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.
For example, if you previously used
tf.contrib.data.Iterator.from_dataset(), both of those have been modified—the former has a new function signature, and the latter has been removed entirely and replaced with the
Dataset.make_initializable_iterator() function. TensorFlow’s documentation has other details about how to migrate away from tf.contrib.data and use the official tf.data library instead.
TensorFlow Estimator simplified
Many of the other additions build on TensorFlow’s reputation for convenience. A
train_and_evaluate function provides a simple way to run TensorFlow’s Estimator (used to automatically configure common model parameters) in a distributed fashion across a cluster. Also, TensorFlow’s built-in debugging system now lets you execute arbitrary Python code in the debugger’s command line, for quick-and-dirty inspection or modification.
TensorFlow CUDA and CuDNN support
TensorFlow 1.4 also updates support for CUDA and CuDNN, Nvidia’s libraries for GPU-accelerated data manipulation and deep learning, to versions 8 and 6, respectively. These aren’t the most recent versions, but TensorFlow’s developers state in the release notes, “We anticipate releasing TensorFlow 1.5 with CUDA 9 and CuDNN 7.”
Where to download TensorFlow 1.4
Installation instructions for TensorFlow on Ubuntu Linux, MacOS, and Microsoft Windows are available on the TensorFlow project page. Docker users can grab a pre-built TensorFlow Docker image directly from Docker Hub. You can also compile the sources into a binary; the sources are available on GitHub.
This story, “What’s new in TensorFlow machine learning” was originally published by InfoWorld