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custom multiple output regression

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Hi
i want a way to solve 'custom multiple output regression'
i just want to see a simple example code. I can't find examples because my googling skills aren't good.
I hope that someone who is very generous can leave even a simple example code.
'custom multiple output regression'
layer structure etc...
Thank you for reading my quesion!

Accepted Answer

Iuliu Ardelean
Iuliu Ardelean on 10 Feb 2021
Edited: Iuliu Ardelean on 10 Feb 2021
layers1 = [
imageInputLayer([21 21 1],"Name","imageinput")
convolution2dLayer([3 3],32,"Name","conv_1","Padding","same")
batchNormalizationLayer("Name","batchnorm_1")
leakyReluLayer(0.01,"Name","leakyrelu_1")
convolution2dLayer([3 3],32,"Name","conv_2","Padding","same")
batchNormalizationLayer("Name","batchnorm_2")
leakyReluLayer(0.01,"Name","leakyrelu_2")
convolution2dLayer([3 3],32,"Name","conv_3","Padding","same")
batchNormalizationLayer("Name","batchnorm_3")
leakyReluLayer(0.01,"Name","leakyrelu_3")
fullyConnectedLayer(8,"Name","fc") % <- 8 outputs
regressionLayer("Name","regressionoutput")];
  1 Comment
jaehong kim
jaehong kim on 10 Feb 2021
Thank you for your answer.
However, I want a neural network layer that receives 8 features and outputs 8(or 1) output.
In other words, i want something about DNN. It seems that your answer is CNN.

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