputs=data(:,1:8);targets=data(:,9);input2=transpose(inputs);target2=transpose(targets);inputs2=normalize(input2,2,'range');layer = mseRegressionLayer('mse');layers = [ featureInputLayer(8,'Name','in') fullyConnectedLayer(1,'Name','fc2') ];lgraph=layerGraph(layers);dlnet=dlnetwork(lgraph);iteration = 1;averageGrad = [];averageSqGrad = [];learnRate = 0.005;gradDecay = 0.75;sqGradDecay = 0.95;dlX=dlarray(inputs2);for it=1:5000 iteration = iteration + 1; [gradient,loss]=dlfeval(@modelGradients,dlnet,dlX,target2); [dlX,averageGrad,averageSqGrad] = adamupdate(dlX,gradient,averageGrad,averageSqGrad,iteration,learnRate,gradDecay,sqGradDecay); if it>=4500 & mod(it,10)==0 disp(it); endendfunction [gradient,loss]=modelGradients(dlnet,dlx,t) out=forward(dlnet,dlx); gradient=dlgradient(loss,dlx); loss=mean((out-t).^2);endError using dlfeval (line 43)First input argument must be a formatted dlarray.Error in untitled3 (line 31) [gradient,loss]=dlfeval(@modelGradients,dlnet,dlX,target2); Thank you for reading my question, and I hope someone who has insight will write an answer.
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