Can You Use Apple Swift To Create Cross-Platform Apps? And being able to write code and test it more efficiently will ultimately save developers like us time and cost. Writing code is easier and faster than ever with Swift, making you more productive. For iOS developers, it’s a no-brainer to use Swift instead of Objective-C. The preferred way to run inference on a model is to use signatures -Īvailable for models converted starting Tensorflow 2.Since WWDC ( Apple’s Worldwide Developers Conference) and Apple’s announcement of Swift, I’ve become increasingly impressed with the new language. Interpreter, it must remain unchanged for the whole lifetime of the ![]() If you use MappedByteBuffer to initialize an In both cases, you must provide a valid TensorFlow Lite model or the API throws Or with a MappedByteBuffer: public MappedByteBuffer mappedByteBuffer) You can initialize an Interpreter using a. In many cases, this may be the only API you need. In Java, you'll use the Interpreter class to load a model and drive model The Java API for running an inference with TensorFlow Lite is primarily designedįor use with Android, so it's available as an Android library dependency: (Optionally resize input tensors if the predefinedįollowing sections describe how these steps can be done in each language.
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