Export models and weights are trained in Keras to be used in TensorFlow Serving library

export_tf_models.py

from keras import backend as K
import tensorflow as tf
from tensorflow.contrib.session_bundle import exporter
sess = K.get_session()
export_path ="."  
export_version = 1

saver = tf.train.Saver(sharded=True)
### Code if you want to only export .meta files
# model_exporter = exporter.Exporter(saver)
# signature = exporter.classification_signature(input_tensor=model.input,
#                                               scores_tensor=model.output)
# model_exporter.init(sess.graph.as_graph_def(),
#                     default_graph_signature=signature)
# model_exporter.export(export_path, tf.constant(export_version), sess)

# Use a saver_def to get the "magic" strings to restore


### Code if you want to export .proto files
saver_def = saver.as_saver_def()
print saver_def.filename_tensor_name
print saver_def.restore_op_name
# write out 3 files
saver.save(sess, 'trained_model.sd')
tf.train.write_graph(sess.graph_def, '.', 'trained_model.proto', as_text=False)
tf.train.write_graph(sess.graph_def, '.', 'trained_model.txt', as_text=True)