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TensorFlow 1.11.0 已经正式发布下载，增加了一些新支持及修复错误，它是谷歌的第二代机器学习系统，在某些基准测试中，TensorFlow 的表现比第一代的 DistBelief 快了 2 倍。
TensorFlow 1.11.0主要更新包括： 1.Nvidia GPU: 现在（根据 TensorFlow 1.11）预构建的二进制文件是针对 cuDNN 7.2 和 TensorRT 4 构建的。 2.Google Cloud TPU: 针对 Google Cloud TPU 上 Keras 的实验性 tf.data 集成。 实验/预览支持 Google Cloud TPU 上的热切执行。 3.DistributionStrategy: 在 tf.keras 中添加多 GPU 的 DistributionStrategy 支持。用户现在可以使用拟合、评估和预测在多个 GPU 上分发他们的模型。 在 Estimator 中添加多 worker DistributionStrategy 和独立客户端支持。 4.添加 C、C ++ 和 Python 函数以查询内核
大的变化 Keras: The default values for tf.keras RandomUniform, RandomNormal, and TruncatedNormal initializers have been changed to match those in external Keras. Breaking change: model.get_config() on a Sequential model now returns a config dictionary (consistent with other Model instances) instead of a list of configs for the underlying layers.
Bug修复和其它更改 1.C++: Changed the signature of SessionFactory::NewSession so that it can return a meaningful error message on failure. 2.tf.data: Remove num_parallel_parser_calls argument from tf.contrib.data.make_csv_dataset(). [tf.data] Remove num_parallel_parser_calls argument from tf.contrib.data.make_csv_dataset(). tf.data.Dataset.list_files() raises an exception at initialization time if the argument matches no files. Renamed BigTable class to BigtableTable for clarity Document use of the Cloud Bigtable API Adding tf.contrib.data.reduce_dataset which can be used to reduce a dataset to a single element. Generalization of tf.contrib.data.sliding_window_batch. 3.INC: Runtime improvements to triangular solve. 4.tf.contrib: Add an implementation argument to tf.keras.layers.LocallyConnected2D and tf.keras.layers.LocallyConnected1D. The new mode (implementation=2) performs forward pass as a single dense matrix multiplication, allowing dramatic speedups in certain scenarios (but worse performance in others - see docstring). The option also allows to use padding=same. Add documentation clarifying the differences between tf.fill and tf.constant. Add experimental IndexedDatasets. Add selective registration target using the lite proto runtime. Add simple Tensor and DataType classes to TensorFlow Lite Java Add support for bitcasting to/from uint32 and uint64. Added a subclass of Estimator that can be created from a SavedModel (SavedModelEstimator). Adds leaf index modes as an argument. Allow a different output shape from the input in tf.contrib.image.transform. Change the state_size order of the StackedRNNCell to be natural order. To keep the existing behavior, user can add reverse_state_order=True when constructing the StackedRNNCells. Deprecate self.test_session() in favor of self.session() or self.cached_session(). Directly import tensor.proto.h (the transitive import will be removed from tensor.h soon) Estimator.train() now supports tf.contrib.summary.* summaries out of the box; each call to .train() will now create a separate tfevents file rather than re-using a shared one. Fix FTRL L2-shrinkage behavior: the gradient from the L2 shrinkage term should not end up in the accumulator. Fix toco compilation/execution on Windows GoogleZoneProvider class added to detect which Google Cloud Engine zone tensorflow is running in. It is now safe to call any of the C API's TF_Delete* functions on nullptr Log some errors on Android to logcat Match FakeQuant numerics in TFLite to improve accuracy of TFLite quantized inference models. Optional bucket location check for the GCS Filesystem. Performance enhancements for StringSplitOp & StringSplitV2Op. Performance improvements for regex replace operations. TFRecordWriter now raises an error if .write() fails. TPU: More helpful error messages in TPUClusterResolvers. The legacy_init_op argument to SavedModelBuilder methods for adding MetaGraphs has been deprecated. Please use the equivalent main_op argument instead. As part of this, we now explicitly check for a single main_op or legacy_init_op at the time of SavedModel building, whereas the check on main_op was previously only done at load time. The protocol used for Estimator training is now configurable in RunConfig. Triangular solve performance improvements. Unify RNN cell interface between TF and Keras. Add new get_initial_state() to Keras and TF RNN cell, which will use to replace the existing zero_state() method. Update initialization of variables in Keras. Updates to "constrained_optimization" in tensorflow/contrib. boosted trees: adding pruning mode tf.train.Checkpoint does not delete old checkpoints by default. tfdbg: Limit the total disk space occupied by dumped tensor data to 100 GBytes. Add environment variable TFDBG_DISK_BYTES_LIMIT to allow adjustment of this upper limit.
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