Export tensorflow model

export.py

import tensorflow as tf
from model import select_model, get_checkpoint

from tensorflow.python.framework import graph_util
from tensorflow.contrib.learn.python.learn.utils import export
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import signature_def_utils
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model import utils

RESIZE_FINAL = 227
GENDER_LIST =['M','F']
AGE_LIST = ['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']

tf.app.flags.DEFINE_string('checkpoint', 'checkpoint',
                          'Checkpoint basename')

tf.app.flags.DEFINE_string('class_type', 'age',
                           'Classification type (age|gender)')

tf.app.flags.DEFINE_string('device_id', '/cpu:0',
                           'What processing unit to execute inference on')

tf.app.flags.DEFINE_string('model_dir', '',
                           'Model directory (where training data lives)')

tf.app.flags.DEFINE_string('export_dir', '/tmp/tf_exported_model/0',
                           'Export directory')

tf.app.flags.DEFINE_string('model_type', 'default',
                           'Type of convnet')

tf.app.flags.DEFINE_string('requested_step', '', 'Within the model directory, a requested step to restore e.g., 9000')

FLAGS = tf.app.flags.FLAGS

def main(argv=None):
	with tf.Session() as sess:

		label_list = AGE_LIST if FLAGS.class_type == 'age' else GENDER_LIST
		nlabels = len(label_list)
		
		model_fn = select_model(FLAGS.model_type)

		with tf.device(FLAGS.device_id):

			images = tf.placeholder(tf.float32, [None, RESIZE_FINAL, RESIZE_FINAL, 3])
			logits = model_fn(nlabels, images, 1, False)
			init = tf.global_variables_initializer()

			requested_step = FLAGS.requested_step if FLAGS.requested_step else None
			checkpoint_path = '%s' % (FLAGS.model_dir)
			model_checkpoint_path, global_step = get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)

			saver = tf.train.Saver()
			saver.restore(sess, model_checkpoint_path)

			prediction_signature = signature_def_utils.predict_signature_def(
				inputs={'images': images},
				outputs={'output': logits})

      #below is wrong.
			classification_signature = signature_def_utils.classification_signature_def(
				examples=images,
				classes=logits,
				scores=logits)

			builder = saved_model_builder.SavedModelBuilder(FLAGS.export_dir)
			legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
			builder.add_meta_graph_and_variables(
				sess, [tag_constants.SERVING],
				signature_def_map={
					'inputs': prediction_signature,
					signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
						classification_signature,
				},
				legacy_init_op=legacy_init_op)

			builder.save()

if __name__ == '__main__':
	tf.app.run()