v1.5.4

• Updates to the RL codebase
• Tests updated to use TensorFlow 1.6
• Various fixes, additions, etc.

v1.5.3

• More flexible Cloud ML Engine usage thanks to @bbarnes52
• Fixes thanks to @stefan-it @wes-turner @deasuke @bwilbertz
• Various other additions, fixes, etc.

v1.5.2

Note: The Text2TextProblem has been refactored so if you have subclassed it you may need to rename some methods. Some vocabulary files may need to be renamed as well.

• Text2TextProblem, Text2ClassProblem and Text2SelfProblem base classes make specifying new text-based problems easy. See text_problems.py.
• New models and problems, including for image generation and speech-to-text
• Various bug fixes, feature additions, improvements, etc.
• Test model export and serving for Python 2.7 and TensorFlow 1.5
• Update Travis tests to test against TensorFlow version 1.4, 1.5, and 1.6

v1.5.1

• TF 1.4 compatibility bug fix for Cloud ML Engine

v1.5.0

• Launch training on Cloud TPUs
• Launch training and hyperparameter tuning on Cloud ML Engine
• New models/research subdirectory for more experimental models
• Some documentation updates
• Bug fixes

v1.4.4

• Cloud ML Engine support added
• New experimental RL module thanks to @piotrmilos
• Various bug fixes, improvements, etc.

v1.4.3

Note: Tensor2Tensor now requires TensorFlow 1.5.

• Working t2t-bleu thanks to @martinpopel
• Improvements to image models: resnet, revnet, and shake_shake
• Image problems refactor: faster input pipeline, richer ImageNet data preprocessing. Note that ImageModality.bottom no longer normalizes images; that's now done in the input pipeline.
• Improvements for running on Google's Cloud TPUs, coming to you soon...
• Various bug fixes, improvements, and additions

v1.4.2

• New export method for exporting to TensorFlow Serving
• Script for BLEU evaluation thanks to @martinpopel
• Better TensorBoard metrics (what was removed has returned), with options to summarize gradients (--hparams='summarize_grads=True')
• Various bug fixes, doc updates, new features, as usual

Internals:

• Scripts in bin/ are now thin and executable
• Main training utility library moved to trainer_lib.py

v1.4.1

• Support for multi-device evaluation
• Support for early stopping in distributed training
• Refactor Librispeech problem to use a new speech recognition base class

v1.4.0

This release is a significant refactor of T2T internals.

• T2TModel subclasses now have the ability to override the entire Estimator model function with the estimator_model_fn method, making them much more flexible. Subclasses can also now override bottom, body, top, loss, and optimize.
• Problem subclasses now have the ability to override the entire Estimator input function with the input_fn method, making them much more flexible.
• The key components of the trainer and decoder - Experiment, Estimator, RunConfig, HParams - are all much more easily constructed and used by library callers through tpu_trainer_lib.py.
• We decided to drop support for MultiModel, i.e. training on multiple problems, because it added too much code complexity for the benefit gained. We will consider adding support back in a way that doesn't overcomplicate things too much if there's sufficient interest.

There are also the usual new models, feature improvements, bug fixes.

• New image_fashion_mnist dataset
• New revnet104 model, implementing a large Reversible Residual Network
• Set --decode_hparams=write_beam_scores=True to include beam scores when writing to a file
• Beginnings of new interactive visualization server at insights/