# BaaL cheat sheet¶

In the table below, we have a mapping between common equations and the BaaL API.

## Setup¶

Here are the types for all variables needed.

model : torch.nn.Module
wrapper : baal.ModelWrapper
dataset: torch.utils.data_utils.Dataset
bald = baal.active.heuristics.BALD()
entropy = baal.active.heuristics.Entropy()


We assume that baal.bayesian.dropout.patch_module has been applied to the model.

model = baal.bayesian.dropout.patch_module(model)

BaaL cheat sheet

Description

Equation

BaaL

Bayesian Model Averaging

$$\hat{T} = p(y \mid x, {\cal D})= \int p(y \mid x, \theta)p(\theta \mid D) d\theta$$

wrapper.predict_on_dataset(dataset, batch_size=B, iterations=I, use_cuda=True).mean(-1)

MC-Dropout

$$T = \{p(y\mid x_j, \theta_i)\} \mid x_j \in {\cal D}' ,i \in \{1, \ldots, I\}$$

wrapper.predict_on_dataset(dataset, batch_size=B, iterations=I, use_cuda=True)

BALD

$${\cal I}[y, \theta \mid x, {\cal D}] = {\cal H}[y \mid x, {\cal D}] - {\cal E}_{p(\theta \mid {\cal D})}[{\cal H}[y \mid x, \theta]]$$

bald.get_uncertainties(T)

Entropy

$$\sum_c \hat{T}_c \log(\hat{T}_c)$$

entropy.get_uncertainties(T)

Contributing

If some equations are missing, please open a PR so that we can make this cheat sheet as useful as possible.