# 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.