Major Features and Improvements
- cuDNN 5 support.
- HDFS Support.
- Adds Fused LSTM support via cuDNN 5 in
tensorflow/contrib/cudnn_rnn.
- Improved support for NumPy style basic slicing including non-1 strides,
ellipses, newaxis, and negative indices. For example complicated expressions
like
foo[1, 2:4, tf.newaxis, ..., :-3:-1, :] are now supported. In addition
we have preliminary (non-broadcasting) support for sliced assignment to
variables. In particular one can write var[1:3].assign([1,11,111]).
- Introducing
core/util/tensor_bundle module: a module to efficiently
serialize/deserialize tensors to disk. Will be used in TF's new checkpoint
format.
- Added tf.svd for computing the singular value decomposition (SVD) of dense
matrices or batches of matrices (CPU only).
- Added gradients for eigenvalues and eigenvectors computed using
self_adjoint_eig or self_adjoint_eigvals.
- Eliminated
batch_* methods for most linear algebra and FFT ops and promoted
the non-batch version of the ops to handle batches of matrices.
- Tracing/timeline support for distributed runtime (no GPU profiler yet).
- C API gives access to inferred shapes with
TF_GraphGetTensorNumDims and
TF_GraphGetTensorShape.
- Shape functions for core ops have moved to C++ via
REGISTER_OP(...).SetShapeFn(...). Python shape inference RegisterShape calls
use the C++ shape functions with common_shapes.call_cpp_shape_fn. A future
release will remove RegisterShape from python.
Bug Fixes and Other Changes
- Documentation now includes operator overloads on Tensor and Variable.
tensorflow.__git_version__ now allows users to identify the version of the
code that TensorFlow was compiled with. We also have
tensorflow.__git_compiler__ which identifies the compiler used to compile
TensorFlow's core.- Improved multi-threaded performance of
batch_matmul.
- LSTMCell, BasicLSTMCell, and MultiRNNCell constructors now default to
state_is_tuple=True. For a quick fix while transitioning to the new
default, simply pass the argument state_is_tuple=False.
- DeviceFactory's AddDevices and CreateDevices functions now return
a Status instead of void.
- Int32 elements of list(type) arguments are no longer placed in host memory by
default. If necessary, a list(type) argument to a kernel can be placed in host
memory using a HostMemory annotation.
uniform_unit_scaling_initializer() no longer takes a full_shape arg,
instead relying on the partition info passed to the initializer function when
it's called.- The NodeDef protocol message is now defined in its own file
node_def.proto
instead of graph.proto.
ops.NoGradient was renamed ops.NotDifferentiable. ops.NoGradient will
be removed soon.dot.h / DotGraph was removed (it was an early analysis tool prior
to TensorBoard, no longer that useful). It remains in history
should someone find the code useful.
Thanks to our Contributors
This release contains contributions from many people at Google, as well as:
Abid K, @afshinrahimi, @AidanGG, Ajay Rao, Aki Sukegawa, Alex Rothberg,
Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Thomas, @Appleholic,
Bastiaan Quast, Ben Dilday, Bofu Chen, Brandon Amos, Bryon Gloden, Cisspยฎ,
@chanis, Chenyang Liu, Corey Wharton, Daeyun Shin, Daniel Julius Lasiman, Daniel
Waterworth, Danijar Hafner, Darren Garvey, Denis Gorbachev, @DjangoPeng,
Egor-Krivov, Elia Palme, Eric Platon, Fabrizio Milo, Gaetan Semet,
Georg Nebehay, Gu Wang, Gustav Larsson, @haosdent, Harold Cooper, Hw-Zz,
@ichuang, Igor Babuschkin, Igor Macedo Quintanilha, Ilya Edrenkin, @ironhead,
Jakub Kolodziejczyk, Jennifer Guo, Jihun Choi, Jonas Rauber, Josh Bleecher
Snyder, @jpangburn, Jules Gagnon-Marchand, Karen Brems, @kborer, Kirill Bobyrev,
Laurent Mazare, Longqi Yang, Malith Yapa, Maniteja Nandana, Martin Englund,
Matthias Winkelmann, @mecab, Mu-Ik Jeon, Nand Dalal, Niels Ole Salscheider,
Nikhil Mishra, Park Jiin, Pieter De Rijk, @raix852, Ritwik Gupta, Sahil Sharma,
@Sangheum, @SergejsRk, Shinichiro Hamaji, Simon Denel, @Steve, @suiyuan2009,
Tiago Jorge, Tijmen Tieleman, @tvn, @tyfkda, Wang Yang, Wei-Ting Kuo, Wenjian
Huang, Yan Chen, @YenChenLin, Yuan (Terry) Tang, Yuncheng Li, Yunfeng Wang, Zack
Polizzi, @zhongzyd, Ziming Dong, @perhapszzy
We are also grateful to all who filed issues or helped resolve them, asked and
answered questions, and were part of inspiring discussions.
tensorflow/tensorflow
