Yuxin Wu • 10/8/2022

Not Every Model Has a Separate "Loss Function"

Read Original

This technical article critiques the common deep learning design pattern of forcing a separate 'loss function' abstraction. It argues this separation is not always optimal, leads to code duplication, and limits flexibility, especially for complex models like multi-task networks. The author proposes an alternative design where the model itself can compute the loss, offering a cleaner and more general-purpose system architecture.

0 comments
#Deep Learning #Kera #Model Design

Not Every Model Has a Separate "Loss Function"

Comments

No comments yet

Be the first to share your thoughts!

Browser Extension

Get instant access to AllDevBlogs from your browser

Top of the Week

1
The Beautiful Web
Jens Oliver Meiert • 2 votes
2
When your coding agent doesn’t understand your project, you’ll get junk
Benjamin Cane • 1 votes
3
LLM Use in the Python Source Code
Miguel Grinberg • 1 votes
4
Wagon’s algorithm in Python
John D. Cook • 1 votes
5
An example conversation with Claude Code
Dumm Zeuch • 1 votes