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

Question

I am looking at Google\'s example on how to deploy and use a pre-trained Tensorflow graph (model) on Android. This example uses a .pb file at:

Answer 1

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.

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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.)