What is a Class Activation Map?

Class activation maps or grad-CAM is another way of visualizing attention over input. Instead of using gradients with respect to output (see saliency), grad-CAM uses penultimate (pre Dense layer) Conv layer output. The intuition is to use the nearest Conv layer to utilize spatial information that gets completely lost in Dense layers.

In keras-vis, we use grad-CAM as its considered more general than Class Activation maps.


There are two APIs exposed to visualize grad-CAM and are almost identical to saliency usage.

  1. visualize_cam: This is the general purpose API for visualizing grad-CAM.
  2. visualize_cam_with_losses: This is intended for research use-cases where some custom weighted loss can be used.

The only notable addition is the penultimate_layer_idx parameter. This can be used to specify the pre-layer whose output gradients are used. By default, keras-vis will search for the nearest layer with filters.


See saliency scenarios. Everything is identical expect the added penultimate_layer_idx param.


grad-CAM only works well if the penultimate layer is close to the layer being visualized. This also applies to Conv filter visualizations. You are better off using saliency of this is not the case with your model.