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From GitBox <...@apache.org>
Subject [GitHub] [singa] nudles commented on pull request #697: New Model Layer Operator API
Date Tue, 02 Jun 2020 05:09:50 GMT

nudles commented on pull request #697:
URL: https://github.com/apache/singa/pull/697#issuecomment-637277574


   > 
   > 
   > > > Summary:
   > > > ```
   > > > * set hx.creator and cx.creator to None. (Still can't use the graph to train
correctly but can be executed normally)
   > > > 
   > > > * create ReLU layer instance
   > > > ```
   > > 
   > > 
   > > update conv and linear layers to include an argument for `activation`
   > > > ```
   > > > * create Loss layer instance
   > > > 
   > > > * remove set_attribute function, just copy the initial value from tensor
directly. Raise warning in __setattr__ when the types do not match
   > > > ```
   > > 
   > > 
   > > Shall we totally disable reassignment, like self.W=..., because it may affect
the graph as it replace the tensor W with another tensor?
   > > > ```
   > > > * remove on_device function, get device info from input tensors
   > > > ```
   > 
   > But I'm not sure where the reassignment is used in the whole project. Maybe it's used
in many places. I think reassignment is still very common.
   
   
   I see.  
   Will reassignment have any side effect to the computational graph?


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