How to restore Tensorflow model from .pb file in python?

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.