v0.3.0

Features

• significantly reduced overhead of skorch over pytorch for small/medium loads
• predefined splits are easier to use (skorch.helper.predefined_split)
• freezing layers is now easier with skorch.helper.filtered_optimizer
• introduce NeuralNetBinaryClassifier
• introduce early stopping callback
• support parallel grid search using Dask
• support for LBFGS
• history can be saved/loaded independently
• learning rate scheduler have a method to simulate behavior
• Checkpoint callback supports pickling and history saving
• Checkpoint callback is less noisy
• added transfer learning tutorial
• added tutorial how to expose skorch via REST API
• improved documentation

API changes

• train_step is now split in train_step and train_step_single in order to support LBFGS, where train_step_single takes the role of your typical training inner-loop when writing PyTorch models
• device parameter on skorch.dataset.Dataset is now deprecated
• Checkpoint parameter target is deprecated in favor of f_params

Contributors

A big thanks to our contributors who helped making this release possible:

• Andrew Spott
• Scott Sievert
• Sergey Alexandrov
• Thomas Fan
• Tomasz Pietruszka