TensorFlow 1.9.0 is here! Novice guide new revision

TensorFlow 1.9.0 is here!

Francois Chollet, a Google brain researcher and Keras author, speaks highly of this version. “TF users and not TF users should check it out: TF has made tremendous progress recently,” he says. This is a big step towards the future of ML.”

So what exactly does this update involve?

The first is support for Keras. TensorFlow is a back end to Keras, a high-level API for deep learning that combines the work required to create and train models into modules. In TensorFlow, it is called tf.keras.

Now, TensorFlow’s new Guide takes you through Keras and includes a detailed Keras Guide.

Meanwhile, Keras in TensorFlow itself has improved. Tf. Keras upgraded to keras 2.1.6 API, added tf. Keras. The layers. The CuDNNGRU and tf keras. The layers. CuDNNLSTM, respectively for GRU helped to achieve faster and faster LSTM is done.

Main features and improvements

• Updated document Tf.Keras: Getting started and Programmer’s Guide page based on the new Keras.

• Update tf.keras for Keras 2.1.6 API.

• Add tf. Keras. The layers. CuDNNGRU and tf keras. The layers. CuDNNLSTM layer.

• Add support and loss for the core function column to the Gradient Vauxtree estimator.

• The Python interface for TFLite optimized converters has been extended, and the command line interface (TOCO, tflite_convert) is once again included in standard PIP installations.

Improve data loading and text processing by:

• tf.decode_compressed

• tf.string_strip

• tf.strings.regex_full_match

Added experimental support for the new prefabricated estimator:

• tf.contrib.estimator.BaselineEstimator

• tf.contrib.estimator.RNNClassifier

• tf.contrib.estimator.RNNEstimator

The Bijector API supports Bijectors with new API changes.

Breakthrough change

• If you open an empty variable range, use variable_scope(tf.get_variable_scope()…) Replace variable_scope (“, etc.) .

• The header used to build custom operations has been moved from site-Packages/external to site-Packages/tensorflow/include/external.

Bug fixes and other changes

Tfe.Net work is deprecated, please use tf.keras.model.

The hierarchical variable name has changed in the following conditions:

• Use tf.keras.layers to customize variable ranges.

• Use tf.layers in a subclass of tF.keras.model.

Tf. Data:

• Dataset.from_generator() now accepts a list of args to create nested generators.

• When shuffle=Falsea or a seed passes, Dataset.list_files() yields a definite result.

• Tf.contrib.data.sample_from_datasets () and tf.contrib.data.choose_from_datasets() make it easier to sample or deterministic select elements from multiple datasets.

• Contrib.data.make_csv_dataset () now supports referencing newlines in strings and removing two infrequently used arguments.

• (C ++) DatasetBase::DebugString() is now const.

• (C + +) DatasetBase: : MakeIterator () has been renamed DatasetBase: : MakeIteratorInternal ().

• The (C ++) IteratorBase::Initialize() method was added to support raising errors during iterator construction.

Eager Execution:

Added the ability to pause gradienttape.stop_recording with tF.gradienttape.stop_recording. Updated documentation, introductory notes.

Tf. Keras:

• Move the Keras code out of the _IMPl folder and delete the API files.

• Tf.keras.model. save_weights is now saved in TensorFlow format by default.

• Enable the dataset iterator to be passed to the tF.keras.modeltraining/eval method.

TensorFlow Debugger (TFDBG)

Fixed an issue where the TensorBoard debugger plug-in could not handle the total source file size exceeding the gRPC message size limit (4 MB).

Tf. Contrib:

• Tf. Contrib. Framework. ResourceVariable zero_initializer support.

• Add “constrained_optimization” to tensorflow/contrib.

  
  

other

• Add GCS configuration operations.

• Change the signature MakeIterator to enable propagation of error state.

• KL divergence of two Dirichlet distributions.

• For some reads beyond EOF, the GcsFileSystem behaves more consistently.

• Update the tF.SCAN benchmark to match the scope of the eager and Graph patterns.

• Fixed tf.reduce_Prod gradient bug for complex dtypes.

• Allow ‘. ‘in variables (for example, “hparams.parse (‘ ab = 1.0’)”), which previously resulted in an error. This will correspond to the name of the property with an embedded ‘. ‘. Symbols (e.g. ‘a.b’) that can only be accessed indirectly (e.g., through getattr and setattr). To set it up, the user first needs to explicitly add the variable to the hparam object (for example, “hparams.add_hparam (name = ‘a.b’, value = 0.0)”).

• Baseline of TF.Scan in graph and eager mode.

• Added support for FFT, FFT2D, FFT3D, IFFT, IFFT2D and IFFT3D complex128.

• Makes IDS unique nm.embedding_lookup_SPARSE, which helps reduce RPC calls to look for embeds when duplicate ids exist in the batch.

• The indicator column is supported in the VTREE.

• Prevents TF.gradients () from propagating back through integer tensors.

• Add LinearOperator [1D, 2D, 3D] Circulant to tensorFlow.linalg.

• Conv3D, Conv3DBackpropInput, and Conv3DBackpropFilter are now available with arbitrary support.

• Add tf.train.Checkpoint to read and write object-based checkpoints.

• Added LinearOperatorKronecker, no dense implementation of the kronecker product.

• Allow the LinearOperator to broadcast.

• SavedModelBuilder now deduplicates resource names that point to files with the same base name and the same content. Note that if resources that previously had the same name but different content overwrite each other, it may cause new resource files to be included in SavedModels.

More features of the new version can be viewed through this portal:

https://github.com/tensorflow/tensorflow/releases/tag/v1.9.0

There’s also a beginner’s guide to freshening up:

https://www.tensorflow.org/tutorials/

Once from the entry to give up students can start again ~