How Do I Convert Tensorflow 2.0 Estimator Model To Tensorflow Lite?

THe following code i have below produce the regular tensorflow model but when i try to convert it to tensorflow lite it doesn't work, i followed the following documentations. https

Solution 1:

Try to use a concrete function:

export_dir = "tmp"
serving_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(
  tf.feature_column.make_parse_example_spec(feat_cols))

estimator.export_saved_model(export_dir, serving_input_fn)

# Convert the model.
saved_model_obj = tf.saved_model.load(export_dir="tmp/1571728920/")
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

tflite_model = converter.convert()

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.