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I'm trying to load a .pb file that was created in tf v1 on a tfv2 dist, my question is, the version 2 does have compatibility with older pb?

I already tried a few things, but none of them worked. Trying to load the pb file directly with:

with tf.compat.v1.gfile.GFile("./saved_model.pb", "rb") as f:
    graph_def = tf.compat.v1.GraphDef()
    graph_def.ParseFromString(f.read())
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def, name="")

The result when i run the code above is:

Traceback (most recent call last):
  File "read_tfv1_pb.py", line 7, in <module>
    graph_def.ParseFromString(f.read())
  File "D:\Anaconda3\envs\tf2\lib\site-packages\google\protobuf\message.py", line 187, in ParseFromString
    return self.MergeFromString(serialized)
  File "D:\Anaconda3\envs\tf2\lib\site-packages\google\protobuf\internal\python_message.py", line 1128, in MergeFromString
    if self._InternalParse(serialized, 0, length) != length:
  File "D:\Anaconda3\envs\tf2\lib\site-packages\google\protobuf\internal\python_message.py", line 1193, in InternalParse
    pos = field_decoder(buffer, new_pos, end, self, field_dict)
  File "D:\Anaconda3\envs\tf2\lib\site-packages\google\protobuf\internal\decoder.py", line 968, in _SkipFixed32
    raise _DecodeError('Truncated message.')
google.protobuf.message.DecodeError: Truncated message.

If not, is there a way that i can save the weights of the old pb and place them in a new model instance on tensorflow v2 to apply transfer learning / save with the new model structure ?

Carlos
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1 Answers1

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Convert it to a tf.saved_model with the code from here Convert a graph proto (pb/pbtxt) to a SavedModel for use in TensorFlow Serving or Cloud ML Engine

I just noticed that your .pb name is saved_model.pb so perhaps it is already a tf.saved_model. If that's the case you can load it as

func = tf.saved_model.load('.').signatures["serving_default"] 
out = func( tf.constant(10,tf.float32) )
dionKBastian
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  • Trying the above i receive a lot of warnings like this: ``` WARNING:tensorflow:Unable to create a python object for variable because it is a reference variable.It may not be visible to training APIs.``` – Carlos Nov 11 '19 at 11:52