[Tensorflow] Tensorflow API r1.8.0

Release 1.8.0

Major Features And Improvements

• Can now pass tf.contrib.distribute.MirroredStrategy() to tf.estimator.RunConfig() to run an Estimator model on multiple GPUs on one machine.
• Add tf.contrib.data.prefetch_to_device(), which supports prefetching to GPU memory.
• Added Gradient Boosted Trees as pre-made Estimators: BoostedTreesClassifier, BoostedTreesRegressor.
• Add 3rd generation pipeline config for Cloud TPUs which improves performance and usability.
• tf.contrib.bayesflow is moving out to it's own repo.
• Added tf.contrib.{proto,rpc} to allow generic proto parsing and RPC communication.

Bug Fixes and Other Changes

• tf.data:
  • Add tf.contrib.data.prefetch_to_device, which enables prefetching dataset elements to GPU memory.
  • Add tf.contrib.data.AUTOTUNE, which allows the tf.data runtime to automatically tune the prefetch buffer sizes based on your system and environment.
  • Add tf.contrib.data.make_csv_dataset for building datasets of CSV files.
• Eager Execution:
  • With eager execution Datasets can now be used as standard python iterators (for batch in dataset:). Both Dataset.__iter__() and Dataset.make_one_shot_iterator() can now be used to create iterators when eager execution is enabled.
  • Automatic device placement has been enabled (i.e., use a GPU if available automatically, without requiring an explicit with tf.device(“/gpu:0”)) (Fixes #14133)
  • tf.GradientTape has moved out of contrib.
• tf.keras:
  • Added the fashion mnist dataset.
  • New data preprocessing functions: image/random_brightness, sequence/TimeseriesGenerator, and text/hashing_trick.
• Accelerated Linear Algebra (XLA):
  • Select and scatter in reference util and evaluator now use lexicographical order to break ties.
• TensorFlow Debugger (tfdbg) CLI:
  • During tensor-filter operations, allow exclusion of nodes by regular expressions.
  • Fix spurious background colors in some text terminals.
• tf.contrib:
  • Add meta-distribution BatchReshape which reshapes batch dimensions.
  • tf.contrib.layers.recompute_grad works for explicit gradient checkpointing on TPU.
  • Add tf.contrib.framework.argsort.
  • Allow DNNBoostedTreeCombinedEstimator to work with core versions of feature columns and losses.
  • Add non-linear image warping ops: tf.contrib.image.sparse_image_warp, tf.contrib.image.dense_image_warp, and tf.contrib.image.interpolate_spline.
  • Fix bug in tf.contrib.opt.MultitaskOptimizerWrapper where types of tensors were mismatched.
• Other:
  • Low-level graph construction now calls the TensorFlow C API. This change should be invisible to most users, but can be disabled by setting the environment variable TF_C_API_GRAPH_CONSTRUCTION=0 in this release. Future releases will remove the ability to disable this change. Please file a bug if you find yourself using this escape hatch.
  • Add description of shapes and a pointer to tutorial notebook in tf.distributions.Distribution.
  • Update scatter operations:
    • Add tf.scatter_min and tf.scatter_max
    • Extend scatter operations to work with a scalar update parameter.
  • Move cuDNN RNN ops to core for use in TensorFlow codebase only.
  • Add float64 support for Conv2d, Conv2dBackpropInput, and Conv2dBackpropFilter.
  • Add float64 support for AvgPool/AvgPoolGrad.
  • Make graph name scope thread local so that they work correctly in multi-threaded environments.
  • Update nsync synchronization library to avoid slow primitives on Linux.
  • Removed need to put nsync/public on C include path when building custom ops.
  • Add tf.image.psnr, tf.image.ssim, tf.image.ssim_multiscale, tf.image.image_gradients, tf.image.sobel_edges.
  • Add links to https://js.tensorflow.org.
  • Fix non-uniformity of orthogonal matrices.
  • Fix bug where multi-image Estimator eval summaries were not displayed correctly.