how to load and use a saved model on tensorflow?

Question

I have found 2 ways to save a model in Tensorflow: tf.train.Saver() and SavedModelBuilder. However, I can\'t find documentation on using the mo

Answer 1

Here's the code snippet to load and restore/predict models using the simple_save

#Save the model:
tf.saved_model.simple_save(sess, export_dir=saveModelPath,
                                   inputs={"inputImageBatch": X_train, "inputClassBatch": Y_train,
                                           "isTrainingBool": isTraining},
                                   outputs={"predictedClassBatch": predClass})

Note that using simple_save sets certain default values (this can be seen at: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/simple_save.py)

Now, to restore and use the inputs/outputs dict:

from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model import signature_constants

with tf.Session() as sess:
  model = tf.saved_model.loader.load(export_dir=saveModelPath, sess=sess, tags=[tag_constants.SERVING]) #Note the SERVINGS tag is put as default.

  inputImage_name = model.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].inputs['inputImageBatch'].name
  inputImage = tf.get_default_graph().get_tensor_by_name(inputImage_name)

  inputLabel_name = model.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].inputs['inputClassBatch'].name
  inputLabel = tf.get_default_graph().get_tensor_by_name(inputLabel_name)

  isTraining_name = model.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].inputs['isTrainingBool'].name
  isTraining = tf.get_default_graph().get_tensor_by_name(isTraining_name)

  outputPrediction_name = model.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].outputs['predictedClassBatch'].name
  outputPrediction = tf.get_default_graph().get_tensor_by_name(outputPrediction_name)

  outPred = sess.run(outputPrediction, feed_dict={inputImage:sampleImages, isTraining:False})

  print("predicted classes:", outPred)

Note: the default signature_def was needed to make use of the tensor names specified in the input & output dicts.