nitin_ch asked . 2021-07-15

How can I find the right neural network architecture

I am trying to learn how to use the neural network to fit functions. I did read a little bit into the subject but I am still not sure how to find the right architecture (the number of neuron in a hidden layer. I use networks with 1 hidden layer and my training algorithm are 'trainlm' and 'trainbr'. Currently I am aware of 4 problems that can occur:

+ Algorithm reaches a local minimum: the best training performance (tr.best_perf) is too large?

+ Overfitting: the best validation performance (tr.best_vperf) is much larger than the best training performance (tr.best_perf)?

+ Underfitting: the best validation performance (tr.best_vperf), the best training performance (tr.best_perf), the best test performance (tr.best_tperf) are in the similar size but they are still too large.

+ Extrapolating: the best test error (tr.best_tperf) is much larger than the two other ones.

Currently, I wrote a loop that examine networks with 1 neuron to 50 neurons. Each network (e.g. a network with 20 neurons) is trained for 10 times and the one with the lowest training performance (tr.best_perf) is chosen in order to avoid the local minimum. Afterwards, I store tr.best_tperf, tr.best_vperf and tr.best_perf of that network in a array. Finally I compare those 50 networks to each other and take the one with the lowest error, with error = max([tr.best_tperf, tr.best_vperf, tr.best_perf]).

The other way to go would be to train each network (e.g. a network with 20 neurons) for 10 times and choose the lowest error, with error = max([tr.best_tperf, tr.best_vperf, tr.best_perf]). Then I store this error for each network in a vector. Finally, I choose the network with the lowest element of that vector.

Can someone tell me which way is the correct way? I really appreciate any help you can provide.

neural network , levenberg-marquard... , baysian regularisa...

Expert Answer

Prashant Kumar answered . 2024-12-20 18:18:10

Search the NEWSREADER and ANSWERS using

   fitnet Hmin Hmax Ntrials

Minimization of the number of hidden nodes subject to the MSEtrn upper bound

 MSEtrn <= 0.01*mean(var(targettrn',1))

        <= 0.01*var(targettrn,1) for 1-dim

this yields a training subset Rsquaretrn exceeding 0.99.

Many of the posts don't have the training subset subscript trn and/or may have used t instead of target. So, there are probably a jillion variations posted including

 MSEgoal = 0.01*vart1

The best way I have found to obtain relatively unbiased results is to use 2 loops.

 1. Outer loop over # of hidden nodes Hmin:dH:Hmax 
    with Hmax <= Hub, the upper bound for not 
   having more unknown weights, Nw, than training 
    equations Ntrneq. 

 2. Inner loop over Ntrials >= 10 different 
    random distributions of initial weights.

Nets are initially ranked by their validation subset performance. Then unbiased estimates of performance are obtained from the test subset performance.

However, I usually rank the nets by their combined nontraining validation AND test subset performance.

Again, I have jillions of examples posted in the NEWSREADER and ANSWERS. The best search words are probably

        Hmin Hmax Ntrials

Not satisfied with the answer ?? ASK NOW

Recent Matlab Projects

Classification of Covid and Non-Covid Lungs CT-Scan using Deep Learning with MATLAB

Matlab simulation on Wind Energy system

Frequently Asked Questions

MATLAB offers tools for real-time AI applications, including Simulink for modeling and simulation. It can be used for developing algorithms and control systems for autonomous vehicles, robots, and other real-time AI systems.

MATLAB Online™ provides access to MATLAB® from your web browser. With MATLAB Online, your files are stored on MATLAB Drive™ and are available wherever you go. MATLAB Drive Connector synchronizes your files between your computers and MATLAB Online, providing offline access and eliminating the need to manually upload or download files. You can also run your files from the convenience of your smartphone or tablet by connecting to MathWorks® Cloud through the MATLAB Mobile™ app.

Yes, MATLAB provides tools and frameworks for deep learning, including the Deep Learning Toolbox. You can use MATLAB for tasks like building and training neural networks, image classification, and natural language processing.

MATLAB and Python are both popular choices for AI development. MATLAB is known for its ease of use in mathematical computations and its extensive toolbox for AI and machine learning. Python, on the other hand, has a vast ecosystem of libraries like TensorFlow and PyTorch. The choice depends on your preferences and project requirements.

You can find support, discussion forums, and a community of MATLAB users on the MATLAB website, Matlansolutions forums, and other AI-related online communities. Remember that MATLAB's capabilities in AI and machine learning continue to evolve, so staying updated with the latest features and resources is essential for effective AI development using MATLAB.

Without any hesitation the answer to this question is NO. The service we offer is 100% legal, legitimate and won't make you a cheater. Read and discover exactly what an essay writing service is and how when used correctly, is a valuable teaching aid and no more akin to cheating than a tutor's 'model essay' or the many published essay guides available from your local book shop. You should use the work as a reference and should not hand over the exact copy of it.