如何从python中的.pb文件恢复Tensorflow模型？

Question

I have an tensorflow .pb file which I would like to load into python DNN, restore the graph and get the predictions. I am doing this to test out whether the .pb file created can make the predictions similar to the normal Saver.save() model.

My basic problem is am getting a very different value of predictions when I make them on Android using the above mentioned .pb file

My .pb file creation code:

frozen_graph = tf.graph_util.convert_variables_to_constants(
        session,
        session.graph_def,
        ['outputLayer/Softmax']
    )
with open('frozen_model.pb', 'wb') as f:
  f.write(frozen_graph.SerializeToString())

So I have two major concerns:

1. How can I load the above mentioned .pb file to python Tensorflow model ?
2. Why am I getting completely different values of prediction in python and android ?

Answer 1

The following code will read the model and print out the names of the nodes in the graph.

import tensorflow as tf
from tensorflow.python.platform import gfile
GRAPH_PB_PATH = './frozen_model.pb'
with tf.Session() as sess:
   print("load graph")
   with gfile.FastGFile(GRAPH_PB_PATH,'rb') as f:
       graph_def = tf.GraphDef()
   graph_def.ParseFromString(f.read())
   sess.graph.as_default()
   tf.import_graph_def(graph_def, name='')
   graph_nodes=[n for n in graph_def.node]
   names = []
   for t in graph_nodes:
      names.append(t.name)
   print(names)

You are freezing the graph properly that is why you are getting different results basically weights are not getting stored in your model. You can use the freeze_graph.py ( link ) for getting a correctly stored graph.

Answer 2

Here is the updated code for tensorflow 2.

import tensorflow as tf

GRAPH_PB_PATH = './frozen_model.pb'
with tf.compat.v1.Session() as sess:
   print("load graph")
   with tf.io.gfile.GFile(GRAPH_PB_PATH,'rb') as f:
       graph_def = tf.compat.v1.GraphDef()
   graph_def.ParseFromString(f.read())
   sess.graph.as_default()
   tf.import_graph_def(graph_def, name='')
   graph_nodes=[n for n in graph_def.node]
   names = []
   for t in graph_nodes:
      names.append(t.name)
   print(names)