error trying to conver tf model into tflite model - tensorflow

Question

import tensorflow as tf
from tensorflow import lite
converter = tf.lite.TFLiteConverter.from_saved_model("C:/tmp")
model = converter.convert()
open("converted_model.lite", "wb").write(model)
it gives
ValueError: This converter can only convert a single ConcreteFunction. Converting multiple functions is under development.
Is there a way to fix this or converting my .pb model into a keras (h5) model???
Related questions: How do i convert tensorflow 2.0 estimator model to tensorflow lite?

Answer 1

Try to use a concrete function:
import tensorflow as tf
from tensorflow import lite
saved_model_obj = tf.saved_model.load(export_dir="C:/tmp")
concrete_func = saved_model_obj.signatures['serving_default']
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
# print(saved_model_obj.signatures.keys())
# converter.optimizations = [tf.lite.Optimize.DEFAULT]
# converter.experimental_new_converter = True
model = converter.convert()
open("converted_model.lite", "wb").write(model)
serving_default is the default key for signatures in a SavedModels.
If not working try to uncomment converter.experimental_new_converter = True and the two lines above it.
Short explanation
Based on Concrete functions guide
Eager execution in TensorFlow 2 evaluates operations immediately, without building graphs.
To save the model you need graph/s which is wrapped in a python callables: a concrete functions.