## What is Activation Maximization?

In a CNN, each Conv layer has several learned template matching filters that maximize their output when a similar template pattern is found in the input image. First Conv layer is easy to interpret; simply visualize the weights as an image. To see what the Conv layer is doing, a simple option is to apply the filter over raw input pixels. Subsequent Conv filters operate over the outputs of previous Conv filters (which indicate the presence or absence of some templates), making them hard to interpret.

The idea behind activation maximization is simple in hindsight - Generate an input image that maximizes the filter output activations. i.e., we compute

and use that estimate to update the input. ActivationMaximization loss simply outputs small values for large filter activations (we are minimizing losses during gradient descent iterations). This allows us to understand what sort of input patterns activate a particular filter. For example, there could be an eye filter that activates for the presence of eye within the input image.

## Usage

There are two APIs exposed to perform activation maximization.

1. visualize_activation: This is the general purpose API for visualizing activations.
2. visualize_activation_with_losses: This is intended for research use-cases where some custom weighted losses can be minimized.

See examples/ for code examples.

### Scenarios

The API is very general purpose and can be used in a wide variety of scenarios. We will list the most common use-cases below:

#### Categorical Output Dense layer visualization

How can we assess whether a network is over/under fitting or generalizing well? Given an input image, a CNN can classify whether it is a cat, bird etc. How can we be sure that it is capturing the correct notion of what it means to be a bird?

One way to answer these questions is to pose the reverse question:

Generate an input image that maximizes the final Dense layer output corresponding to bird class.

This can be done by pointing layer_idx to final Dense layer, and setting filter_indices to the desired output category.

• For multi-class classification, filter_indices can point to a single class. You could point also point it to multiple categories to see what a cat-fish might look like, as an example.
• For multi-label classifier, simply set the appropriate filter_indices.

#### Regression Output Dense layer visualization

Unlike class activation visualizations, for regression outputs, we could visualize input that

• increases
• decreases

the regressed filter_indices output. For example, if you trained an apple counter model, increasing the regression output should correspond to more apples showing up in the input image. Similarly one could decrease the current output. This can be achieved by using grad_modifier option. As the name suggests, it is used to modify the gradient of losses with respect to inputs. By default, ActivationMaximization loss is used to increase the output. By setting grad_modifier='negate' you can negate the gradients, thus causing output values to decrease. gradient_modifiers are very powerful and show up in other visualization APIs as well.

#### Conv filter visualization

By pointing layer_idx to Conv layer, you can visualize what pattern activates a filter. This might help you discover what a filter might be computing. Here, filter_indices refers to the index of the Conv filter within the layer.

backprop_modifiers allow you to modify the backpropagation behavior. For examples, you could tweak backprop to only propagate positive gradients by using backprop_modifier='relu'. This parameter also accepts a function and can be used to implement your crazy research idea :)
• If you get garbage visualization, try setting verbose=True to see various losses during gradient descent iterations. By default, visualize_activation uses TotalVariation and LpNorm regularization to enforce natural image prior. It is very likely that you would see ActivationMaximization Loss bounce back and forth as they are dominated by regularization loss weights. Try setting all weights to zero and gradually try increasing values of total variation weight.
• Regression models usually do not provide enough gradient information to generate meaningful input images. Try seeding the input using seed_input and see if the modifications to the input make sense.