Release Version 0.3.0 · pytorchbearer/torchbearer

[0.3.0] - 2019-02-28

Added torchbearer.variational, a sub-package for implementations of state of the art variational auto-encoders
Added SimpleUniform and SimpleExponential distributions
Added a decorator which can be used to cite a research article as part of a doc string
Added an optional dimension argument to the mean, std and running_mean metric aggregators
Added a var metric and decorator which can be used to calculate the variance of a metric
Added an unbiased flag to the std and var metrics to optionally not apply Bessel's correction (consistent with torch.std / torch.var)
Added support for rounding 1D lists to the Tqdm callback
Added SimpleWeibull distribution
Added support for Python 2.7
Added SimpleWeibullSimpleWeibullKL
Added SimpleExponentialSimpleExponentialKL
Added the option for model parameters only saving to Checkpointers.
Added documentation about serialization.
Added support for indefinite data loading. Iterators can now be run until complete independent of epochs or iterators can be refreshed during an epoch if complete.
Added support for batch intervals in interval checkpointer
Added line magic %torchbearer notebook
Added 'accuracy' variants of 'acc' default metrics

Changed the default behaviour of the std metric to compute the sample std, in line with torch.std
Tqdm precision argument now rounds to decimal places rather than significant figures
Trial will now simply infer if the model has an argument called 'state'
Torchbearer now infers if inside a notebook and will use the appropriate tqdm module if not set

Fixed a bug in the weight decay callback which would result in potentially negative decay (now just uses torch.norm)
Fixed a bug in the cite decorator causing the citation to not show up correctly
Fixed a memory leak in the mse primitive metric