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@ottonemo ottonemo released this 19 Jun 15:21
· 342 commits to master since this release
v0.6.0
14f374d

[0.6.0] - 2019-07-19

This release introduces convenience features such as SliceDataset which makes using torch datasets (e.g. from torchvision) easier in combination with sklearn features such as GridSearchCV. There was also some work to make the transition from CUDA trained models to CPU smoother and learning rate schedulers were upgraded to use torch builtin functionality.

Here's the full list of changes:

Added

  • Adds FAQ entry regarding the initialization behavior of NeuralNet when passed instantiated models. (#409)
  • Added CUDA pickle test including an artifact that supports testing on CUDA-less CI machines
  • Adds train_batch_count and valid_batch_count to history in training loop. (#445)
  • Adds score method for NeuralNetClassifier, NeuralNetBinaryClassifier, and NeuralNetRegressor (#469)
  • Wrapper class for torch Datasets to make them work with some sklearn features (e.g. grid search). (#443)

Changed

  • Repository moved to https://github.com/skorch-dev/skorch/, please change your git remotes
  • Treat cuda dependent attributes as prefix to cover values set using set_params since
    previously "criterion_" would not match net.criterion__weight as set by
    net.set_params(criterion__weight=w)
  • skorch pickle format changed in order to improve CUDA compatibility, if you have pickled models, please re-pickle them to be able to load them in the future
  • net.criterion_ and its parameters are now moved to target device when using criteria that inherit from torch.nn.Module. Previously the user had to make sure that parameters such as class weight are on the compute device
  • skorch now assumes PyTorch >= 1.1.0. This mainly affects learning rate schedulers, whose inner workings have been changed with version 1.1.0. This update will also invalidate pickled skorch models after a change introduced in PyTorch optimizers.

Fixed

  • Include requirements in MANIFEST.in
  • Add criterion_ to NeuralNet.cuda_dependent_attributes_ to avoid issues with criterion
    weight tensors from, e.g., NLLLoss (#426)
  • TrainEndCheckpoint can be cloned by sklearn.base.clone. (#459)

Thanks to all the contributors:

  • Bram Vanroy
  • Damien Lancry
  • Ethan Rosenthal
  • Sergey Alexandrov
  • Thomas Fan
  • Zayd Hammoudeh
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