Source | TensorFlow public number

TensorFlow version 1.11.0 is now available.

This article covers some of the major changes, major features and improvements, Bug fixes, and other changes.

Main features and improvements

Nvidia GPU:

  • Now (according to TensorFlow 1.11) the pre-built binaries are built for cuDNN 7.2 and TensorRT 4. Please see the installation guide: Installing TensorFlow on Ubuntu (www.tensorflow.org/install/ins…

Google Cloud TPU:

  • Experimental TF.data integration for Keras on Google Cloud TPU
  • Pilot/preview support for Eager Execution on Google Cloud TPU

Distributed strategy:

  • Added multi-GPU distributed policy support for TF.keras. Fit, Evaluate, and Predict support for distribution
  • Add distributed policy support and independent client support for multi-worker in Estimator. See [README] (github.com/tensorflow/…). For more details

Add C, C++, and Python functions to query the kernel

Major change

Keras:

  • The default values of RandomUniform, RandomNormal, and TruncatedNormal initializers for TF.keras have been changed to ensure that they match the default values in the external KERAS
  • Major change: Running the model.get_config() method for the Sequential model now returns the Config Dictionary (in line with other model instances) instead of the base layer configuration list

Bug fixes and other changes

C + + :

  • Change the SessionFactory: : NewSession signature, so it can give detailed information to the failure

Tf. Data:

  • Contrib.data.make_csv_dataset () removes the num_parallel_parser_calls parameter
  • If the parameter does not match a file, tf.data.dataset. List_files () will raise an exception during initialization
  • For clarity, rename the BigTable category to BigtableTable
  • Record Cloud Bigtable API usage
  • Add tf.contrib.data.reduce_dataset, which can be used to reduce a dataset to a single element
  • Generalization tf. Contrib. Data. Sliding_window_batch

INC:

  • The trig solution is improved in operation

Tf. Contrib:

  • For tf. Keras. The layers. LocallyConnected2D and tf keras. The layers. The LocallyConnected1D implementation for parameter was added. The new mode (implementation = 2) performs forward propagation as a product of a single dense matrix, which results in significant acceleration in some scenarios (but may result in performance degradation in some scenarios – see DocString). This option also allows the use of padding = same

  • New documentation has been added to explain the difference between tf.fill and tf.constant

  • IndexedDatasets were experimentally added

  • Add selective registration targets using the Lite Proto runtime

  • Add simple Tensor and DataType categories for TensorFlow Lite Java

  • Added support for bitcasting of uint32 and uint64

  • An Estimator subclass was added, which can be created from SavedModel (SavedModelEstimator)

  • Add leaf index mode as parameter

  • From the tf. Contrib. Image. The transform of the input is allowed in the different output shape

  • Change the state_size order of the StackedRNNCell to the natural order. To preserve the existing behavior, users can add reverse_STATE_ORDER = True when constructing StackedRNNCell

  • Deprecate self.test_session() in favor of self.session() or self.cached_session()

  • Direct import tensor.proto.h (pass import will be removed from tensor.h soon)

  • Estimator.train() now supports out-of-the-box tF.contrib. summary. * Each call to.train() now creates a separate TFEvents file and does not reuse the shared file

  • Fixed shrinkage performance of FTRL optimizer L2: Gradient of L2 shrinkage term should not terminate in accumulator

  • Fix toCO compilation/execution bug on Windows

  • The GoogleZoneProvider category was added to detect where parts of the TensorFlow program run in the Google Cloud Engine

  • It is now safe to call the TF_Delete * function of any C API on NullPTR

  • Log some error messages on Android

  • The FakeQuant numbers in TFLite were matched to improve the accuracy of TFLite quantitative reasoning model

  • Check optional bucket locations for GCS file systems

  • StringSplitOp and StringSplitV2Op have been enhanced

  • The performance of regular expression replacement operations is improved

  • If if.write() fails, TFRecordWriter will now report an error

  • The TPU: TPUClusterResolvers cluster parser will provide more detailed and useful error messages

  • It is not recommended to use the SavedModelBuilder method to add the MetaGraphs legacY_init_op parameter. Use the equivalent main_op parameter instead. We now explicitly check for a single main_OP or legacy_init_op when we build SavedModel, whereas previously main_op was checked only at load time

  • The protocol for Estimator training can now be configured in RunConfig

  • The performance of solving trigonometric numbers is improved

  • The API of RNN unit of TF and Keras is unified. A new get_initial_state() method was added for the RNN cells of Keras and TF, which will replace the existing Zero_state () method in the future

  • Updated initialization of variables in Keras

  • Update “constrained_optimization” in tensorflow/contrib

  • Lift tree algorithm: Add pruning mode

  • Tf.train.Checkpoint By default, old checkpoints are not deleted

  • TFDBG: When debugging, the Tensor data cached will take up to 100 GB of disk space. Allow the environment variable TFDBG_DISK_BYTES_LIMIT to be added to adjust this upper limit

Thanks to our contributors

This launch was made possible by the contributions of many people at Google: Aapeli, adoda, Ag Ramesh, Amogh Mannekote, Andrew Gibiansky, Andy Craze, Anirudh Koul, Aurelien Geron, Avijit, Avijit-Nervana, Ben, Benjamin H. Myara, Bhack, Brett Koonce, Tianqi Zhang, Xiaofei Zhang……

Finally, please click on the “www.tensorflowers.cn/t/6838”, check out…