Using PyTorch to implement a deep learning model - Emmanuel Katto

[emmanuelkattouganda]

Hi all, I am Emmanuel Katto from Uganda. I'm trying to use PyTorch to implement a deep learning model for image classification, but I'm having trouble with model interpretation and feature importance. I've tried using the torchvision library, but I'm not sure how to extract meaningful insights from my model's predictions.

Can anyone provide guidance on how to interpret the results of a PyTorch model, including any recommendations for techniques or tools for feature importance analysis or visualizing model output? Are there any resources or tutorials that can help me improve my understanding of deep learning model interpretability?

Thanks!
Emmanuel Katto

[Larz60+]

You'll find a bunch of tutorials here.
Perhaps one of these will help.

[AdamHensley]

Great to see you're diving into deep learning with PyTorch! For interpreting your model and understanding feature importance, check out tools like Grad-CAM and Guided Backpropagation. These help visualize which parts of your images your model focuses on. The torchcam library can assist with this. Also, explore Captum, which is designed for model interpretability in PyTorch. It offers algorithms like Integrated Gradients, DeepLIFT, and SHAP values. The documentation and tutorials are beneficial. For more resources, the official PyTorch tutorials and Andrew Ng's Deep Learning Specialization on Coursera are excellent for building a solid foundation.