Use keras with tensorflow serving

Question

I was wondering how to use my model trained with keras on a production server. I heard about tensorflow serving, but I can't figure out how to use it with my keras model.

I found this link : https://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html

But I don't know how to initialize the sess variable, since my model is already trained and everything. Is there any way to do this?

Answer 1

You can initialise your session variable as

from keras import backend as K
sess = K.get_session()

and go about exporting the model as in the tutorial (Note that import for exporter has changed)

from tensorflow.contrib.session_bundle import exporter

K.set_learning_phase(0)
export_path = ... # where to save the exported graph
export_version = ... # version number (integer)

saver = tf.train.Saver(sharded=True)
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)