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[标题党] tensorflow 1.3.0rc1 发布

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发表于 2017-7-28 20:55:10 | 显示全部楼层 |阅读模式
Author: Google Inc.
Home Page: http://tensorflow.org/
Keywords: tensorflow tensor machine learning
License: Apache 2.0
Release 1.3.0
Major Features and Improvements
Added canned estimators to Tensorflow library. List of added estimators: DNNClassifier, DNNRegressor, LinearClassifer, LinearRegressor, DNNLinearCombinedClassifier, DNNLinearCombinedRegressor.
All our prebuilt binaries have been built with cuDNN 6.
Adds a file cache to the GCS filesystem with configurable max staleness for file contents. This permits caching of file contents across close/open boundaries.
Added an axis parameter to tf.gather.
Added a c**tant_values keyword argument to tf.pad.
Adds Dataset.interleave transformation.
Add ConcatenateDataset to concatenate two datasets.
Added Mobilenet support to TensorFlow for Poets training script.
Adds a block cache to the GCS filesystem with configurable block size and count.
SinhArcSinh bijector added.
Added Dataset.list_files API.
Introduces new operati** and Python bindings for the Cloud TPU.
Adding TensorFlow-iOS CocoaPod for symmetry with tensorflow-android.
Introduces base implementati** of ClusterResolvers.
Unify memory representati** of TensorShape and PartialTensorShape. As a c**equence, tensors now have a maximum of 254 dimensi**, not 255.
Changed references to LIBXSMM to use version 1.8.1.
TensorFlow Debugger (tfdbg): Display summaries of numeric tensor values with the -s flag to command print_tensor or pt.
Initial release of the statistical distribution library tf.distributi**.
GPU kernels and speed improvements for for unary tf.where and tf.nn.top_k.
Monotonic Attention wrappers added to tf.contrib.seq2seq.
Breaking Changes to the API
tf.RewriterConfig was removed from the Python API after being available in 1.2 release candidates (it was never in an actual release). Graph rewriting is still available, just not as tf.RewriterConfig. Instead add an explicit import.
Breaking change to tf.contrib.data.Dataset APIs that expect a nested structure. Lists are now converted to tf.Tensor implicitly. You may need to change uses of lists to tuples in existing code. In addition, dicts are now supported as a nested structure.



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