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TensorFlow v0.12.0 RC0

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@gunan gunan released this 29 Nov 02:08
· 65 commits to r0.12 since this release
0.12.0-rc0
48408fc

Release 0.12.0

Major Features and Improvements

  • TensorFlow now builds and runs on Microsoft Windows (tested on Windows 10,
    Windows 7, and Windows Server 2016). Supported languages include Python (via a
    pip package) and C++. CUDA 8.0 and cuDNN 5.1 are supported for GPU
    acceleration. Known limitations include: It is not currently possible to load
    a custom op library. The GCS and HDFS file systems are not currently
    supported. The following ops are not currently implemented:
    DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter,
    DepthwiseConv2dNativeBackpropInput, Dequantize, Digamma, Erf, Erfc, Igamma,
    Igammac, Lgamma, Polygamma, QuantizeAndDequantize, QuantizedAvgPool,
    QuantizedBatchNomWithGlobalNormalization, QuantizedBiasAdd, QuantizedConcat,
    QuantizedConv2D, QuantizedMatmul, QuantizedMaxPool,
    QuantizeDownAndShrinkRange, QuantizedRelu, QuantizedRelu6, QuantizedReshape,
    QuantizeV2, RequantizationRange, and Requantize.
  • Go: Experimental API in Go to create and execute graphs
    (https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go)
  • New checkpoint format becomes the default in tf.train.Saver. Old V1
    checkpoints continue to be readable; controlled by the write_version
    argument, tf.train.Saver now by default writes out in the new V2
    format. It significantly reduces the peak memory required and latency
    incurred during restore.
  • Added a new library for library of matrix-free (iterative) solvers for linear
    equations, linear least-squares, eigenvalues and singular values in
    tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization,
    conjugate gradients and CGLS.
  • Added gradients for matrix_solve_ls and self_adjoint_eig.
  • Large cleanup to add second order gradient for ops with C++ gradients and
    improve existing gradients such that most ops can now be differentiated
    multiple times.
  • Added a solver for ordinary differential equations,
    tf.contrib.integrate.odeint.
  • New contrib module for tensors with named axes, tf.contrib.labeled_tensor.
  • Visualization of embeddings in TensorBoard.

Breaking Changes to the API

  • BusAdjacency enum replaced with a protocol buffer DeviceLocality. PCI bus
    indexing now starts from 1 instead of 0, and bus_id==0 is used where
    previously BUS_ANY was used.
  • Env::FileExists and FileSystem::FileExists now return a
    tensorflow::Status intead of a bool. Any callers to this function can be
    converted to a bool by adding .ok() to the call.
  • C API: Type TF_SessionWithGraph has been renamed to TF_Session, indicating
    its preferred use in language bindings for TensorFlow. What was previously
    TF_Session has been renamed to TF_DeprecatedSession.
  • C API: Renamed TF_Port to TF_Output.
  • C API: The caller retains ownership of TF_Tensor objects provided to
    TF_Run, TF_SessionRun, TF_SetAttrTensor etc.
  • Renamed tf.image.per_image_whitening() to
    tf.image.per_image_standardization()
  • Move Summary protobuf constructors to tf.summary submodule.
  • Deprecate histogram_summary, audio_summary, scalar_summary,
    image_summary, merge_summary, and merge_all_summaries.
  • Combined batch_* and regular version of linear algebra and FFT ops. The
    regular op now handles batches as well. All batch_* Python interfaces were
    removed.
  • tf.all_variables, tf.VARIABLES and tf.initialize_all_variables renamed
    to tf.global_variables, tf.GLOBAL_VARIABLES and
    tf.global_variable_initializers respectively.

Bug Fixes and Other Changes

  • Use threadsafe version of lgamma function.
  • Fix tf.sqrt handling of negative arguments.
  • Fixed bug causing incorrect number of threads to be used for multi-threaded
    benchmarks.
  • Performance optimizations for batch_matmul on multi-core CPUs.
  • Improve trace, matrix_set_diag, matrix_diag_part and their gradients to
    work for rectangular matrices.
  • Support for SVD of complex valued matrices.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

@a7744hsc, Abhi Agg, @admcrae, Adriano Carmezim, Aki Sukegawa, Alex Kendall,
Alexander Rosenberg Johansen, @amcrae, Amlan Kar, Andre Simpelo, Andreas Eberle,
Andrew Hundt, Arnaud Lenglet, @b0noI, Balachander Ramachandran, Ben Barsdell,
Ben Guidarelli, Benjamin Mularczyk, Burness Duan, @c0g, Changming Sun,
@chanis, Corey Wharton, Dan J, Daniel Trebbien, Darren Garvey, David Brailovsky,
David Jones, Di Zeng, @DjangoPeng, Dr. Kashif Rasul, @Drag0, Fabrizio (Misto)
Milo, FabríCio Ceschin, @fp, @Ghedeon, @guschmue, Gökçen Eraslan, Haosdent
Huang, Haroen Viaene, Harold Cooper, Henrik Holst, @hoangmit, Ivan Ukhov, Javier
Dehesa, Jingtian Peng, Jithin Odattu, Joan Pastor, Johan Mathe, Johannes Mayer,
Jongwook Choi, Justus Schwabedal, Kai Wolf, Kamil Hryniewicz, Kamran Amini,
Karen Brems, Karl Lattimer, @kborer, Ken Shirriff, Kevin Rose, Larissa Laich,
Laurent Mazare, Leonard Lee, Liang-Chi Hsieh, Liangliang He, Luke Iwanski,
Marek Kolodziej, Moustafa Alzantot, @MrQianJinSi, @nagachika, Neil Han, Nick
Meehan, Niels Ole Salscheider, Nikhil Mishra, @nschuc, Ondrej Skopek, OndřEj
Filip, @OscarDPan, Pablo Moyano, Przemyslaw Tredak, @qitaishui, @Quarazy,
@raix852, Philipp Helo, Sam Abrahams, @SriramRamesh, Till Hoffmann, Tushar Soni,
@tvn, @tyfkda, Uwe Schmidt, Victor Villas, Vit Stepanovs, Vladislav Gubarev,
@wujingyue, Xuesong Yang, Yi Liu, Yilei Yang, @youyou3, Yuan (Terry) Tang,
Yuming Wang, Zafar Takhirov, @zhongyuk, Ziming Dong, @guotong1988

We are also grateful to all who filed issues or helped resolve them, asked and
answered questions, and were part of inspiring discussions.

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