The neural network stops before it starts? (minimum gradient reached ) error
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Hello every body. I am using Neural network in my research graduation project " Pattern Identification ". I have built a custom neural network, and I used the function 'trainlm' .
The Problem is that, once I start the training process, the training window appears and tells me that the minimum gradient is reached and the neural network stops even before it starts !
( The architecture of my NN is complicated, and I'm using three inputs with 13 layers each of them carries different neurons ! )
I wish I can find someone helping me
Thanks in advance !
2 个评论
EanX
2015-9-30
I have a similar problem in designing a NARNET.
Currently best performance was obtained with 15 hidden nodes (1 hidden layer), mapminmax for processing function for inputs (ymin=0, ymax=10), tansig as transfer function for layer 1 and poslin for output layer.
I noticed that if I change output transfer function to the more commonly employed purelin I can avoid "minimum gradient reached" issue, but I used poslin to obtain forecasts always positive.
Can anyone enlighten me on this topic?
Thanks in advance !
Greg Heath
2015-9-30
编辑：Greg Heath
2015-9-30
Do not change defaults until you have obtained the best default result.
Are you looping over 10 or more weight initializations and getting premature MinGrad reached on all of them?
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Greg Heath
2014-5-30
2 个投票
The best way to design a neural net is to first find the simplest architecture that yields a good solution.
My advice is to start with a single hidden layer and find the smallest number of hidden neurons that will yield a solution that cannot be significantly improved by using more neurons.
The best targets for patternnet are unit column vectors with a single unity component. The best training algorithm for that type of target is trainscg.
Hope this helps.
Greg
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