{"action":"created","issue":{"url":"https://api.github.com/repos/NVIDIA/DIGITS/issues/44","labels_url":"https://api.github.com/repos/NVIDIA/DIGITS/issues/44/labels{/name}","comments_url":"https://api.github.com/repos/NVIDIA/DIGITS/issues/44/comments","events_url":"https://api.github.com/repos/NVIDIA/DIGITS/issues/44/events","html_url":"https://github.com/NVIDIA/DIGITS/issues/44","id":66161485,"number":44,"title":"50% Prediction Errors at \"100%\" accuracy","user":{"login":"Dezmon","id":3802392,"avatar_url":"https://avatars.githubusercontent.com/u/3802392?v=3","gravatar_id":"","url":"https://api.github.com/users/Dezmon","html_url":"https://github.com/Dezmon","followers_url":"https://api.github.com/users/Dezmon/followers","following_url":"https://api.github.com/users/Dezmon/following{/other_user}","gists_url":"https://api.github.com/users/Dezmon/gists{/gist_id}","starred_url":"https://api.github.com/users/Dezmon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Dezmon/subscriptions","organizations_url":"https://api.github.com/users/Dezmon/orgs","repos_url":"https://api.github.com/users/Dezmon/repos","events_url":"https://api.github.com/users/Dezmon/events{/privacy}","received_events_url":"https://api.github.com/users/Dezmon/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":16,"created_at":"2015-04-03T14:58:53Z","updated_at":"2015-04-07T18:54:38Z","closed_at":null,"body":"When training an AlexNet with two classes of 5,000 images per each class in separate training and validation directories (total 20,000 images).  The training and val loss values drop to near zero and the accuracy goes to an unbelievable 100%. Doing single image tests from the validation set for the second class is almost always an incorrect prediction. Single image tests from the validation set for the first class is always a correct prediction.  Am I misunderstanding what is being reported as \"Accuracy\"? the images are png files that are approximately 800x600. \r\n\r\nI have gotten the same result with much smaller subset of the data 1000 image per class. "},"comment":{"url":"https://api.github.com/repos/NVIDIA/DIGITS/issues/comments/90697161","html_url":"https://github.com/NVIDIA/DIGITS/issues/44#issuecomment-90697161","issue_url":"https://api.github.com/repos/NVIDIA/DIGITS/issues/44","id":90697161,"user":{"login":"Dezmon","id":3802392,"avatar_url":"https://avatars.githubusercontent.com/u/3802392?v=3","gravatar_id":"","url":"https://api.github.com/users/Dezmon","html_url":"https://github.com/Dezmon","followers_url":"https://api.github.com/users/Dezmon/followers","following_url":"https://api.github.com/users/Dezmon/following{/other_user}","gists_url":"https://api.github.com/users/Dezmon/gists{/gist_id}","starred_url":"https://api.github.com/users/Dezmon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Dezmon/subscriptions","organizations_url":"https://api.github.com/users/Dezmon/orgs","repos_url":"https://api.github.com/users/Dezmon/repos","events_url":"https://api.github.com/users/Dezmon/events{/privacy}","received_events_url":"https://api.github.com/users/Dezmon/received_events","type":"User","site_admin":false},"created_at":"2015-04-07T18:54:38Z","updated_at":"2015-04-07T18:54:38Z","body":"You are correct, adjusting the learning rate up by his recommended sqrt(k) gives very different network performance (exploding training loss, woohoo :/ ). Should I close this? Since the problem appears to only comes up with pathologically un/under-trained networks that for some reason are reporting low validation loss and 100% accuracy.  \r\n\r\nThank you both very much for your help."}}