TensorFlow v1.1.0-rc1 發布,一個表達機器學習算法的接口

TensorFlow 是一個表達機器學習算法的接口，并且是執行算法的實現框架。使用 TensorFlow 表示的計算可以在眾多異構的系統上方便地移植，從移動設別如手機或者平板電腦到成千的GPU計算集群上都可以執行。該系統靈活，可以被用來表示很多的算法包括，深度神經網絡的訓練和推斷算法，也已經被用作科研和應用機器學習系統在若干的計算機科學領域或者其他領域中，例如語言識別、計算機視覺、機器人、信息檢索、自然語言理解、地理信息抽取和計算藥物發現。

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.

下載

• Source code (zip)
• Source code (tar.gz)

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