DevHeads IoT Integration Server•17mo ago

Efficiently Converting and Quantizing a Trained Model to TensorFlow Lite

Hey guys in contiuation of my project

Disease Detection

Disease Detection

from

X-Ray

X-Ray

Scans Using

TinyML

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

TensorFlow Lite

for deployment on a microcontroller, i have converted it using

TensorFlow Lite's

TensorFlow Lite's

converter to convert it to a

.tflite

.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

method

you used to

convert

convert

your

model

model

TensorFlow Lite

TensorFlow Lite

is perfectly

valid

valid

and commonly used. However, if you’re concerned about the

model size

model size

and

performance

performance

on a

microcontroller

microcontroller

quantization

quantization

is definitely something you should look into.

Quantization

Quantization

helps by reducing the

precision

precision

of the weights and biases, most times from

32-bit

32-bit

floats to

8-bit

8-bit

integers, which reduces the

model

model

size and can significantly speed up

inference

inference

, especially on

hardware

hardware

with limited resources like

microcontrollers

microcontrollers

.

You can apply

quantization

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!")

Jump to solution

nour_oud•8/13/24, 11:40 PM

converter.py249B

Solution

RED HATAPP•8/16/24, 4:07 PM

Hey @Enthernet Code good job on getting this far with your project, The

method

method

you used to

convert

convert

your

model

model

TensorFlow Lite

TensorFlow Lite

is perfectly

valid

valid

and commonly used. However, if you’re concerned about the

model size

model size

and

performance

performance

on a

microcontroller

microcontroller

quantization

quantization

is definitely something you should look into.

Quantization

Quantization

helps by reducing the

precision

precision

of the weights and biases, most times from

32-bit

32-bit

floats to

8-bit

8-bit

integers, which reduces the

model

model

size and can significantly speed up

inference

inference

, especially on

hardware

hardware

with limited resources like

microcontrollers

microcontrollers

.

You can apply

quantization

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!")

Alien QueenAPP•8/16/24, 4:09 PM

TensorFlow Lite also supports other types of

quantization

quantization

, such as integer quantization or quantization. If you want more control over how the

quantization

quantization

is applied, you can specify the type of

quantization

quantization

during conversion.

For instance, if you want to use integer quantization, you can modify your code to be like this

This will convert both the weights and activations to , which is often the most efficient for

microcontrollers

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.