CNN Explainers

In the cats vs dogs classification task we trained a model benefiting from data augmentation that has achieved a certain level of accuracy without overfitting the data. Your task here is to provide the implementation of explainer algorithms to understand the model’s decision-making process. After you watch the video, insert in the cells elow your code below that implement two methods:

1. Integrated Gradients
2. Grad-CAM

It is strongly advised (unless you are already have expertise in Keras/TF) to implement the assignment with Pytorch and Captum. You are free to use the high-level APIs of the framework of your choice.

Your notebook should include markdown cells that describes the explainer method in a tutorial fashion (so that anyone that knows just how CNNs work should be able to understand) and the results of each explainer algorithm.

In [3]:

# insert here your code