How does inference work in TensorFlow Lite’s interpreter?
The term inference refers to the process of running a TensorFlow Lite model on a device to make predictions based on input data. To perform inference with a TensorFlow Lite model, you must run it through an interpreter. TensorFlow Lite’s interpreter is designed to be snappy and fast.
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How to run inference in Java with TensorFlow?
The Java API for running inference with TensorFlow Lite is primarily designed for use with Android, so it is available as a dependency of the Android library: org.tensorflow:tensorflow-lite. In Java, you will use the Interpreter class to load a model and drive model inference. In many cases, this may be the only API you need.
How to run TensorFlow Lite model in C++?
Running a TensorFlow Lite model with C++ involves a few simple steps: Load the model into memory as a FlatBufferModel. Create an interpreter based on an existing FlatBufferModel. Set input tensor values. (Optionally resize the input tensors if the default sizes are not desired.) Invoke the inference. Read output tensor values.
How are string types encoded in TensorFlow Lite?
String types are also supported, but are encoded differently than primitive types. In particular, the shape of a chord tensioner dictates the number and arrangement of the chords in the tensioner, each element itself being a chord of variable length.
How are TensorFlow models used in mobile apps?
TensorFlow models can be used in applications running on mobile and embedded platforms. TensorFlow Lite and TensorFlow Mobile are two versions of TensorFlow for mobile devices with limited resources. TensorFlow Lite supports a subset of the functionality compared to TensorFlow Mobile.
What do you need to know about TensorFlow Lite?
TensorFlow Lite Support Library is a cross-platform library that helps to customize the model interface and build inference pipelines. It contains a variety of useful methods and data structures to perform pre- and post-processing and data conversion.