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custom regression deep learning

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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.

Accepted Answer

Iuliu Ardelean
Iuliu Ardelean on 10 Feb 2021
Hey, when you call dlX=dlarray(inputs2), you should specify which dimensions are Spatial/Batch/Channel etc.
e.g.
X = randn(3,5);
dlX = dlarray(X,'SC')
SC are space and channel in this case.
Read more here:
  6 Comments
jaehong kim
jaehong kim on 16 Feb 2021
yes!
Thank you all the time for your hard work.

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