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

Assuming you want predictions in Python, SavedModelPredictor is probably the easiest way to load a SavedModel and get predictions. Suppose you save your model like so:

# Build the graph
f1 = tf.placeholder(shape=[], dtype=tf.float32)
f2 = tf.placeholder(shape=[], dtype=tf.float32)
f3 = tf.placeholder(shape=[], dtype=tf.float32)
l1 = tf.placeholder(shape=[], dtype=tf.float32)
output = build_graph(f1, f2, f3, l1)

# Save the model
inputs = {'F1': f1, 'F2': f2, 'F3': f3, 'L1': l1}
outputs = {'output': output_tensor}
tf.contrib.simple_save(sess, export_dir, inputs, outputs)

(The inputs can be any shape and don't even have to be placeholders nor root nodes in the graph).

Then, in the Python program that will use the SavedModel, we can get predictions like so:

from tensorflow.contrib import predictor

predict_fn = predictor.from_saved_model(export_dir)
predictions = predict_fn(
    {"F1": 1.0, "F2": 2.0, "F3": 3.0, "L1": 4.0})
print(predictions)

This answer shows how to get predictions in Java, C++, and Python (despite the fact that the question is focused on Estimators, the answer actually applies independently of how the SavedModel is created).