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

For anyone who needs a working example of saving a trained canned model and serving it without tensorflow serving ,I have documented here https://github.com/tettusud/tensorflow-examples/tree/master/estimators

1. You can create a predictor from tf.tensorflow.contrib.predictor.from_saved_model( exported_model_path)

2. Prepare input

   tf.train.Example( 
       features= tf.train.Features(
           feature={
               'x': tf.train.Feature(
                    float_list=tf.train.FloatList(value=[6.4, 3.2, 4.5, 1.5])
               )     
           }
       )    
   )
   

---

Here x is the name of the input that was given in input_receiver_function at the time of exporting. for eg:

feature_spec = {'x': tf.FixedLenFeature([4],tf.float32)}

def serving_input_receiver_fn():
    serialized_tf_example = tf.placeholder(dtype=tf.string,
                                           shape=[None],
                                           name='input_tensors')
    receiver_tensors = {'inputs': serialized_tf_example}
    features = tf.parse_example(serialized_tf_example, feature_spec)
    return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)