custom mulitiple output regression

i just want mulitiple output regression custom code.

i can't find that...

i think that fullyconnectedlayer's outputsize is key for multiple output regression.

Is it correct?

ex..

layers = [

featureInputLayer(2,'Name','in')

fullyConnectedLayer(64,'Name','fc1')

tanhLayer('Name','tanh1')

fullyConnectedLayer(32,'Name','fc2')

tanhLayer('Name','tanh2')

fullyConnectedLayer(16,'Name','fc3')

tanhLayer('Name','tanh3')

fullyConnectedLayer(8,'Name','fc4')

tanhLayer('Name','tanh4')

fullyConnectedLayer(6,'Name','fc5')

];

6==outputsize

thank you for reading my question!

Answer 1

[Raynier Suresh]

Hi, For multiple regression output you can also create networks with multiple output layers. For more information on this you can refer the below link.

https://www.mathworks.com/help/deeplearning/ug/train-network-with-multiple-outputs.html#responsive_offcanvas

[jaehong kim]

Thank you for the answer. I'll take a good reference.