Optimize Models With Parallel Coordinates
In this article, we take a look at how to optimize models with parallel coordinates using Weights & Biases in just a couple of lines of code.
Created on March 13|Last edited on January 31
Comment
The ability to visualize and compare the performance of models using parallel coordinates charts is a powerful capability.
In this article, we take a look at how Weights & Biases can help you log and visualize your experiments in just a few lines of code.
Visualize Model Performance
With Weights & Biases, it's possible to easily visualize how hyperparameters like dropout and learning rate affect the output metric accuracy.
The chart below is called a parallel coordinates chart and is a powerful way to explore the results of your model training:
- Each line represents a single model.
- Hover over lines with high accuracy to see which hyperparameter choices performed the best.
- Visualize a high-level overview of the different models you've tried.
Get started logging experiments to visualize with just a few lines of code in your model script.
Parallel Coordinates Chart
Try It in 5 Minutes
Add just a couple of lines of code to your model training script to get this and more powerful visualizations. If you'd like to try the PyTorch model I used in this project, take a look at my code →
It's fast and free to get started comparing and visualizing your models.
See the docs →
Add a comment
Iterate on AI agents and models faster. Try Weights & Biases today.