转载

TensorFlow v1.1.0-rc1 发布,一个表达机器学习算法的接口

TensorFlow 是一个表达机器学习算法的接口，并且是执行算法的实现框架。使用 TensorFlow 表示的计算可以在众多异构的系统上方便地移植，从移动设别如手机或者平板电脑到成千的GPU计算集群上都可以执行。该系统灵活，可以被用来表示很多的算法包括，深度神经网络的训练和推断算法，也已经被用作科研和应用机器学习系统在若干的计算机科学领域或者其他领域中，例如语言识别、计算机视觉、机器人、信息检索、自然语言理解、地理信息抽取和计算药物发现。

更新日志

Added Java API support for Windows.
Added tf.spectral module. Moved existing FFT ops to tf.spectral while
keeping an alias in the old location (tf.*).
Added 1D, 2D and 3D Fourier transform ops for real signals to tf.spectral.
Added a tf.bincount function.
Added Keras 2 API to contrib.
Added a new lightweight queue-like object - RecordInput.
Added tf.contrib.image.compose_transforms function.
Bring tf.estimator.* into the API. Non-deprecated functionality from tf.contrib.learn.Estimator is moved to tf.estimator.Estimator with cosmetic changes.
Docker images: TF images on gcr.io and Docker Hub are upgraded to ubuntu:16.04.
Added the following features to TensorFlow Debugger (tfdbg):
- Ability to inspect Python source file against TF ops and tensors (command print_source / ps)
- New navigation bar in Curses-based UI
- NodeStepper (command invoke_stepper) now uses intermediate tensor dumps. It also usesTensorHandles as direct feeds during successive cont calls for improved performance and reduced memory consumption.
Initial release of installation guides for Java, C, and Go.
TensorFlow 1.1.0 will be the last time we release a binary with Mac GPU support. Going forward, we will stop testing on Mac GPU systems. We continue to welcome patches that maintain Mac GPU support, and we will try to keep the Mac GPU build working.
The behavior of RNNCells is now stricter due to the transition towards making RNNCells act more like Keras layers.
- If an RNNCell is used twice in two different variable scopes, an error is raised describing how to avoid this behavior.
- If an RNNCell is used in a variable scope with existing conflicting variables, an error is raised showing that the RNNCell must be constructed with argument reuse=True.
Deprecated contrib/distributions pmf, pdf, log_pmf, log_pdf.
Moved bayesflow.special_math to distributions.
tf.contrib.tensor_forest.python.tensor_forest.RandomForestDeviceAssigner removed.
Changed some MVN classes and parameters:
- tf.contrib.distributions.MultivariateNormalFull replaced bytf.contrib.distributions.MultivariateNormalTriL.
- tf.contrib.distributions.MultivariateNormalCholesky replaced bytf.contrib.distributions.MultivariateNormalTriL
- tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev replaced
  by tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale
- tf.contrib.distributions.MultivariateNormalDiag arguments changed from mu, diag_stddevto log, scale_diag.
- tf.contrib.distributions.MultivariateNormalDiagPlusVDVT removed.
- tf.contrib.distributions.MultivariateNormalDiagPlusLowRank added.
Java: Support for loading models exported using the SavedModel API (courtesy @EronWright).
Go: Added support for incremental graph execution.
Fix a bug in the WALS solver when single-threaded.
Added support for integer sparse feature values in tf.contrib.layers.sparse_column_with_keys.
Fixed tf.set_random_seed(0) to be deterministic for all ops.
Stability improvements for the GCS file system support.
Improved TensorForest performance.
Added support for multiple filename globs in tf.matching_files.
LogMessage now includes a timestamp as beginning of a message.
Added MultiBox person detector example standalone binary.
Android demo: Makefile build functionality added to build.gradle to fully support building TensorFlow demo in Android on Windows.
Android demo: read MultiBox priors from txt file rather than protobuf.
Added colocation constraints to StagingArea.
sparse_matmul_op reenabled for Android builds.
Restrict weights rank to be the same as the broadcast target, to avoid ambiguity on broadcast rules.
Upgraded libxsmm to 1.7.1 and applied other changes for performance and memory usage.
Fixed bfloat16 integration of LIBXSMM sparse mat-mul.
Improved performance and reduce memory usage by allowing ops to forward input buffers to output buffers and perform computations in-place.
Improved the performance of CPU assignment for strings.
Speed up matrix * vector multiplication and matrix * matrix with unknown shapes.
C API: Graph imports now support input remapping, control dependencies, and returning imported nodes (see TF_GraphImportGraphDefWithReturnOutputs())
Multiple C++ API updates.
Multiple TensorBoard updates including:
- Users can now view image summaries at various sampled steps (instead of just the last step).
- Bugs involving switching runs as well as the image dashboard are fixed.
- Removed data download links from TensorBoard.
- TensorBoard uses a relative data directory, for easier embedding.
- TensorBoard automatically ignores outliers for domain calculation, and formats proportional values consistently.
Multiple tfdbg bug fixes:
- Fixed Windows compatibility issues.
- Command history now persists across runs.
- Bug fix in graph validation related to tf.while_loops.
Java Maven fixes for bugs with Windows installation.