版本contribute發布：https://fossies.org/linux/tensorflow/RELEASE.md

????????????????????????????????????? https://github.com/tensorflow/tensorflow/releases/tag/v1.14.0

??????????????????????????????????? ? https://www.oschina.net/news/107581/tensorflow-1-14-0-released

此版本包含不少新特性和功能改進，還修復了大量的 bug。

主要新特性和功能改進：

• 這是包含 compat.v2 模塊的第一個 1.x 版本。該模塊要求 Tensorflow 庫發布在 1.x 和 2.x 中都有效的代碼。在此版本之后，2.0 Python API 中不允許進行向后不兼容的更改。
• 默認情況下打開 MKL-DNN contraction 內核。MKL-DNN 基于 CPU 矢量體系結構動態調度最佳內核實現。要禁用它們，請使用 ?--define=tensorflow_mkldnn_contraction_kernel=0 進行構建。
• 現在，非 Windows 系統庫已經過版本控制，它只影響系統包維護者或那些構建 TensorFlow 的擴展：
  • Python wheels（Pip 包）包含一個庫文件
    • Linux：libtensorflow_framework.so.1
    • MacOS：?libtensorflow_framework.1.dylib
  • libtensorflow?存檔包含?libtensorflow?庫和兩個符號鏈接。 MacOS?.dylib?庫是一樣的，?但符合 MacOS 庫命名要求（即?libtensorflow.1.dylib）：
    • libtensorflow.so.1.14.0，主要的庫
    • libtensorflow.so.1，符號鏈接到主庫
    • libtensorflow.so，符號鏈接到?.so.1

新版本還包含大量更改和 bug 修復，詳情請查看更新說明。

重要的變化：

在運算符的改進上，

• New ops and improved op functionality
  • Add OpKernels for some stateless maps
  • Add v2 APIs for AUCCurve and AUCSummationMethod enums. #tf-metrics-convergence
  • Add tf.math.nextafter op.
  • Add CompositeTensor base class.
  • Add tf.linalg.tridiagonal_solve op.
  • Add opkernel templates for common table operations.
  • Added GPU implementation of tf.linalg.tridiagonal_solve.
  • Added support for TFLite in TensorFlow 2.0.
  • Adds summary trace API for collecting graph and profile information.
  • Add batch_dims argument to tf.gather.
  • Add support for add_metric in the graph function mode.
  • Add C++ Gradient for BatchMatMulV2.
  • Added tf.random.binomial
  • Added gradient for SparseToDense op.
  • Add legacy string flat hash map op kernels
  • Add a ragged size op and register it to the op dispatcher
  • Add broadcasting support to tf.matmul.
  • Add ellipsis (...) support for tf.einsum()
  • Added LinearOperator.adjoint and LinearOperator.H (alias).
  • Added GPU implementation of tf.linalg.tridiagonal_solve.
  • Added strings.byte_split
  • Add RaggedTensor.placeholder()
  • Add a new "result_type" parameter to tf.strings.split
  • add_update can now be passed a zero-arg callable in order to support turning off the update when setting trainable=False on a Layer of a Model compiled with run_eagerly=True.
  • Add variant wrapper for absl::string_view
  • Add expand_composites argument to all nest.* methods.
  • Add pfor converter for Squeeze.
  • Bug fix for tf.tile gradient
  • Expose CriticalSection in core as tf.CriticalSection.
  • Update Fingerprint64Map to use aliases
  • ResourceVariable support for gather_nd.
  • ResourceVariable's gather op supports batch dimensions.
  • Variadic reduce is supported on CPU
  • Extend tf.function with basic support for CompositeTensors arguments (such as SparseTensor and RaggedTensor).
  • Add templates and interfaces for creating lookup tables
  • Post-training quantization tool supports quantizing weights shared by multiple operations. The models made with versions of this tool will use INT8 types for weights and will only be executable interpreters from this version onwards.
  • Malformed gif images could result in an access out of bounds in the color palette of the frame. This has been fixed now
  • image.resize now considers proper pixel centers and has new kernels (incl. anti-aliasing).
• Performance
  • Turn on MKL-DNN contraction kernels by default. MKL-DNN dynamically dispatches the best kernel implementation based on CPU vector architecture. To disable them, build with --define=tensorflow_mkldnn_contraction_kernel=0.
  • Support for multi-host ncclAllReduce in Distribution Strategy.
  • Expose a flag that allows the number of threads to vary across Python benchmarks.

把contribute的一部分算子移到了基本運算層中.

• TensorFlow Lite
  • "Adds support for tflite_convert in 2.0."
  • "Remove lite.OpHint, lite.experimental, and lite.constant from 2.0 API."
• tf.contrib
  • Added Neural Turing Implementation as described in https://arxiv.org/abs/1807.08518.
  • Remove tf.contrib.timeseries dependency on TF 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 tf.Example.
• Add tf.contrib.bayesflow.hmc.
• Add tf.contrib.distributions.MixtureSameFamily.
• Make Dataset.shuffle() always reshuffles after each iteration by default.
• Add tf.contrib.bayesflow.metropolis_hastings.
• Add log_rate parameter to tf.contrib.distributions.Poisson.
• Extend tf.contrib.distributions.bijector API to handle some non-injective transforms.
• Java:
  • Generics (e.g., Tensor<Integer>) for improved type-safety (courtesy @andrewcmyers).
  • Support for multi-dimensional string tensors.
  • Support loading of custom operations (e.g.?many in tf.contrib) on Linux and OS X

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工具鏈-編譯選項：

Toolchains

• CUDNN_INSTALL_PATH, TENSORRT_INSTALL_PATH, NCCL_INSTALL_PATH, NCCL_HDR_PATH are deprecated. Use TF_CUDA_PATHS instead which supports a comma-separated list of base paths that are searched to find CUDA libraries and headers.
• TF code now resides in tensorflow_core and tensorflow is just a virtual pip package. No code changes are needed for projects using TensorFlow, the change is transparent

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