Version 0.5.0

Version 0.5.0

Added

• Basic usage notebook now runs on Google Colab
• Advanced usage notebook now runs on Google Colab
• MNIST with scikit-learn and skorch now runs on Google Colab
• Better user-facing messages when module or optimizer are re-initialized
• Added an experimental API (net._register_virtual_param) to register "virtual"
  parameters on the network with custom setter functions. (#369)
• Setting parameters lr, momentum, optimizer__lr, etc. no longer resets
  the optmizer. As of now you can do net.set_params(lr=0.03) or
  net.set_params(optimizer__param_group__0__momentum=0.86) without triggering
  a re-initialization of the optimizer (#369)
• Support for scipy sparse CSR matrices as input (as, e.g., returned by sklearn's
  CountVectorizer); note that they are cast to dense matrices during batching
• Helper functions to build command line interfaces with almost no
  boilerplate, example that shows usage

Changed

• Reduce overhead of BatchScoring when using train_loss_score or valid_loss_score by skipping superfluous inference step (#381)
• The on_grad_computed callback function will yield an iterable for named_parameters only when it is used to reduce the run-time overhead of the call (#379)
• Default fn_prefix in TrainEndCheckpoint is now train_end_ (#391)
• Issues a warning when Checkpoints's monitor parameter is set to monitor and the history contains <monitor>_best. (#399)

Fixed

• Re-initialize optimizer when set_params is called with lr argument (#372)
• Copying a SliceDict now returns a SliceDict instead of a dict (#388)
• Calling == on SliceDicts now works as expected when values are numpy arrays and torch tensors