Training a deep neural net
James Prestegard
on 3 Jan 2024
Latest activity Reply by James Prestegard
on 12 Jan 2024
I have been developing a neural net to extract a set of generative parameters from an image of a 2-D NMR spectrum. I use a pair of convolution layers each followed by a fullyconnected layer; the pair are joined by an addtion layer and that fed to a regression layer. This trains fine, but answers are sub-optimal. I woudl like to add a fully connected layer between the addtion layer and regression, but training using default training scripts simply won't converge. Any suggestions? Maybe I can start with the pre-trained weights for the convolution layers, but I don't know how to do this.
JHP
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Are you using the Deep Learning Toolbox? I use the Deep Network Designer App for these situations.
Thanks for the response.
I am using the DeepLearning Toolbox, but have not been using the designer app. I build the network as a layer graph and then train using the command:
net=trainNetwork(input,target,lgraph,options)
The training cycle runs; the RMSE just doesn't converge as it does without the additional fully connected layer.
I'll look into the designer app to see if it makes any difference.
