TensorFlow v1.1.0 发布,一个表达机器学习算法的接口
jopen 8年前
<p style="text-align: center;"><strong><img alt="" src="https://simg.open-open.com/show/326eaa9a539ec5575022ac39de585d3d.png" /></strong></p> <p> </p> <p>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。</p> <p style="text-align: center;"><a href="https://simg.open-open.com/show/4a67e12961d71d510c83c2aa35a8febb.gif"><img alt="" src="https://simg.open-open.com/show/4a67e12961d71d510c83c2aa35a8febb.gif" /></a></p> <h2>更新日志</h2> <ul> <li>Java: Support for loading models exported using the SavedModel API (courtesy <a href="/misc/goto?guid=4959004934800948279">@EronWright</a>).</li> <li>Go: Added support for incremental graph execution.</li> <li>Fix a bug in the WALS solver when single-threaded.</li> <li>Added support for integer sparse feature values in <code>tf.contrib.layers.sparse_column_with_keys</code>.</li> <li>Fixed <code>tf.set_random_seed(0)</code> to be deterministic for all ops.</li> <li>Stability improvements for the GCS file system support.</li> <li>Improved TensorForest performance.</li> <li>Added support for multiple filename globs in <code>tf.matching_files</code>.</li> <li><code>LogMessage</code> now includes a timestamp as beginning of a message.</li> <li>Added MultiBox person detector example standalone binary.</li> <li>Android demo: Makefile build functionality added to build.gradle to fully support building TensorFlow demo in Android on Windows.</li> <li>Android demo: read MultiBox priors from txt file rather than protobuf.</li> <li>Added colocation constraints to <code>StagingArea</code>.</li> <li><code>sparse_matmul_op</code> reenabled for Android builds.</li> <li>Restrict weights rank to be the same as the broadcast target, to avoid ambiguity on broadcast rules.</li> <li>Upgraded libxsmm to 1.7.1 and applied other changes for performance and memory usage.</li> <li>Fixed bfloat16 integration of LIBXSMM sparse mat-mul.</li> <li>Improved performance and reduce memory usage by allowing ops to forward input buffers to output buffers and perform computations in-place.</li> <li>Improved the performance of CPU assignment for strings.</li> <li>Speed up matrix * vector multiplication and matrix * matrix with unknown shapes.</li> <li>C API: Graph imports now support input remapping, control dependencies, and returning imported nodes (see <code>TF_GraphImportGraphDefWithReturnOutputs()</code>)</li> <li>Multiple C++ API updates.</li> <li>Multiple TensorBoard updates including: <ul> <li>Users can now view image summaries at various sampled steps (instead of just the last step).</li> <li>Bugs involving switching runs as well as the image dashboard are fixed.</li> <li>Removed data download links from TensorBoard.</li> <li>TensorBoard uses a relative data directory, for easier embedding.</li> <li>TensorBoard automatically ignores outliers for domain calculation, and formats proportional values consistently.</li> </ul> </li> <li>Multiple tfdbg bug fixes: <ul> <li>Fixed Windows compatibility issues.</li> <li>Command history now persists across runs.</li> <li>Bug fix in graph validation related to <code>tf.while_loops</code>.</li> </ul> </li> <li>Java Maven fixes for bugs with Windows installation.</li> <li>Backport fixes and improvements from external keras.</li> <li>Keras config file handling fix.</li> </ul> <h2>下载</h2> <ul> <li><a href="/misc/goto?guid=4959007285816495073" rel="nofollow"><strong>Source code</strong> (zip)</a></li> <li><a href="/misc/goto?guid=4959007285935639849" rel="nofollow"><strong>Source code</strong> (tar.gz)</a></li> </ul> <p>本站原创，转载时保留以下信息：<br /> 本文转自：深度开源（open-open.com）<br /> 原文地址：<a href="http://www.open-open.com/news/view/3db99bb2">http://www.open-open.com/news/view/3db99bb2</a></p>