This release is a significant refactor of T2T internals.
T2TModel
subclasses now have the ability to override the entire Estimator model function with theestimator_model_fn
method, making them much more flexible. Subclasses can also now overridebottom
,body
,top
,loss
, andoptimize
.Problem
subclasses now have the ability to override the entire Estimator input function with theinput_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 throughtpu_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/