Could anyone help me on what basis the number of hidden layers are chosen for deep neural network.

1 visualización (últimos 30 días)
Let me explain in brief.
I have generated the code for deep neural network for regression purpose using numerical data to predict the formation of clusters.
when I run the code, for four hidden layers i can get the lowest value of mean square error as compared to 2 hidden layers,3 hidden layers,5 hidden layers, and 6 hidden layers.
So,I can say four hidden layers are optimal in my case.
But I would like to know is there any other reason other the mean square error to justify why four hidden layers are optimal.
Also let me know, for an image based on pixel, I can find low level features, high level features and so on.
But for numerical data what represent low level and high level features.
Could anyone please clarify me.

Respuestas (1)

Matt J
Matt J el 3 de Jul. de 2021
Editada: Matt J el 3 de Jul. de 2021
But I would like to know is there any other reason other the mean square error to justify why four hidden layers are optimal.
No, if you change the loss function or any other thing about your network architecture (e.g., number of neurons per layer), you could very well find you get a different optimal number of layers.
But for numerical data what represent low level and high level features.
In general, you won't know that in advance. The main purpose of a neural network is for the network to learn the relevant features on its own are during training.
  15 comentarios
Matt J
Matt J el 5 de Jul. de 2021
jaah navi's comment moved here:
As mentioned, the training is not converging for the 3-layer case and 2-layer case is there any reason other than the convergence state.
Matt J
Matt J el 5 de Jul. de 2021
Editada: Matt J el 5 de Jul. de 2021
Reason for what? If the results don't represent converged networks, there is no basis on which to compare them.

Iniciar sesión para comentar.

Categorías

Más información sobre Sequence and Numeric Feature Data Workflows 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!

Translated by