hasktorch- Functional differentiable programming in Haskell
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Modules for defining datasets and how to efficiently iterate over them. If you have an indexable (fixed-size) dataset, see Torch.Data.Pipeline. If you want to stream in your data then see Torch.Data.StreamedPipeline. The Torch.Data.Utils module provides some convienient functions for both indexable and streamed datasets.

The mnist examples show how to run data for a predefined dataset.


Running data

The preferred method for running data is the same for both Dataset and Datastream. The intended use is to use the streamFrom family of functions and run the continuation returned by those functions with a function that specifies what to do with the given stream. Datasets are then a pipes stream of samples, so anything that you can with a pipes stream you can do with a Dataset or Datastream. As such you should have some basic familiarity with pipes streams, though typically you'll want to a fold over the dataset, where Pipes.Prelude has convenient functions for folding streams.

import qualified Pipes.Prelude as P
import Pipes

-- Take a model and a stream of data from a Dataset or Datastream,
-- and train the model.
train :: model -> ListT m sample -> m model
train model = runEffect . P.foldM step begin done . enumerate
      -- run a training step over a given sample from the dataset
      step model batch = undefined
      begin = pure model
      done = pure

runData = runContT (train model) $ streamFromMap (datasetOptions 1) myDataset

See the foldl library for the style of fold used here.