Loading and using a trained TensorFlow model in Python
(Answering my own question) I realized that the easiest way to do this was to use the tf.estimator API. By initializing an estimator that warm starts from the model directory, it's possible to just call estimator.predict and pass the correct args (predict_fn) and get the predictions immediately. It's not required to deal with the graph variables in any way.
I trained a model in TensorFlow using the tf.estimator API, more specifically using tf.estimator.train_and_evaluate. I have the output directory of the training. How do I load my model from this and then use it?
I have tried using the tf.train.Saver class by loading the most recent ckpt file and restoring the session. However, then to call sess.run() I need to know what the name of the output node of the graph is so I can pass this to the fetches argument. What is the name/how can I access this output node? Is there a better way to load and use the trained model?
Note that I have already trained and saved the model in a ckpt file, so please do not suggest that I use the simple_save function.