Sebastian Raschka • 3/24/2022

TorchMetrics

Read Original

This article provides a tutorial on using the TorchMetrics library for iterative model evaluation in PyTorch. It clarifies the practical difference between the .update() and .forward() methods, using a hands-on example of computing classification accuracy across minibatches to reduce boilerplate code.

0 comments
#metrics #Deep Learning #Model Evaluation
TorchMetrics

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
Using Browser Apis In React Practical Guide
Jivbcoop • 2 votes
2
Better react-hook-form Smart Form Components
Maarten Hus • 2 votes
3
Top picks — 2026 January
Paweł Grzybek • 1 votes
4
In Praise of –dry-run
Henrik Warne • 1 votes
5
Deep Learning is Powerful Because It Makes Hard Things Easy - Reflections 10 Years On
Ferenc Huszár • 1 votes
6
Vibe coding your first iOS app
William Denniss • 1 votes
7
AGI, ASI, A*I – Do we have all we need to get there?
John D. Cook • 1 votes
8
Quoting Thariq Shihipar
Simon Willison • 1 votes
9
Dew Drop – January 15, 2026 (#4583)
Alvin Ashcraft • 1 votes