How to extract weights and other variable values from tensorflow checkpoint without restoring the graph?












1















Provided a checkpoint file but no meta graph or code that produced the network, I want to extract the stored values of the variables in the checkpoint file.



So without restoring the graph, how do I extract the values stored in thr checkpoint. I could potentially convert everything from the checkpoint to a dictionary of numpy arrays or something similar.










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    1















    Provided a checkpoint file but no meta graph or code that produced the network, I want to extract the stored values of the variables in the checkpoint file.



    So without restoring the graph, how do I extract the values stored in thr checkpoint. I could potentially convert everything from the checkpoint to a dictionary of numpy arrays or something similar.










    share|improve this question



























      1












      1








      1








      Provided a checkpoint file but no meta graph or code that produced the network, I want to extract the stored values of the variables in the checkpoint file.



      So without restoring the graph, how do I extract the values stored in thr checkpoint. I could potentially convert everything from the checkpoint to a dictionary of numpy arrays or something similar.










      share|improve this question
















      Provided a checkpoint file but no meta graph or code that produced the network, I want to extract the stored values of the variables in the checkpoint file.



      So without restoring the graph, how do I extract the values stored in thr checkpoint. I could potentially convert everything from the checkpoint to a dictionary of numpy arrays or something similar.







      python tensorflow checkpoint






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      edited Nov 20 '18 at 10:48









      D Manokhin

      599219




      599219










      asked Nov 20 '18 at 10:43









      dsalajdsalaj

      6641228




      6641228
























          1 Answer
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          Found the solution:



          reader = tf.train.NewCheckpointReader("/path/to/checkpoint")
          shapes_dict = reader.get_variable_to_shape_map() # use it to get the variable names
          extracted_values = reader.get_tensor(shapes_dict.keys()[0])
          # array([[ 0. , -1.8053141],
          # [-1.5647348, 0. ]], dtype=float32)


          The tf.train.NewCheckpointReader is not really documented in current documentation of API r1.12.
          But you can see the usage example in the source code here.






          share|improve this answer























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            1 Answer
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            active

            oldest

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            1 Answer
            1






            active

            oldest

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            active

            oldest

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            active

            oldest

            votes









            1














            Found the solution:



            reader = tf.train.NewCheckpointReader("/path/to/checkpoint")
            shapes_dict = reader.get_variable_to_shape_map() # use it to get the variable names
            extracted_values = reader.get_tensor(shapes_dict.keys()[0])
            # array([[ 0. , -1.8053141],
            # [-1.5647348, 0. ]], dtype=float32)


            The tf.train.NewCheckpointReader is not really documented in current documentation of API r1.12.
            But you can see the usage example in the source code here.






            share|improve this answer




























              1














              Found the solution:



              reader = tf.train.NewCheckpointReader("/path/to/checkpoint")
              shapes_dict = reader.get_variable_to_shape_map() # use it to get the variable names
              extracted_values = reader.get_tensor(shapes_dict.keys()[0])
              # array([[ 0. , -1.8053141],
              # [-1.5647348, 0. ]], dtype=float32)


              The tf.train.NewCheckpointReader is not really documented in current documentation of API r1.12.
              But you can see the usage example in the source code here.






              share|improve this answer


























                1












                1








                1







                Found the solution:



                reader = tf.train.NewCheckpointReader("/path/to/checkpoint")
                shapes_dict = reader.get_variable_to_shape_map() # use it to get the variable names
                extracted_values = reader.get_tensor(shapes_dict.keys()[0])
                # array([[ 0. , -1.8053141],
                # [-1.5647348, 0. ]], dtype=float32)


                The tf.train.NewCheckpointReader is not really documented in current documentation of API r1.12.
                But you can see the usage example in the source code here.






                share|improve this answer













                Found the solution:



                reader = tf.train.NewCheckpointReader("/path/to/checkpoint")
                shapes_dict = reader.get_variable_to_shape_map() # use it to get the variable names
                extracted_values = reader.get_tensor(shapes_dict.keys()[0])
                # array([[ 0. , -1.8053141],
                # [-1.5647348, 0. ]], dtype=float32)


                The tf.train.NewCheckpointReader is not really documented in current documentation of API r1.12.
                But you can see the usage example in the source code here.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 20 '18 at 10:56









                dsalajdsalaj

                6641228




                6641228
































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