sxjscience commented on issue #9583: use nd for accuracy calculation
URL: https://github.com/apache/incubator-mxnet/pull/9583#issuecomment-374425245
 
 
   Sure. Let’s move the discussion there.
   
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   ________________________________
   From: ThomasDelteil <[email protected]>
   Sent: Monday, March 19, 2018 4:17:58 PM
   To: apache/incubator-mxnet
   Cc: Xingjian SHI; Mention
   Subject: Re: [apache/incubator-mxnet] use nd for accuracy calculation (#9583)
   
   
   @ThomasDelteil commented on this pull request.
   
   ________________________________
   
   In 
python/mxnet/metric.py<https://github.com/apache/incubator-mxnet/pull/9583#discussion_r175615075>:
   
   >
                check_label_shapes(label, pred_label)
   
   -            self.sum_metric += (pred_label.flat == label.flat).sum()
   -            self.num_inst += len(pred_label.flat)
   +            if pred_label.context != label.context:
   +                pred_label = pred_label.as_in_context(label.context)
   +
   +            self.sum_metric += (pred_label.flatten() == 
label.flatten()).sum().asscalar()
   
   
   Thanks yes that's my understanding. However I think it should be left to the 
user to decide when to block, since it depends highly on their GPU and model 
size (like every 100 batches or every epoch). Also is there a reason why the 
accuracy is stored on the CPU rather than on specific context? My measures 
showed great improvements when storing the accuracy on GPU. Maybe if you don't 
mind we can continue the discussion there: 
#9571<https://github.com/apache/incubator-mxnet/issues/9571>
   
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