Deep learning regression network improvements?
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Giacomo Notaro
le 16 Mar 2020
Commenté : Giacomo Notaro
le 19 Mar 2020
Hi all.
I'm trying to train a deep learning model giving me as responses the curves I attached.
The skeleton of the simple model is attached, too.
How could I improve the network to obtain better results?
I'm wondering why the model is not able to reach the same order of magnitude of the training data (the attached images represents only few of the similar curves used for the training).
I tried increasing or reducing the size of the single layers, I tried to add more layers with a smaller size, but the result is still the same.
The input sequences are a representation of the initial conditions and wind velocity for each curve and it is recognizing the shape of the curves but not their magnitudes.
Thank you in advance.
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Mahesh Taparia
le 19 Mar 2020
Modifié(e) : Mahesh Taparia
le 19 Mar 2020
Hi
By looking at your network design, it seem that you are having input data with dimension of 4 and trying to regress the output. You are taking the number of hidden nodes as 1000/2000 from 4 as input and converting back to 1 from 2000 nodes, which is not recomended. You can go with something like 4->8->16->32->16->8->1 (it deepend upon number of hidden layers you put). Try with different optimization techniques like adam/ sgdm, batch size, learning rate, number of hidden layers/nodes, input data normalization etc. Hope it will help.
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Mahesh Taparia
le 19 Mar 2020
Yeah try different optimization techniques and it was some form of 4->8->16->32->16->8->1.
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