{"action":"opened","issue":{"url":"https://api.github.com/repos/eric-haibin-lin/mxnet/issues/17","repository_url":"https://api.github.com/repos/eric-haibin-lin/mxnet","labels_url":"https://api.github.com/repos/eric-haibin-lin/mxnet/issues/17/labels{/name}","comments_url":"https://api.github.com/repos/eric-haibin-lin/mxnet/issues/17/comments","events_url":"https://api.github.com/repos/eric-haibin-lin/mxnet/issues/17/events","html_url":"https://github.com/eric-haibin-lin/mxnet/issues/17","id":163781302,"number":17,"title":"Low precision model prediction ","user":{"login":"eric-haibin-lin","id":5545640,"avatar_url":"https://avatars.githubusercontent.com/u/5545640?v=3","gravatar_id":"","url":"https://api.github.com/users/eric-haibin-lin","html_url":"https://github.com/eric-haibin-lin","followers_url":"https://api.github.com/users/eric-haibin-lin/followers","following_url":"https://api.github.com/users/eric-haibin-lin/following{/other_user}","gists_url":"https://api.github.com/users/eric-haibin-lin/gists{/gist_id}","starred_url":"https://api.github.com/users/eric-haibin-lin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/eric-haibin-lin/subscriptions","organizations_url":"https://api.github.com/users/eric-haibin-lin/orgs","repos_url":"https://api.github.com/users/eric-haibin-lin/repos","events_url":"https://api.github.com/users/eric-haibin-lin/events{/privacy}","received_events_url":"https://api.github.com/users/eric-haibin-lin/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-07-05T06:41:25Z","updated_at":"2016-07-05T06:41:25Z","closed_at":null,"body":"Experiment Setup \r\n\r\n* Baseline \r\n\r\nTrained [Inception BN Model(small)](https://github.com/dmlc/mxnet/blob/master/example/image-classification/symbol_inception-bn-28-small.py) on cifar-10 dataset with 20 epochs. \r\n\r\nThe validation accuracy of this model is: \r\n2016-07-04 20:47:55,781 Node[0] Epoch[20] Validation-accuracy=0.881230\r\n2016-07-04 20:47:55,781 Node[0] Epoch[20] Validation-top_k_accuracy_5=0.995154\r\n2016-07-04 20:47:55,781 Node[0] Epoch[20] Validation-top_k_accuracy_10=1.000000\r\n2016-07-04 20:47:55,781 Node[0] Epoch[20] Validation-top_k_accuracy_20=1.000000\r\n\r\n* Rounded model\r\n\r\nJust to kick it started, use the trained model for prediction, except that all the intermediate results in between operators are [**rounded** to closest integers](https://github.com/eric-haibin-lin/mxnet/blob/master/example/image-classification/symbol_inception-bn-28-small.py#L23). There's no rounding during training. No other quantization method is applied yet. \r\n\r\nThe validation accuracy of the rounded model is: \r\n2016-07-05 02:23:44,401 Node[0] Epoch[20] Validation-accuracy=0.698081\r\n2016-07-05 02:23:44,401 Node[0] Epoch[20] Validation-top_k_accuracy_5=0.969244\r\n2016-07-05 02:23:44,401 Node[0] Epoch[20] Validation-top_k_accuracy_10=1.000000\r\n2016-07-05 02:23:44,401 Node[0] Epoch[20] Validation-top_k_accuracy_20=1.000000\r\n\r\n\r\n\r\n\r\n@harouwu  @muli \r\n\r\n"}}
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