[GitHub] cjolivier01 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-classifica

[GitBox]

cjolivier01 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_r168325934
 
 

 ##
 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:
   This seems a little confusing to me, as in what's the actual expected loss 
string.
   Maybe something like:
   "report the loss. Options are ce (cross-entropy) or nll-loss (likelihood 
loss)
   ?


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[GitHub] cjolivier01 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-classifica

[GitBox]

cjolivier01 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_r168324964
 
 

 ##
 File path: example/image-classification/common/fit.py
 ##
 @@ -260,6 +262,18 @@ def fit(args, network, data_loader, **kwargs):
 eval_metrics.append(mx.metric.create(
 'top_k_accuracy', top_k=args.top_k))
 
+supported_loss = ['ce', 'nll_loss']
+if args.loss:
 
 Review comment:
   would len(args.loss) > 0 or come other non-implied boolean test read more 
easily?


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[GitHub] cjolivier01 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-classifica

[GitBox]

cjolivier01 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_r168325934
 
 

 ##
 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:
   This seems a little confusing to me, as in what's the actual expected loss 
string.
   Maybe something like:
   "report the loss. Options are ce (cross-entropy) or nll-loss (likelihood 
loss)
   ?
   
   ?


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To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
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