TensorFlow v1.1.0 發布,一個表達機器學習算法的接口

jopen 7年前發布 | 14K 次閱讀 機器學習 TensorFlow

 

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

  • 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.
  • Backport fixes and improvements from external keras.
  • Keras config file handling fix.

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