Support for Mobile Devices

TensorFlow's developers avoid restricting the use of the framework to large computing environments. One intended application scenario for TensorFlow relies on mobile and embedded systems, and it can even be executed in browsers, thanks to its JavaScript connection.

Especially for mobile devices, the developers have come up with TensorFlow Lite (see the associated article in this issue). This lean version can execute developed models but does not handle the task of automatically expanding the neural network. Ultimately, it becomes clear at the program level that the two steps are logically distinct. Once a pattern has been defined, it processes a task for given material as an independent model within the AI framework. The machine learning part must be considered separately and might not even be necessary. Running on mobile devices, TensorFlow Lite simply does not have the resources needed for the neural network part, which is why the product is limited to the remaining task.

Extensions with TensorFlow Hub

Google is known to be committed to standardization, even if it would prefer to make its own standards the worldwide norm. For the developers of machine learning applications, the TensorFlow developers have therefore come up with the TensorFlow Hub. Any developer can contribute to this online directory of machine learning modules by uploading their own code.

Google anticipates several advantages: First, individual developers can work with smaller datasets if they find optimized code and matching data on the TensorFlow Hub. Additionally, common directories for corresponding modules help set standards: If TensorFlow Hub finds a module that performs a certain task well and reliably, developers can fall back on it instead of reinventing the wheel, which in turn reduces susceptibility to errors. Newcomers to machine learning will also tend to find it easier to access code on the Hub, enabling faster learning.

Keras for Even Faster Entry

Keras, another library for machine learning, follows in the wake of TensorFlow. Keras also was written by a Google employee, François Chollet, who published the first version in 2015.

Originally, Keras and TensorFlow had nothing to do with each other. Although Keras is also a library for deep machine learning, it differs from TensorFlow in one striking aspect: Keras does not itself implement an engine and instead sees itself primarily as a front end for machine learning engines – like TensorFlow. Since version 1.4, TensorFlow offers support for the Keras API, which is developed independently of TensorFlow (Figure 1). A merge was explicitly ruled out, not least because Keras would have had to sacrifice some of its functionality.

Figure 1: TensorFlow and Keras is a team that allows even newcomers to get started with AI and deep learning. © TensorFlow

In combination with TensorFlow, Keras still offers very useful additional functions, but the library is uncompromisingly trimmed for simplicity and usability. If you look at TensorFlow Hub, you can see that the idea has caught on; a fair share of the samples and templates available there not only need TensorFlow, but also Keras. Even users who have little or no experience with AI and deep learning will quickly achieve initial success with Keras. The library is therefore ideally suited for education.