Can the "input data normalization" of "trainNetwork" be done separatedly?
23 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
Jaime Almonacid-Caballer
le 11 Sep 2020
Commenté : Jaime Almonacid-Caballer
le 16 Sep 2020
Hi,
I am begining with Convolutional Nural Networks in Matlab following the available examples.
I have prepared the input data and parameters. When I have run the training of the networks (trainNetworks) it has began with the 'input data normalization' (during more than 2 hours). Once it has been done, when it should have began the iterations, Matlab has failed (Gpu out of memory).
Would any way to have the normalization done before the training so that I could get the errors (logical errors while I am learning) without expending the previous 2 hours each time?
Thanks a lot,
Jaime
0 commentaires
Réponse acceptée
Madhav Thakker
le 14 Sep 2020
Hi Jaime,
I understand that you want to stop the inbuilt data normalization. You can do so by creating your own input data layer and setting normalization to none. I was able to disable normalization in https://www.mathworks.com/help/deeplearning/ug/create-simple-deep-learning-network-for-classification.html by calling
imageInputLayer([28 28 1], 'Normalization', 'none')
instead of
imageInputLayer([28 28 1])
when defining the network.
Hope this helps.
Plus de réponses (0)
Voir également
Catégories
En savoir plus sur Image Data Workflows dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!