Major Features And Improvements
tf.data is now part of the core TensorFlow API.
The API is now subject to backwards compatibility guarantees.
For a guide to migrating from the tf.contrib.data API, see the
Major new features include Dataset.from_generator() (for building an input
pipeline from a Python generator), and the Dataset.apply() method for
applying custom transformation functions.
Several custom transformation functions have been added, including
Add train_and_evaluate for simple distributed Estimator training.
Add tf.spectral.dct for computing the DCT-II.
Add Mel-Frequency Cepstral Coefficient support to tf.contrib.signal
(with GPU and gradient support).
Add a self-check on import tensorflow for Windows DLL issues.
Add NCHW support to tf.depth_to_space on GPU.
SinhArcsinh (scalar) distribution added to contrib.distributions.
Make GANEstimator opensource.
Estimator.export_savedmodel() now includes all valid serving signatures
that can be constructed from the Serving Input Receiver and all available
ExportOutputs. For instance, a classifier may provide regression- and
prediction-flavored outputs, in addition to the classification-flavored one.
Building signatures from these allows TF Serving to honor requests using the
different APIs (Classify, Regress, and Predict). Furthermore,
serving_input_receiver_fn() may now specify alternative subsets of nodes
that may act as inputs. This allows, for instance, producing a prediction
signature for a classifier that accepts raw Tensors instead of a serialized
Make Dataset.shuffle() always reshuffles after each iteration by default.
Add log_rate parameter to tf.contrib.distributions.Poisson.
Extend tf.contrib.distributions.bijector API to handle some non-injective
Generics (e.g., Tensor
Support for multi-dimensional string tensors.
Support loading of custom operations (e.g. many in tf.contrib) on Linux
and OS X