# Release Notes ## 0.3.5.2 * Unsupervised Pre-training guide for semi-supervised learning included. * Larger datasets run faster due to optimized dataset cardinality calculation. * Autofit runs with the same early stopping callback but epochs set to 1000000, not 2**31, to make console output more interpretable. * Distillation report includes number of weights in source and target (teacher and student) models. * Logging includes both val and test sets (if test is available). Note that the metalearning algorithm never sees test - evaluations on test are purely for diagnostics. * Log directories named by run number (e.g. ~/.masterful/run-00001) instead of datetime (e.g. ~/.masterful/UTC_2021-08-18__17-34-29.037488) * Detailed logs originally (sent to ~/.masterful) can now also be sent to console via env variable MASTERFUL_LOG_TO_CONSOLE=1. * In some cases, fit was broken due to a bug in keras: model.trainable on a cloned model has undocumented behavior. Solution was implemented which ensures autofit and masterful.core.fit will run successfully. * Warmup implemented. * Batchnorm warmup implemented to ensure val metrics are based on stable batch norm moving metrics. This is particularly helpful on image data that is not prenormalized to zero-mean, unit-variance (ZMUV). Known Issues: * "Gradient Not Found" warning is sent to console during autofit and masterful.core.fit. This warning is innocuous. ## 0.3.5.1 * Noisy Student Training reintroduced. * Robust but slower settings for optimizer policy. * Unsupervised pretraining supports larger model sizes. * Distillation API. * Removed warmup due to overfitting bug, will slow down training speeds but not affect final accuracy. * Documentation for graphical front end, ensembling, and distillation. ## 0.3.5 * Revised API for autofit and advanced "core" api. * find_optimizer_policy searches for optimal policy for optimizer settings. * find_batch_size searches for largest batch size that fits in memory to speedup training. * General API for data and model specifications. * Unsupervised pretraining in the advanced API. * Added reflection on spatial transformations. ## 0.3.4 * Added quickstart tutorial documentation. * Separated console output from logging to disk. * Support for several data formats. * Anchor box conversion for Fizyr Keras Retinanet model. * Localization/detection support for spatial transforms. * Protobuf logging on intermediate search phases. * Layerization of losses with serialization. * Native support for loss_weights. * Native support for multiple losses. (codename victor) ## 0.3.3 * Mixup transformation. * Eliminated all showstopper bugs from previous release. * Careful control of LR during metalearning algorithm. (codename uniform) ## 0.3.2 * Cutmix transformation. * Removed epochs and lr callbacks, now user responsibility. * Eliminated some showstopper bugs from previous version. Known Issues: * Unstable release, do not use. (codename tango) ## 0.3.1 * Added saving and loading of policies. * Noisy Student Training functionality. Known Issues: * Unstable release, do not use. (codename sierra) ## 0.3 * Layerization of transforms for high speed augmentation. * Groundup implementation of transforms. * Distance analysis to cluster transforms. * Metalearner is now beam search. (codename bravo) ## 0.2.1 * Multiple bug fixes and performance improvements * Adds supports for TPU training in GCP using Tensorflow 1.15 * Package name has been renamed masterful from masterful_lite * Corresponding API's now reside under masterful.api rather than masterful.api.lite (fka 0.1.5) ## 0.2 * Support for multiple instance segmentation masks and bounding boxes has been added. Breaking Changes: * This add an API breaking change in the way that labels and masks are packed. See the updated documentation for details. (fka 0.1.2) ## 0.1 * Episilon greedy based meta learning algorithm. Known Issues: * Slow as it requires frequent shuffling between CPU and GPU via py_function.