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@BenjaminBossan BenjaminBossan released this 30 Aug 10:46
· 185 commits to master since this release
v0.9.0
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Learn about vigilant mode.

This release of skorch contains a few minor improvements and some nice additions. As always, we fixed a few bugs and improved the documentation. Our learning rate scheduler now optionally logs learning rate changes to the history; moreover, it now allows the user to choose whether an update step should be made after each batch or each epoch.

If you always longed for a metric that would just use whatever is defined by your criterion, look no further than loss_scoring. Also, skorch now allows you to easily change the kind of nonlinearity to apply to the module's output when predict and predict_proba are called, by passing the predict_nonlinearity argument.

Besides these changes, we improved the customization potential of skorch. First of all, the criterion is now set to train or valid, depending on the phase -- this is useful if the criterion should act differently during training and validation. Next we made it easier to add custom modules, optimizers, and criteria to your neural net; this should facilitate implementing architectures like GANs. Consult the docs for more on this. Conveniently, net.save_params can now persist arbitrary attributes, including those custom modules.
As always, these improvements wouldn't have been possible without the community. Please keep asking questions, raising issues, and proposing new features. We are especially grateful to those community members, old and new, who contributed via PRs:

Aaron Berk
guybuk
kqf
Michał Słapek
Scott Sievert
Yann Dubois
Zhao Meng

Here is the full list of all changes:

Added

  • Added the event_name argument for LRScheduler for optional recording of LR changes inside net.history. NOTE: Supported only in Pytorch>=1.4
  • Make it easier to add custom modules or optimizers to a neural net class by automatically registering them where necessary and by making them available to set_params
  • Added the step_every argument for LRScheduler to set whether the scheduler step should be taken on every epoch or on every batch.
  • Added the scoring module with loss_scoring function, which computes the net's loss (using get_loss) on provided input data.
  • Added a parameter predict_nonlinearity to NeuralNet which allows users to control the nonlinearity to be applied to the module output when calling predict and predict_proba (#637, #661)
  • Added the possibility to save the criterion with save_params and with checkpoint callbacks
  • Added the possibility to save custom modules with save_params and with checkpoint callbacks

Changed

  • Removed support for schedulers with a batch_step() method in LRScheduler.
  • Raise FutureWarning in CVSplit when random_state is not used. Will raise an exception in a future (#620)
  • The behavior of method net.get_params changed to make it more consistent with sklearn: it will no longer return "learned" attributes like module_; therefore, functions like sklearn.base.clone, when called with a fitted net, will no longer return a fitted net but instead an uninitialized net; if you want a copy of a fitted net, use copy.deepcopy instead;net.get_params is used under the hood by many sklearn functions and classes, such as GridSearchCV, whose behavior may thus be affected by the change. (#521, #527)
  • Raise FutureWarning when using CyclicLR scheduler, because the default behavior has changed from taking a step every batch to taking a step every epoch. (#626)
  • Set train/validation on criterion if it's a PyTorch module (#621)
  • Don't pass y=None to NeuralNet.train_split to enable the direct use of split functions without positional y in their signatures. This is useful when working with unsupervised data (#605).
  • to_numpy is now able to unpack dicts and lists/tuples (#657, #658)
  • When using CrossEntropyLoss, softmax is now automatically applied to the output when calling predict or predict_proba

Fixed

  • Fixed a bug where CyclicLR scheduler would update during both training and validation rather than just during training.
  • Fixed a bug introduced by moving the optimizer.zero_grad() call outside of the train step function, making it incompatible with LBFGS and other optimizers that call the train step several times per batch (#636)
  • Fixed pickling of the ProgressBar callback (#656)
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