cifar_with_attributions

Explaining Custom CNN CIFAR-10 Classification Using the Attributions Explainer

This notebook demonstrates how to use the attributions explainer API to explain the CIFAR-10 dataset image classification example using a Custom PyTorch CNN.

TorchVision_CIFAR_Interpret.ipynb performs the following steps:

1. Import dependencies
2. Load the CIFAR-10 dataset from TorchVision hub
3. Design the PyTorch CNN model
4. Train the CNN
5. Visualize the custom CNN classifications using saliency(), integratedgradients(), deeplift(), smoothgrad() and featureablation()

Running the notebook

To run TorchVision_CIFAR_Interpret.ipynb, install the following dependencies:

1. Intel® Explainable AI

References