custom regression deep learning

Question

0 投票

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 all
data=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);
    end
end
function [gradient,loss]=modelGradients(dlnet,dlx,t)
    out=forward(dlnet,dlx);
    gradient=dlgradient(loss,dlx);
    loss=mean((out-t).^2);
end
Error 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.