How do I convert a Keras model to TensorFlow Lite format?

Hello @everyone @Middleware & OS How do I convert a Keras model to TensorFlow Lite format for running on a microcontroller using TensorFlow Lite for Microcontrollers?
Solution:
Hey man @Enthernet Code it's few things I would want you to do , firstly you have to export to tensor flow by saving your keras model as a tensor flow saved model or frozen Graph
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Solution
Marvee Amasi
Marvee Amasi5mo ago
Hey man @Enthernet Code it's few things I would want you to do , firstly you have to export to tensor flow by saving your keras model as a tensor flow saved model or frozen Graph
Marvee Amasi
Marvee Amasi5mo ago
you will now need to convert to tensor flow lite by using tensor flow lite converter to transform the model into a tensor flow lite format but consider quantization to reduce model size and optimize for performance
Marvee Amasi
Marvee Amasi5mo ago
Integrate it now into your microcontroller project using the tensor flow lite for microcontrollers library
Marvee Amasi
Marvee Amasi5mo ago
This is just a quick overview of what you would need to do
Camila_99$$
Camila_99$$5mo ago
@Marvee Amasi Great points! Just to add a bit more detail: 1/Export Keras model:
model.save('saved_model')

model.save('saved_model')

2/Convert to TensorFlow Lite:
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model('saved_model')
converter.optimizations = [tf.lite.Optimize.DEFAULT] # Optional but recommended
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)

import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model('saved_model')
converter.optimizations = [tf.lite.Optimize.DEFAULT] # Optional but recommended
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)

3/Integrate with microcontroller: Use TensorFlow Lite for Microcontrollers library. More details here: TensorFlow Lite for Microcontrollers.
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