Improve Research Reproducibility A Bio-protocol resource
- Home
- Protocols
-
Concise Method
Definition of loss function and weight optimization
Request a Protocol
Ask a
question
Favorite
In the proposed neural network model, the input flows to the output layer in a feedforward fashion. The mean squared error was used as a loss function to train the weights and biases. To prevent overfitting, we applied L2 regularization, so the norm of weights is added to the loss. The Adam optimizer was used for training the network (learning rate 0.005, batch size 256).
Copyright and License information: The Author(s) ©2021
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Do you have any questions about this protocol?
Post your question to gather feedback from the community. We will also invite the authors of this article to respond.
Post a Question0 Q&A
