TensorFlow Lite is a lightweight, production-grade, and cross-platform deep learning framework that has its main utility in deploying machine learning models on mobile and edge devices. It allows executing machine learning models on low-latency, computationally less powerful devices, making TensorFlow models portable to applications developed for Android and iOS.
In this article, we will cover the essential steps in converting TensorFlow models to TensorFlow Lite models and how we can deploy them on mobile devices. We will delve into practical code snippets and examples to enhance our understanding.
Understanding TensorFlow Lite
TensorFlow Lite helps in optimizing models so they can run directly on mobile devices. This involves model conversion, optimization, and native APIs for integration into mobile apps. It becomes an essential tool when you require deep learning capabilities on mobile apps while ensuring optimum resource usage.
Converting a TensorFlow Model to TensorFlow Lite
The first step to deploying models on mobile devices involves converting existing TensorFlow models to TensorFlow Lite format. Here's a basic example on how you perform the conversion:
import tensorflow as tf
# Create or load a TensorFlow model
model = tf.keras.models.load_model('path_to_your_model')
# Convert the model to the TensorFlow Lite format
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
# Save the converter model as tflite file
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
This snippet covers converting a Keras model saved in TensorFlow format to a .tflite model that is deployable on a mobile device.
Optimizing the Model
Optimizations are crucial since mobile devices have different hardware limitations. TensorFlow Lite supports various optimizations like full integer quantization. Here’s a code specimen to use post-training quantization:
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model_quant = converter.convert()
# Save the optimized model as tflite file
with open('model_quant.tflite', 'wb') as f:
f.write(tflite_model_quant)
This optimization technique reduces model size significantly while maintaining relatively good performance.
Deploying on Android
Here is an example of how you can implement the TensorFlow Lite model in an Android application, specifically using Kotlin:
// Load the TFLite model from the asset folder
val tfliteModel: ByteBuffer = FileUtil.loadMappedFile(this, "model.tflite")
val interpreter = Interpreter(tfliteModel)
// Allocate memory for the model's input and output.
val input: FloatArray = floatArrayOf(/* Input data */)
val output = FloatArray(1) // assuming output is a single float value
// Run inference
interpreter.run(input, output)This Kotlin snippet illustrates ways to embed TFLite models using Android facilities.
Deploying on iOS
Here is a sample of how you load and run a `.tflite` model on an iOS app using Swift:
let modelPath = Bundle.main.path(forResource: "model", ofType: "tflite")!
let tfliteOptions = Interpreter.Options()
let interpreter = try! Interpreter(modelPath: modelPath, options: tfliteOptions)
interpreter.allocateTensors()
var inputData: [Float] = [/* Input data */]
let inputTensor = try! interpreter.input(at: 0)
try! interpreter.copy(&inputData, toInputAt: 0)
try! interpreter.invoke()In this Swift developer guide, the steps involve setting up the model with TensorFlow Interpreter and running inference inside an iOS application.
Complete deployment involves preparing the app package and utilizing platform tools like Android Studio or Xcode for compiling and running apps equipped with TensorFlow Lite models on actual devices.
Conclusion
TensorFlow Lite significantly enhances the scope of deep learning by making models executable on mobile devices. Its framework supports both Android and iOS through dedicated APIs and efficient model conversion techniques. Optimizing models before deployment ensures an ideal application performance in highly resource-constrained environments.
