Terms commonly used in FastAI.jl.
In many docstrings, generic types are abbreviated with the following symbols. Many of these refer to a learning task; the context should make clear which task is meant.
DC{T}: A
data container of type T, meaning a type that implements the data container interface
getindex/
getobs and
length/
numobs where
getobs : (DC{T}, Int) -> Int, that is, each observation is of type
T.
I: Type of the unprocessed input in the context of a task.
T: Type of the target variable.
X: Type of the processed input. This is fed into a
model, though it may be batched beforehand.
Xs represents a batch of processed inputs.
Y: Type of the model output.
Ys represents a batch of model outputs.
model/
M: A learnable mapping
M : (X,) -> Y or
M : (Xs,) -> Ys. It predicts an encoded target from an encoded input. The learnable part of a learning task.
Some examples of these in use:
LearningTask is a concrete approach to learning to predict
T from
I by using the encoded representations
X and
Y.
encodeinput : (task, context, I) -> X encodes an input so that a prediction can be made by a model.
A task dataset is a
DC{(I, T)}, i.e. a data container where each observation is a 2-tuple of an input and a target.
A data structure that is used to load a number of data observations separately and lazily. It defines how many observations it holds with
numobs and how to load a single observation with
getobs.
An instance of
DLPipelines.LearningTask. A concrete approach to solving a learning task. Encapsulates the logic and configuration for processing data to train a model and make predictions.
See the DLPipelines.jl documentation for more information.
DC{(I, T)}. A data container containing pairs of inputs and targets. Used in
taskdataset,
taskdataloaders and
evaluate.