tensorflow - Deploying Keras Models via Google Cloud ML

Question

I am looking to use Google Cloud ML to host my Keras models so that I can call the API and make some predictions. I am running into some issues from the Keras side of things.

So far I have been able to build a model using TensorFlow and deploy it on CloudML. In order for this to work I had to make some changes to my basic TF code. The changes are documented here: https://cloud.google.com/ml/docs/how-tos/preparing-models#code_changes

I have also been able to train a similar model using Keras. I can even save the model in the same export and export.meta format as I would get with TF.

from keras import backend as K

saver = tf.train.Saver()
session = K.get_session()
saver.save(session, 'export')

The part I am missing is how do I add the placeholders for input and output into the graph I build on Keras?

Answer 1

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