custom regression deep learning
6 次查看(过去 30 天)
显示 更早的评论
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.
0 个评论
采纳的回答
Iuliu Ardelean
2021-2-10
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 个评论
Iuliu Ardelean
2021-2-15
Hey I've read the question and am not sure why this happens. I will try finding what is wrong tomorrow
更多回答(0 个)
另请参阅
类别
在 Help Center 和 File Exchange 中查找有关 Sequence and Numeric Feature Data Workflows 的更多信息
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!