juliusshufan commented on a change in pull request #9793: Enable the reporting 
of cross-entropy or nll loss value when training CNN network using the models 
defined by example/image-classification
URL: https://github.com/apache/incubator-mxnet/pull/9793#discussion_r168378166
 
 

 ##########
 File path: example/image-classification/common/fit.py
 ##########
 @@ -117,6 +117,8 @@ def add_fit_args(parser):
                        help='load the model on an epoch using the 
model-load-prefix')
     train.add_argument('--top-k', type=int, default=0,
                        help='report the top-k accuracy. 0 means no report.')
+    train.add_argument('--loss', type=str,
+                       help='report the cross-entropy or nll-loss. ce means 
cross-entropy, nll-loss means likelihood loss')
 
 Review comment:
   @piiswrong @cjolivier01 Thanks for all your comments, and sorry for the 
confusion.
   
   Let me try to explain:
   1. My purpose is to report the loss value during training, this is because 
the loss value trends is helpful to monitor the convergence trend.
   2. Per my understanding is, the cross-entropy loss and (negative) likelihood 
loss are most common used for softmax output, and therefore I choose reporting 
either cross-entropy or negative likelihood loss as long as the output layer is 
the softmax.
   
   The current implementation of my PR is add a string-type argument, that is 
the "--loss", and the corresponding type loss will be reported accordingly.
   
   Meanwhile, my understanding to Eric's comment is, it be better to set the 
the argument as a list including all the matching loss type (['ce', 'nll-loss'] 
for softmax), and give the user to decide which loss value will be used. 
   
   May I know which method is acceptable? I'll then update my implementation 
accordingly.
   Thanks for your time.
   
   

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