RED HAT Comments - Answer Overflow

RED HAT

•Created by Enthernet Code on 8/13/2024 in #middleware-and-os

Efficiently Converting and Quantizing a Trained Model to TensorFlow Lite

Hey @Enthernet Code good job on getting this far with your project, The method you used to convert your model to TensorFlow Lite is perfectly valid and commonly used. However, if you’re concerned about the model size and performance on a microcontroller, quantization is definitely something you should look into. Quantization helps by reducing the precision of the weights and biases, most times from 32-bit floats to 8-bit integers, which reduces the model size and can significantly speed up inference, especially on hardware with limited resources like microcontrollers. You can apply quantization during the conversion process like this:

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

with open('xray_model_quantized.tflite', 'wb') as f:
    f.write(tflite_model)

print("Model successfully converted and quantized to TensorFlow Lite!")

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

with open('xray_model_quantized.tflite', 'wb') as f:
    f.write(tflite_model)

print("Model successfully converted and quantized to TensorFlow Lite!")

5 replies

Gaming

Programming