# code from https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc
# Thanks Morgan

import os, argparse

import tensorflow as tf
from tensorflow.python.framework import graph_util

dir = os.path.dirname(os.path.realpath(__file__))


def freeze_graph(model_folder):
  # We retrieve our checkpoint fullpath
  checkpoint = tf.train.get_checkpoint_state(model_folder)
  input_checkpoint = checkpoint.model_checkpoint_path

  # We precise the file fullname of our freezed graph
  absolute_model_folder = "/".join(input_checkpoint.split('/')[:-1])
  output_graph = absolute_model_folder + "/frozen_model.pb"

  # Before exporting our graph, we need to precise what is our output node
  # This is how TF decides what part of the Graph he has to keep and what part it can dump
  # NOTE: this variable is plural, because you can have multiple output nodes
  output_node_names = "clone_0/MobileNet/Predictions/Softmax"

  # We clear devices to allow TensorFlow to control on which device it will load operations
  clear_devices = True

  # We import the meta graph and retrieve a Saver
  saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices)

  # We retrieve the protobuf graph definition
  graph = tf.get_default_graph()
  input_graph_def = graph.as_graph_def()

  # We start a session and restore the graph weights
  with tf.Session() as sess:
    saver.restore(sess, input_checkpoint)

    # We use a built-in TF helper to export variables to constants
    output_graph_def = graph_util.convert_variables_to_constants(
      sess,  # The session is used to retrieve the weights
      input_graph_def,  # The graph_def is used to retrieve the nodes
      output_node_names.split(",")  # The output node names are used to select the usefull nodes
    )

    # Finally we serialize and dump the output graph to the filesystem
    with tf.gfile.GFile(output_graph, "wb") as f:
      f.write(output_graph_def.SerializeToString())
    print("%d ops in the final graph." % len(output_graph_def.node))


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument("--model_folder", type=str, help="Model folder to export")
  args = parser.parse_args()

freeze_graph(args.model_folder)
Add freeze_graph tool