API

The ProbFlow API consists of four primary modules:

• distributions

• parameters

• modules

• models

as well as a module which contains pre-built models for standard tasks:

• applications

and two other modules which allow for customization of the training process:

• callbacks

• data

The distributions module contains classes to instantiate probability distributions, which describe the likelihood of either a parameter or a datapoint taking any given value. Distribution objects are used to represent the predicted probability distribution of the data, and also the parameters’ posteriors and priors.

The parameters module contains classes to instantiate parameters, which are values that characterize the behavior of a model. When fitting a model, we want to find the values of the parameters which best allow the model to explain the data. However, with Bayesian modeling we want not only to find the single best value for each parameter, but a probability distribution which describes how likely any given value of a parameter is to be the best or true value.

The modules module contains the Module abstract base class. Modules are building blocks which can be used to create probabilistic models. They can contain parameters, other information, and even other Modules. They take Tensor(s) as input, perform some computation them, and output a Tensor. A good example of a Module is a neural network layer, because it needs to contain parameters (the weights and biases), and it takes one Tensor as input and outputs a different Tensor. The modules module also contains several specific types of Modules, such as a Dense neural network layer, a BatchNormalization layer and an Embedding layer.

The models module contains abstract base classes for Models. Unlike Modules, Models encapsulate an entire probabilistic model: they take Tensor(s) as input and output a probability distribution. Like Modules, they can contain Parameters and Modules. They can be fit to data, and have many methods for inspecting the quality of the fit.

The applications module contains pre-built models for standard applications (e.g. linear regression, logistic regression, and multilayer dense neural networks) which are ready to be fit.

The callbacks module contains the Callback abstract base class, and several specific types of callbacks which can be used to control the training process, record metrics or parameter values over the course of training, stop training early, etc.

The data module contains the DataGenerator class, which can be used to feed data during training when the dataset is too large to fit into memory.

The utils module contains various utilities, generally not intended to be used by users, such as abstract base classes, casting functions, settings, etc.