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Chapter 13
Figure 2: TensorFlow Lite internal architecture
A generic example of application
In this section we are going to see how to convert a model to TensorFlow Lite
and then run it. Note that training can still be performed by TensorFlow in the
environment that best fits your needs. However, inference runs on the mobile device.
Let's see how with the following code fragment in Python:
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
The code is self-explanatory. A standard TensorFlow 2.x model is opened and
converted by using tf.lite.TFLiteConverter.from_saved_model(saved_model_
dir). Pretty simple! Note that no specific installation is required. We simply use
the tf.lite API (https://www.tensorflow.org/api_docs/python/tf/lite).
It is also possible to apply a number of optimizations. For instance, post-training
quantization can be applied by default:
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quant_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_quantized_model)
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