Is there an example on how to generate protobuf files holding trained TensorFlow graphs

EDIT: The freeze_graph.py script, which is part of the TensorFlow repository, now serves as a tool that generates a protocol buffer representing a “frozen” trained model, from an existing TensorFlow GraphDef and a saved checkpoint. It uses the same steps as described below, but it much easier to use.

Currently the process isn’t very well documented (and subject to refinement), but the approximate steps are as follows:

1. Build and train your model as a tf.Graph called g_1.
2. Fetch the final values of each of the variables and store them as numpy arrays (using Session.run()).
3. In a new tf.Graph called g_2, create tf.constant() tensors for each of the variables, using the value of the corresponding numpy array fetched in step 2.

4. Use tf.import_graph_def() to copy nodes from g_1 into g_2, and use the input_map argument to replace each variable in g_1 with the corresponding tf.constant() tensors created in step 3. You may also want to use input_map to specify a new input tensor (e.g. replacing an input pipeline with a tf.placeholder()). Use the return_elements argument to specify the name of the predicted output tensor.

5. Call g_2.as_graph_def() to get a protocol buffer representation of the graph.

(NOTE: The generated graph will have extra nodes in the graph for training. Although it is not part of the public API, you may wish to use the internal graph_util.extract_sub_graph() function to strip these nodes from the graph.)