How to add more than one hidden layer?
17 visualizaciones (últimos 30 días)
Mostrar comentarios más antiguos
Pratibha
el 1 de Abr. de 2015
Comentada: Marco Pizzoli
el 8 de Jun. de 2021
I need to use feedforwardnet to classify the images and also have train the NN in 3 levels.
Is it possible to add 3 hidden layers to feedforwardnet?
0 comentarios
Respuesta aceptada
Vinod Sudheesh
el 1 de Abr. de 2015
Yes, it is possible to create a "feedforward neural network" with three hidden layers using the "feedforwardnet" function. This can be achieved by passing a vector of hidden layer sizes as the argument to the "feedforwardnet" function.
>> net=feedforwardnet([10 11 12]);
>> view(net);
6 comentarios
Greg Heath
el 17 de Feb. de 2019
A linear function does not need a hidden layer.
A nonlinear function needs no more than one.
However some nonlinear functions are more conveniently represented by two or more hidden layers.
There is an inherent degree of approximation for bounded piecewise continuous functions. Trying to force a closer fit by adding higher order terms (e.g., adding additional hidden nodes )often leads to instability.
You can test the stability of different designs with a different no. of hidden nodes. by comparing their performance as increasing levels of noise are added to the input.
Hope this helps.
Thank you for formally accepting my answer
Greg
Marco Pizzoli
el 8 de Jun. de 2021
Hi Greg,
I am very curious about your observation on the minimum number of necessary hidden nodes. In this regard I have a question: what do you mean by target vs input plot? Because, I can imagine finding the local maxima of the time series of the target or input (taken separately), but not on the graph that considers them together. I apologize in advance for my stupid question.
Best regards,
Marco
Más respuestas (0)
Ver también
Categorías
Más información sobre Deep Learning Toolbox en Help Center y File Exchange.
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!