Tensorflow Hub.load Model To Tflite

I am trying to convert a model loaded with hub.load to TFLite. The model in question is universal-sentence-encoder (4) found at https://tfhub.dev/google/universal-sentence-encoder/

Solution 1:

Try using hub.KerasLayer to load your model into a tf.keras.Model and then convert it to ŧflite using .from_keras_model.

There's no such thing as a "keras SavedModel". There's the SavedModel, which is .pb file + assets folder + variables folder. It's like a file format, a way to store your model. It has nothing to do with the in memory tf.keras.Models. hub.load does not return a tf.keras.Model, but rather "the most generic thing" you can save in the SavedModel file format, namely a _UserObject. This is because you can save other things than just tf.keras.Modelss in a SavedModels file format.

I know this was not your question, but if you do want to get your tf.keras.Model back after loading, you can use tf.keras.save_model to save it. Then it will come back as a tf.keras.Model after loading using tf.saved_model.load (so then it's no longer the most generic thing).

EDIT:

Just the code:

import tensorflow as tf
import tensorflow_hub as hub 
model = tf.keras.Sequential()
model.add(tf.keras.layers.InputLayer(dtype=tf.string, input_shape=()))
model.add(hub.KerasLayer("https://tfhub.dev/google/universal-sentence-encoder/4"))
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

which works (it starts converting), but you get a:

2020-05-05 10:48:44.927433:Itensorflow/lite/toco/import_tensorflow.cc:659]Converting unsupported operation:StatefulPartitionedCall

So this is the code to convert models saved in SavedModel format to tflite, but you get a google-universal-sentence-encoder specific error. No idea how to fix that tough.