Changes

Most recent releases are shown at the top. Each release shows:

New: New classes, methods, functions, etc
Changed: Additional paramaters, changes to inputs or outputs, etc
Fixed: Bug fixes that don't change documented behaviour

Note that the top-most release is changes in the unreleased master branch on Github.

1.0.7 (2018-10-19)

New:

New class ImagePoints for targets that are a set of point coordinates
New function Image.predict(learn:Learner) to get the activations of the model in Learner for an image
New function Learner.validate to validate on a given dl (default valid_dl), with maybe new metrics or callbacks
New function error_rate which is just 1-accuracy()

Changed:

data is now called basic_data to avoid weird conflicts when naming our data objects data.
All vision models are now in the models module, including torchvision models (where tested and supported). So use models instead of tvm now. If your preferred torchvision model isn't imported, feel free to test it out and tell us on the forum if it works. And if it doesn't, a PR with a test and a fix would be appreciated!
ImageBBox is now a subclass of ImagePoints
All metrics are now Callback. You can pass a regular function like accuracy that will get averaged over batch or a full Callback that can do more complex things
All datasets convenience functions and paths are inside the URLs class
URLs that are a sample have name now suffixed with _SAMPLE

Fixed:

Fix WeightDropout in RNNs when p=0
pad_collate gets its kwargs from TextClasDataBunch
Add small eps to std in TabularDataset to avoid division by zero
fit_one_cycle doesn't take other callbacks
Many broken docs links fixed

1.0.6 (2018-10-01)

Last release without CHANGES updates

1.0.0 (2018-10-01)

First release