How can I add one more hidden layer to ANN wizard?

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Bees
Bees 2013 年 11 月 22 日
コメント済み: Greg Heath 2013 年 12 月 5 日
I am using ANN wizard (nnstart) to run neural networks for fitting (prediction) purposes. According to the results, it appears adding one more hidden layer would produce a better predictability. However, the wizard doesn't have the capacity to add more hidden layers.
Could anyone please advise what I need to do to run a 3 layer ANN (2 hidden and 1 output) using the wizard?
Thanks

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Greg Heath
Greg Heath 2013 年 11 月 23 日
A second hidden layer will not improve performance if your single hidden layer model is optimized.
size(input) = ? size(target) = ?
Type of network = fitnet?
No. of Hidden nodes= ?
NMSE = mse(target-output)/mean(var(target',1)) = ?
What is your NMSEgoal ? <= 1/100 ?
  2 件のコメント
Bees
Bees 2013 年 11 月 29 日
Thank you very much for your prompt advice.
I have just started using Matlab for my project. I'm practising at the moment to get familiar with ANN on Matlab. I had seen in the literature that some people have used two-layer networks and some one-layer. So, I tried to check how I could use a two-layer if necessary.
I will soon be using my data and will provide you with full answers to get your valuable advice.
Greg Heath
Greg Heath 2013 年 11 月 29 日
Before you do that try looking at some of my posted code
greg fitnet % etc
HTH
Greg

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その他の回答 (1 件)

Bruno Pop-Stefanov
Bruno Pop-Stefanov 2013 年 11 月 22 日
I don't think there is a way to change the number of hidden layers using the wizard. Only the number of neurons in the hidden layer can be changed.
If you are okay not using the GUI, you can use network to create a customized neural network. This lets you specify the number of layers and customize every parameter of your neural network. See Classify Patterns with a Neural Network for details on using training and fitting command-line functions.
  1 件のコメント
Greg Heath
Greg Heath 2013 年 12 月 5 日
It is not necessary to design a custom net to use multiple hidden layers. For example
net = fitnet([ 5 5 ]);
view(net)

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