book18.qmd

# *CNN Interpretation with CAM*  {#sec-CAM}

Now that we know how to build up pretty much anything from scratch, let's use that knowledge to create entirely new (and very useful!) functionality: the *class activation map*. It gives us some insight into why a CNN made the predictions it did.

In the process, we'll learn about one handy feature of PyTorch we haven't seen before, the *hook*, and we'll apply many of the concepts introduced in the rest of the book. If you want to really test out your understanding of the material in this book, after you've finished this chapter, try putting it aside and recreating the ideas here yourself from scratch (no peeking!).

## CAM and Hooks

The class activation map (CAM) was introduced by Bolei Zhou et al. in ["Learning Deep Features for Discriminative Localization"](https://arxiv.org/abs/1512.04150). It uses the output of the last convolutional layer (just before the average pooling layer) together with the predictions to give us a heatmap visualization of why the model made its decision. This is a useful tool for interpretation.

More precisely, at each position of our final convolutional layer, we have as many filters as in the last linear layer. We can therefore compute the dot product of those activations with the final weights to get, for each location on our feature map, the score of the feature that was used to make a decision.

We're going to need a way to get access to the activations inside the model while it's training. In PyTorch this can be done with a *hook*. Hooks are PyTorch's equivalent of fastai's callbacks. However, rather than allowing you to inject code into the training loop like a fastai `Learner` callback, hooks allow you to inject code into the forward and backward calculations themselves. We can attach a hook to any layer of the model, and it will be executed when we compute the outputs (forward hook) or during backpropagation (backward hook). A forward hook is a function that takes three things—a module, its input, and its output—and it can perform any behavior you want. (fastai also provides a handy `HookCallback` that we won't cover here, but take a look at the fastai docs; it makes working with hooks a little easier.)

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This is just a preview of this chapter. The rest of this chapter is not available here, but you read the [source notebook](https://github.com/fastai/fastbook/) which has the same content (but with less nice formatting).