Deep Learning and AI Superhero

Practical Exercises Chapter 8

Exercise 1: Convert a TensorFlow Model to TensorFlow Lite

Task: Convert a pre-trained TensorFlow model (e.g., a simple image classification model) to TensorFlow Lite format for edge deployment. Apply quantization to reduce the model size.

Solution:

import tensorflow as tf

# Load a pre-trained model (for example, from a saved model directory)

model = tf.keras.models.load_model('my_saved_model')

# Convert the model to TensorFlow Lite format

converter = tf.lite.TFLiteConverter.from_keras_model(model)

# Apply quantization to optimize the model

converter.optimizations = [tf.lite.Optimize.DEFAULT]

tflite_model = converter.convert()

# Save the TensorFlow Lite model to a file

with open('model_quantized.tflite', 'wb') as f: