explainable-cnn 0.0.4

📦 Flexible visualization package for generatinglayer-wise explanations for CNNs.

Explainable CNNs

Its a common notion that a Deep Learning model is considered as a black box. Working towards this problem, this project focusses on making the internal working of the Neural layers more transparent. In order to do so, Explainable CNNs is a plug n play component that visualizes the layers based on on their gradients and builds different representations including Saliency Map, GuidedBackPropagation, GradCam and GuidedgradCam.

Architechture

Usage

Install the package

pip install explainable-cnn

To create visualizations, create an instance of CNNExplainer

from explainable_cnn import CNNExplainer

x_cnn = CNNExplainer(...)

The following method calls returns different visualizations

saliency_map = x_cnn.get_saliency_map(...)

grad_cam = x_cnn.get_grad_cam(...)

guided_grad_cam = x_cnn.get_guided_grad_cam(...)

To see full list of arguments and their usage for all methods, please refer to this file

You may want to look at example usage in the example notebook.

Output

Below is a comparison of the visualization generated between GradCam and GuidedGradCam

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Ashutosh Hathidara 💻 🎨 🔬

Lalit Pandey 🔬

This project follows the all-contributors specification. Contributions of any kind welcome!

References

• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• Grad CAM demonstrations in PyTorch