TensorFlow: How to predict from a SavedModel?

Question

I have exported a SavedModel and now I with to load it back in and make a prediction. It was trained with the following features and labels:

F1          


        

Answer 1

Once the graph is loaded, it is available in the current context and you can feed input data through it to obtain predictions. Each use-case is rather different, but the addition to your code will look something like this:

with tf.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(
        sess,
        [tf.saved_model.tag_constants.SERVING],
        "/job/export/Servo/1503723455"
    )

    prediction = sess.run(
        'prefix/predictions/Identity:0',
        feed_dict={
            'Placeholder:0': [20.9],
            'Placeholder_1:0': [1.8],
            'Placeholder_2:0': [0.9]
        }
    )

    print(prediction)

Here, you need to know the names of what your prediction inputs will be. If you did not give them a nave in your serving_fn, then they default to Placeholder_n, where n is the nth feature.

The first string argument of sess.run is the name of the prediction target. This will vary based on your use case.