difference between Tensorflow's Graph and GraphDef

Question

I'm quite new to the tensorflow. I would like to understand to conceptual difference between Graph and GraphDef.

furthermore, which one should I have to run a graph loaded from protobuf file (.pb)?

Thanks!

Answer 1

Graph or Computional Graph is the core concept of tensorflow to present computation. When you use tensorflow, you firstly create you own Computation Graph and pass the Graph to tensorflow. How to do that? As you may know, tensorflow support many front-end programming languages, like Python, C++, Java and Go and the core language is C++; how do the other languages transform the Graph to C++? They use a tool called protobuf which can generate specific language stubs, that's where the GraphDef come from. It's a serialized version of Graph.

which one should I have to run a graph loaded from protobuf file (.pb)

You should read your *pb file using GraphDef and bind the GraphDef to a (default) Graph, then use a session to run the Graph for computation, like the following code:

import tensorflow as tf from tensorflow.python.platform import gfile with tf.Session() as sess:     model_filename ='PATH_TO_PB.pb'     with gfile.FastGFile(model_filename, 'rb') as f:         graph_def = tf.GraphDef()         graph_def.ParseFromString(f.read())         g_in = tf.import_graph_def(graph_def) LOGDIR='/logs/tests/1/' train_writer = tf.summary.FileWriter(LOGDIR) train_writer.add_graph(sess.graph)