interfaces.md

Interfaces

FastAI.jl provides many interfaces that allow extending its functionality.

Learning method interfaces

Learning methods form the core of FastAI.jl's high-level API. See this tutorial for a motivation and introduction.

Functions for the learning method interfaces always dispatch on a LearningMethod. A LearningMethod defines everything that needs to happen to turn an input into a target and much more. LearningMethod should be a struct containing configuration.

Core interface

Enables training and prediction. Prerequisite for other, optional learning method interfaces.

{.tight}

• Required methods:
  • encode or both encodeinput and encodetarget.
  • decodeŷ
• Optional methods:
  • shouldbatch
  • encode! or both encodeinput! and encodetarget!.
• Enables use of:
  • methoddataset
  • methoddataloaders
  • predict
  • predictbatch

Plotting interface

For visualizing observations and predictions using Makie.jl.

{.tight}

• Required methods:
  • plotsample!
  • plotxy!
  • plotprediction!
• Enables use of:
  • plotsamples
  • plotbatch

Training interface

Convenience for creating Learners.

{.tight}

• Required methods:
  • methodlossfn
  • methodmodel
• Enables use of:
  • methodlearner

Testing interface

Automatically test interfaces.

{.tight}

• Required methods:
  • mockmodel
  • mocksample or both mockinput and mocktarget
• Enables use of:
  • checkmethod_core

Callback interface

See the FluxTraining.jl tutorial.

Data container interface