DevHeads IoT Integration Server•5mo ago

Efficiently Converting and Quantizing a Trained Model to TensorFlow Lite

Hey guys in contiuation of my project Disease Detection from X-Ray Scans Using TinyML, i am done training my model and would like to know the most easiest and efficient method for converting the trained model to TensorFlow Lite for deployment on a microcontroller, i have converted it using TensorFlow Lite's converter to convert it to a .tflite file but dont know if its the best method, and also how can i quantinize it to reduce the model size and improve inference speed

Solution:

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:...

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3 Replies

nour_oud•5mo ago

converter.py

Solution

RED HAT•5mo ago

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!")

Alien Queen•5mo ago

TensorFlow Lite also supports other types of quantization, such as full integer quantization or float16 quantization. If you want more control over how the quantization is applied, you can specify the type of quantization during conversion. For instance, if you want to use full integer quantization, you can modify your code to be like this

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.int8]
tflite_model = converter.convert()

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

print("Model successfully converted with full integer quantization!")

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.int8]
tflite_model = converter.convert()

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

print("Model successfully converted with full integer quantization!")

This will convert both the weights and activations to int8, which is often the most efficient for microcontrollers. Although quantization the accuracy of your model may be affected so you would ahv to test it again to ensure it meets your accuracy requirements, you can test the original and quantized to see if theres any notable difference in model capability.

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Efficiently Converting and Quantizing a Trained Model to TensorFlow Lite