Copying weights of a specific layer - keras












2















According to this the following copies weights from one model to another:



target_model.set_weights(model.get_weights())


What about copying the weights of a specific layer, would this work?



model_1.layers[0].set_weights(source_model.layers[0].get_weights())
model_2.layers[0].set_weights(source_model.layers[0].get_weights())


If I train model_1 and model_2 will they have separate weights? The documentation doesn't state whether if this get_weights makes a deep copy or not. If this doesn't work, how can this be achieved?










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    2















    According to this the following copies weights from one model to another:



    target_model.set_weights(model.get_weights())


    What about copying the weights of a specific layer, would this work?



    model_1.layers[0].set_weights(source_model.layers[0].get_weights())
    model_2.layers[0].set_weights(source_model.layers[0].get_weights())


    If I train model_1 and model_2 will they have separate weights? The documentation doesn't state whether if this get_weights makes a deep copy or not. If this doesn't work, how can this be achieved?










    share|improve this question



























      2












      2








      2








      According to this the following copies weights from one model to another:



      target_model.set_weights(model.get_weights())


      What about copying the weights of a specific layer, would this work?



      model_1.layers[0].set_weights(source_model.layers[0].get_weights())
      model_2.layers[0].set_weights(source_model.layers[0].get_weights())


      If I train model_1 and model_2 will they have separate weights? The documentation doesn't state whether if this get_weights makes a deep copy or not. If this doesn't work, how can this be achieved?










      share|improve this question
















      According to this the following copies weights from one model to another:



      target_model.set_weights(model.get_weights())


      What about copying the weights of a specific layer, would this work?



      model_1.layers[0].set_weights(source_model.layers[0].get_weights())
      model_2.layers[0].set_weights(source_model.layers[0].get_weights())


      If I train model_1 and model_2 will they have separate weights? The documentation doesn't state whether if this get_weights makes a deep copy or not. If this doesn't work, how can this be achieved?







      python keras neural-network deep-learning keras-layer






      share|improve this question















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      edited Nov 15 '18 at 7:39









      today

      11k22038




      11k22038










      asked Nov 15 '18 at 7:12









      bones.felipebones.felipe

      315312




      315312
























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          1














          Of course, it would be a copy of the weights. It does not make sense the weights object to be shared between two separate models. You can check it for yourself with a simple example like this:



          model1 = Sequential()
          model1.add(Dense(10, input_dim=2))

          model2 = Sequential()
          model2.add(Dense(10, input_dim=2))

          model1.compile(loss='mse', optimizer='adam')
          model2.compile(loss='mse', optimizer='adam')


          Test:



          >>> model1.layers[0].get_weights()
          [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
          0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
          [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
          0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
          dtype=float32),
          array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

          >>> model2.layers[0].get_weights()
          [array([[-0.30062824, -0.3740575 , -0.3502644 , 0.28050178, -0.68631136,
          0.1596322 , 0.08288956, -0.20988202, 0.34323698, 0.2893324 ],
          [-0.29182747, -0.2754455 , -0.64082885, 0.29160154, 0.04342002,
          -0.4996035 , 0.6608283 , 0.10293472, 0.11375248, -0.43438092]],
          dtype=float32),
          array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

          >>> model2.layers[0].set_weights(model1.layers[0].get_weights())
          >>> model2.layers[0].get_weights()
          [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
          0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
          [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
          0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
          dtype=float32),
          array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

          >>> id(model1.layers[0].get_weights()[0])
          140494823634144

          >>> id(model2.layers[0].get_weights()[0])
          140494823635664


          The ids of kernel weights arrays are different so they are different objects, but with the same value.






          share|improve this answer

























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            1 Answer
            1






            active

            oldest

            votes








            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            1














            Of course, it would be a copy of the weights. It does not make sense the weights object to be shared between two separate models. You can check it for yourself with a simple example like this:



            model1 = Sequential()
            model1.add(Dense(10, input_dim=2))

            model2 = Sequential()
            model2.add(Dense(10, input_dim=2))

            model1.compile(loss='mse', optimizer='adam')
            model2.compile(loss='mse', optimizer='adam')


            Test:



            >>> model1.layers[0].get_weights()
            [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
            0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
            [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
            0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
            dtype=float32),
            array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

            >>> model2.layers[0].get_weights()
            [array([[-0.30062824, -0.3740575 , -0.3502644 , 0.28050178, -0.68631136,
            0.1596322 , 0.08288956, -0.20988202, 0.34323698, 0.2893324 ],
            [-0.29182747, -0.2754455 , -0.64082885, 0.29160154, 0.04342002,
            -0.4996035 , 0.6608283 , 0.10293472, 0.11375248, -0.43438092]],
            dtype=float32),
            array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

            >>> model2.layers[0].set_weights(model1.layers[0].get_weights())
            >>> model2.layers[0].get_weights()
            [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
            0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
            [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
            0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
            dtype=float32),
            array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

            >>> id(model1.layers[0].get_weights()[0])
            140494823634144

            >>> id(model2.layers[0].get_weights()[0])
            140494823635664


            The ids of kernel weights arrays are different so they are different objects, but with the same value.






            share|improve this answer






























              1














              Of course, it would be a copy of the weights. It does not make sense the weights object to be shared between two separate models. You can check it for yourself with a simple example like this:



              model1 = Sequential()
              model1.add(Dense(10, input_dim=2))

              model2 = Sequential()
              model2.add(Dense(10, input_dim=2))

              model1.compile(loss='mse', optimizer='adam')
              model2.compile(loss='mse', optimizer='adam')


              Test:



              >>> model1.layers[0].get_weights()
              [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
              0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
              [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
              0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
              dtype=float32),
              array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

              >>> model2.layers[0].get_weights()
              [array([[-0.30062824, -0.3740575 , -0.3502644 , 0.28050178, -0.68631136,
              0.1596322 , 0.08288956, -0.20988202, 0.34323698, 0.2893324 ],
              [-0.29182747, -0.2754455 , -0.64082885, 0.29160154, 0.04342002,
              -0.4996035 , 0.6608283 , 0.10293472, 0.11375248, -0.43438092]],
              dtype=float32),
              array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

              >>> model2.layers[0].set_weights(model1.layers[0].get_weights())
              >>> model2.layers[0].get_weights()
              [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
              0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
              [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
              0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
              dtype=float32),
              array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

              >>> id(model1.layers[0].get_weights()[0])
              140494823634144

              >>> id(model2.layers[0].get_weights()[0])
              140494823635664


              The ids of kernel weights arrays are different so they are different objects, but with the same value.






              share|improve this answer




























                1












                1








                1







                Of course, it would be a copy of the weights. It does not make sense the weights object to be shared between two separate models. You can check it for yourself with a simple example like this:



                model1 = Sequential()
                model1.add(Dense(10, input_dim=2))

                model2 = Sequential()
                model2.add(Dense(10, input_dim=2))

                model1.compile(loss='mse', optimizer='adam')
                model2.compile(loss='mse', optimizer='adam')


                Test:



                >>> model1.layers[0].get_weights()
                [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
                0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
                [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
                0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
                dtype=float32),
                array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

                >>> model2.layers[0].get_weights()
                [array([[-0.30062824, -0.3740575 , -0.3502644 , 0.28050178, -0.68631136,
                0.1596322 , 0.08288956, -0.20988202, 0.34323698, 0.2893324 ],
                [-0.29182747, -0.2754455 , -0.64082885, 0.29160154, 0.04342002,
                -0.4996035 , 0.6608283 , 0.10293472, 0.11375248, -0.43438092]],
                dtype=float32),
                array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

                >>> model2.layers[0].set_weights(model1.layers[0].get_weights())
                >>> model2.layers[0].get_weights()
                [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
                0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
                [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
                0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
                dtype=float32),
                array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

                >>> id(model1.layers[0].get_weights()[0])
                140494823634144

                >>> id(model2.layers[0].get_weights()[0])
                140494823635664


                The ids of kernel weights arrays are different so they are different objects, but with the same value.






                share|improve this answer















                Of course, it would be a copy of the weights. It does not make sense the weights object to be shared between two separate models. You can check it for yourself with a simple example like this:



                model1 = Sequential()
                model1.add(Dense(10, input_dim=2))

                model2 = Sequential()
                model2.add(Dense(10, input_dim=2))

                model1.compile(loss='mse', optimizer='adam')
                model2.compile(loss='mse', optimizer='adam')


                Test:



                >>> model1.layers[0].get_weights()
                [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
                0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
                [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
                0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
                dtype=float32),
                array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

                >>> model2.layers[0].get_weights()
                [array([[-0.30062824, -0.3740575 , -0.3502644 , 0.28050178, -0.68631136,
                0.1596322 , 0.08288956, -0.20988202, 0.34323698, 0.2893324 ],
                [-0.29182747, -0.2754455 , -0.64082885, 0.29160154, 0.04342002,
                -0.4996035 , 0.6608283 , 0.10293472, 0.11375248, -0.43438092]],
                dtype=float32),
                array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

                >>> model2.layers[0].set_weights(model1.layers[0].get_weights())
                >>> model2.layers[0].get_weights()
                [array([[-0.42853734, 0.18648076, -0.47137827, 0.1792168 , 0.0373047 ,
                0.2765705 , 0.38383502, 0.09664273, -0.4971757 , 0.41548246],
                [ 0.0403192 , -0.01309097, 0.6656211 , -0.0536288 , 0.58677703,
                0.21625364, 0.26447064, -0.42619988, 0.17218047, -0.39748642]],
                dtype=float32),
                array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]

                >>> id(model1.layers[0].get_weights()[0])
                140494823634144

                >>> id(model2.layers[0].get_weights()[0])
                140494823635664


                The ids of kernel weights arrays are different so they are different objects, but with the same value.







                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Nov 16 '18 at 6:49

























                answered Nov 15 '18 at 7:38









                todaytoday

                11k22038




                11k22038
































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