TensorFlow Lite is a TensorFlow lightweight solution for mobile and embedded devices. It lets you run TensorFlow models on mobile devices with low latency and quickly without necessarily incurring a round trip to the server.
Before you run your model with TensorFlow Lite you need to convert your trained model into the TensorFlow Lite model. This model then exported to mobile devices.
In this tutorial, we’re going to convert the TensorFlow or Keras model into the TensorFlow Lite model to use on mobile or IoT devices. To convert the model we are using Python API. It makes it easier to convert models as part of a model development pipeline.TensorFlow Lite converter takes a TensorFlow or Keras model and generates a .tflite file.
The following code describes how to use the tf.lite.TFLiteConverter using the Python API in TensorFlow 2.0.
Convert .pb to .tflite file
We have a model saved after training as .pb file. Now converts SavedModel directories into .tflite.
Save model
model.save(filepath="save_model/")converter = tf.lite.TFLiteConverter.from_saved_model('save_model')
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)Finally, we’ll convert .tflite using the TFLiteConverter this is achieved with the from_saved_model method will pass directory of .pb file and variable. Then call the converter to and save its results as tflite_model.tflite
Convert Keras(.h5) model to .tflite file
If you have saved keras(h5) model then you need to convert it to tflite before running in the mobile device. In TensorFlow 2.0 you can not convert .h5 to .tflite file directly. First, you need to load the saved keras model then convert using TFLiteConverter.
new_model= tf.keras.models.load_model(filepath="keras_model.h5")tflite_converter = tf.lite.TFLiteConverter.from_keras_model(new_model)
tflite_model = tflite_converter.convert()
open("tf_lite_model.tflite", "wb").write(tflite_model)Finally, there’s the TensorFlow Lite model as a .lite which built from Keras(.h5) using the TensorFlow TFLiteConverter.