export.py

import tensorflow as tf
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def
from tensorflow.python.saved_model import tag_constants
tf.keras.backend.set_learning_phase(0)    

# The export path contains the name and the version of the model
export_path = './PlanetModel/1'
model = tf.keras.models.load_model('./model.h5')
builder = saved_model_builder.SavedModelBuilder(export_path)

# Specify the signature definition
signature = predict_signature_def(inputs={'images': model.input},
                    outputs={t.name:t for t in model.outputs}) 

# This defaults to 'serving_default'
key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY

# Feth the current session and save the model
with tf.keras.backend.get_session() as sess:
    builder.add_meta_graph_and_variables(sess=sess,
                                 tags=[tag_constants.SERVING],
                                 signature_def_map={key: signature})
    
    builder.save()