v0.2.0

Features

• PyTorch 0.4 support
• Add GradNormClipping callback
• Add generic learning rate scheduler callback
• Add CyclicLR learning rate scheduler
• Add WarmRestartLR learning rate scheduler
• Scoring callbacks now re-use predictions, accelerating training
• fit() and inference methods (e.g., predict()) now support torch.util.data.Dataset as input as long as (X, y) pairs are returned
• forward and forward_iter now allow you to specify on which device to store intermediate predictions
• Support for setting optimizer param groups using wildcards (e.g., {'layer*.bias': {'lr': 0}})
• Computed gradients can now be processed by callbacks using on_grad_computed
• Support for fit_params parameter which gets passed directly to the module
• Add skorch.helper.SliceDict so that you can use dict as X with sklearn's GridSearchCV, etc.
• Add Dockerfile

API changes

• Deprecated use_cuda parameter in favor of device parameter
• skorch.utils.to_var is gone in favor of skorch.utils.to_tensor
• training_step and validation_step now return a dict with the loss and the module's prediction
• predict and predict_proba now handle multiple outputs by assuming the first output to be the prediction
• NeuralNetClassifier now only takes log of prediction if the criterion is set to NLLLoss

Examples

• RNN sentiment classification

Communication

• We now run the #skorch channel on the PyTorch slack workspace

Contributors

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

• Felipe Ribeiro
• Grzegorz Rygielski
• Juri Paern
• Thomas Fan