TensorFlow Lite is designed for mobile and embedded devices, and it allows you to run TensorFlow models on these devices with high efficiency. This guide provides an overview of how to deploy TensorFlow Lite models for production use.
Overview
TensorFlow Lite supports various platforms, including Android, iOS, and edge devices. The deployment process involves converting your TensorFlow model to the TensorFlow Lite format, optimizing it for the target platform, and integrating it into your application.
Conversion
The first step in deploying a TensorFlow Lite model is to convert it from the TensorFlow format to the TensorFlow Lite format. You can use the TensorFlow Lite Converter for this purpose. The converter supports various input formats, including TensorFlow, TF.js, and Keras.
Optimization
After converting your model to TensorFlow Lite format, you can optimize it for the target platform. Optimization can significantly improve the model's performance and reduce its size.
Integration
Once your model is converted and optimized, you can integrate it into your application. TensorFlow Lite provides a C++ API, a Java/Kotlin API, and a Python API for different platforms.
Example
Here's an example of how to integrate a TensorFlow Lite model into an Android application:
// Load the model
try {
File modelFile = new File(getFilesDir(), "model.tflite");
Interpreter interpreter = new Interpreter(modelFile);
} catch (IOException e) {
e.printStackTrace();
}