November 15th, 2017

What’s new in TensorFlow machine learning

Data Analytics, Development Tools, Java App Dev, Open Source, others, Programing, by admin.

TensorFlow, Google’s contribution to the world of machine learning and data science, is a general framework for quickly developing neural networks. Despite being relatively new, TensorFlow has already found wide adoption as a common platform for such work, thanks to its powerful abstractions and ease of use.

TensorFlow 1.4 API additions

TensorFlow Keras API

The biggest changes in TensorFlow 1.4 involve two key additions to the core TensorFlow API. The tf.keras API allows users to employ the Keras API, a neural network library that predates TensorFlow but is quickly being displaced by it. The tf.keras API allows software using Keras to be transitioned to TensorFlow, either by using the Keras interface permanently, or as a prelude to the software being reworked to use TensorFlow natively.

TensorFlow Dataset API

Another addition to the core TensorFlow APIs is the tf.data or Dataset API, originally available as a contributed API but now officially supported. The Dataset API provides a set of abstractions for creating and re-using input pipelines—potentially complex datasets gleaned from one or more sources, with each element transformed as needed. Datasets can also have specific functions associated with iterations through the set—for instance, if you’re making multiple training passes through a dataset and need different behaviors on each pass.

TensorFlow Dataset API compatibility issue

If you have already been using the contributed version of the data API from the previous version of TensorFlow (tf.contrib.data), be warned that the official tf.data API isn’t perfectly backward compatible. The total number of changes isn’t large, but most of them are in strategic, commonly used functions, so there’s a fair chance existing code will break.

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