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Regularization: global or layerwise?



The Next CEO of Stack Overflow
2019 Community Moderator ElectionUnderstanding regularizationChoosing regularization method in neural networksL1 regularization in pybrainRegularization practice with ANNsSVM regularization - minimizing margin?How can I improve my regression model?Is regularization included in loss history Keras returns?Which regularization in convolution layers (conv2D)How does a Bayes regularization works?Regularization in Embedding models?










0












$begingroup$


Keras gives you the option to apply regularization differently to different layers. I mean, why not? Though when I first learned about neural nets (from ESL), I thought of it as a global parameter.



Global is simpler to tune, but obviously a global penalty can be no better than equally efficient when compared to some optimal set of layerwise ones.



So, what are the cases where different penalties for different layers will work better than a single global penalty, and better-enough to be worth the bother?










share|improve this question









$endgroup$
















    0












    $begingroup$


    Keras gives you the option to apply regularization differently to different layers. I mean, why not? Though when I first learned about neural nets (from ESL), I thought of it as a global parameter.



    Global is simpler to tune, but obviously a global penalty can be no better than equally efficient when compared to some optimal set of layerwise ones.



    So, what are the cases where different penalties for different layers will work better than a single global penalty, and better-enough to be worth the bother?










    share|improve this question









    $endgroup$














      0












      0








      0





      $begingroup$


      Keras gives you the option to apply regularization differently to different layers. I mean, why not? Though when I first learned about neural nets (from ESL), I thought of it as a global parameter.



      Global is simpler to tune, but obviously a global penalty can be no better than equally efficient when compared to some optimal set of layerwise ones.



      So, what are the cases where different penalties for different layers will work better than a single global penalty, and better-enough to be worth the bother?










      share|improve this question









      $endgroup$




      Keras gives you the option to apply regularization differently to different layers. I mean, why not? Though when I first learned about neural nets (from ESL), I thought of it as a global parameter.



      Global is simpler to tune, but obviously a global penalty can be no better than equally efficient when compared to some optimal set of layerwise ones.



      So, what are the cases where different penalties for different layers will work better than a single global penalty, and better-enough to be worth the bother?







      machine-learning neural-network keras regularization






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 2 hours ago









      generic_usergeneric_user

      29418




      29418




















          1 Answer
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          0












          $begingroup$

          Regularisation is a technique to solve overfitting.



          This feature from Keras is going to help a lot in many scenarios. Few times we don't want simpler but a granulr tuning.



          1. CNN: we all know that each convolution layer can contribute to certain set of features from the dataset, and we now a days know what it is trying to do, by defining regularisation to each layer differently, we can better understand how each layer is effecting the final output

          2. Transfer Learning: Where we want learn from the already trained network, and use that domain knowledge. now during this, we can now control, how much we want to regularise before/after merging from the base network.

          3. Multi Task Learning: This is a technique in which we learn multiple tasks together, now with this kind of regularisation we can now control before the merge of the layers, how much of the information can be merged.

          these are the quick things i could think of. But there are definitely lots of other uses.



          Vote up, if this helps ;)






          share|improve this answer









          $endgroup$












          • $begingroup$
            Are you a neural network?
            $endgroup$
            – generic_user
            41 mins ago










          • $begingroup$
            You are really a generic_user. Lol.
            $endgroup$
            – William Scott
            10 mins ago











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          1 Answer
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          0












          $begingroup$

          Regularisation is a technique to solve overfitting.



          This feature from Keras is going to help a lot in many scenarios. Few times we don't want simpler but a granulr tuning.



          1. CNN: we all know that each convolution layer can contribute to certain set of features from the dataset, and we now a days know what it is trying to do, by defining regularisation to each layer differently, we can better understand how each layer is effecting the final output

          2. Transfer Learning: Where we want learn from the already trained network, and use that domain knowledge. now during this, we can now control, how much we want to regularise before/after merging from the base network.

          3. Multi Task Learning: This is a technique in which we learn multiple tasks together, now with this kind of regularisation we can now control before the merge of the layers, how much of the information can be merged.

          these are the quick things i could think of. But there are definitely lots of other uses.



          Vote up, if this helps ;)






          share|improve this answer









          $endgroup$












          • $begingroup$
            Are you a neural network?
            $endgroup$
            – generic_user
            41 mins ago










          • $begingroup$
            You are really a generic_user. Lol.
            $endgroup$
            – William Scott
            10 mins ago















          0












          $begingroup$

          Regularisation is a technique to solve overfitting.



          This feature from Keras is going to help a lot in many scenarios. Few times we don't want simpler but a granulr tuning.



          1. CNN: we all know that each convolution layer can contribute to certain set of features from the dataset, and we now a days know what it is trying to do, by defining regularisation to each layer differently, we can better understand how each layer is effecting the final output

          2. Transfer Learning: Where we want learn from the already trained network, and use that domain knowledge. now during this, we can now control, how much we want to regularise before/after merging from the base network.

          3. Multi Task Learning: This is a technique in which we learn multiple tasks together, now with this kind of regularisation we can now control before the merge of the layers, how much of the information can be merged.

          these are the quick things i could think of. But there are definitely lots of other uses.



          Vote up, if this helps ;)






          share|improve this answer









          $endgroup$












          • $begingroup$
            Are you a neural network?
            $endgroup$
            – generic_user
            41 mins ago










          • $begingroup$
            You are really a generic_user. Lol.
            $endgroup$
            – William Scott
            10 mins ago













          0












          0








          0





          $begingroup$

          Regularisation is a technique to solve overfitting.



          This feature from Keras is going to help a lot in many scenarios. Few times we don't want simpler but a granulr tuning.



          1. CNN: we all know that each convolution layer can contribute to certain set of features from the dataset, and we now a days know what it is trying to do, by defining regularisation to each layer differently, we can better understand how each layer is effecting the final output

          2. Transfer Learning: Where we want learn from the already trained network, and use that domain knowledge. now during this, we can now control, how much we want to regularise before/after merging from the base network.

          3. Multi Task Learning: This is a technique in which we learn multiple tasks together, now with this kind of regularisation we can now control before the merge of the layers, how much of the information can be merged.

          these are the quick things i could think of. But there are definitely lots of other uses.



          Vote up, if this helps ;)






          share|improve this answer









          $endgroup$



          Regularisation is a technique to solve overfitting.



          This feature from Keras is going to help a lot in many scenarios. Few times we don't want simpler but a granulr tuning.



          1. CNN: we all know that each convolution layer can contribute to certain set of features from the dataset, and we now a days know what it is trying to do, by defining regularisation to each layer differently, we can better understand how each layer is effecting the final output

          2. Transfer Learning: Where we want learn from the already trained network, and use that domain knowledge. now during this, we can now control, how much we want to regularise before/after merging from the base network.

          3. Multi Task Learning: This is a technique in which we learn multiple tasks together, now with this kind of regularisation we can now control before the merge of the layers, how much of the information can be merged.

          these are the quick things i could think of. But there are definitely lots of other uses.



          Vote up, if this helps ;)







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 50 mins ago









          William ScottWilliam Scott

          1063




          1063











          • $begingroup$
            Are you a neural network?
            $endgroup$
            – generic_user
            41 mins ago










          • $begingroup$
            You are really a generic_user. Lol.
            $endgroup$
            – William Scott
            10 mins ago
















          • $begingroup$
            Are you a neural network?
            $endgroup$
            – generic_user
            41 mins ago










          • $begingroup$
            You are really a generic_user. Lol.
            $endgroup$
            – William Scott
            10 mins ago















          $begingroup$
          Are you a neural network?
          $endgroup$
          – generic_user
          41 mins ago




          $begingroup$
          Are you a neural network?
          $endgroup$
          – generic_user
          41 mins ago












          $begingroup$
          You are really a generic_user. Lol.
          $endgroup$
          – William Scott
          10 mins ago




          $begingroup$
          You are really a generic_user. Lol.
          $endgroup$
          – William Scott
          10 mins ago

















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          ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result