{"action":"created","issue":{"url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/332","repository_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn","labels_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/332/labels{/name}","comments_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/332/comments","events_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/332/events","html_url":"https://github.com/tiny-dnn/tiny-dnn/issues/332","id":181613930,"number":332,"title":"Why network that have worked in version 0.1.1 does not work in 1.0.0?","user":{"login":"pi-null-mezon","id":5991537,"avatar_url":"https://avatars.githubusercontent.com/u/5991537?v=3","gravatar_id":"","url":"https://api.github.com/users/pi-null-mezon","html_url":"https://github.com/pi-null-mezon","followers_url":"https://api.github.com/users/pi-null-mezon/followers","following_url":"https://api.github.com/users/pi-null-mezon/following{/other_user}","gists_url":"https://api.github.com/users/pi-null-mezon/gists{/gist_id}","starred_url":"https://api.github.com/users/pi-null-mezon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pi-null-mezon/subscriptions","organizations_url":"https://api.github.com/users/pi-null-mezon/orgs","repos_url":"https://api.github.com/users/pi-null-mezon/repos","events_url":"https://api.github.com/users/pi-null-mezon/events{/privacy}","received_events_url":"https://api.github.com/users/pi-null-mezon/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":11,"created_at":"2016-10-07T08:41:59Z","updated_at":"2016-11-18T06:41:46Z","closed_at":null,"body":"When I migrate to the version 1.0.0 I have found strange behavior. My network that i previously trained by version 0.1.1 of tiny-dnn (I have implement SegNet-like architecture for Chest X-RAY lungs segmentation and it works surprisingly well) do not produce similar results with tiny-dnn version 1.0.0. In v.0.1.1 my network trains well (I can see that at the each new epoch of training the loss function value becomes lower and lower and the activation in the last layer becomes more and more accurate), but in v.1.0.0 training process on the same data seems to diverges all the time (loss is not changed considerably between epoches and the last layer activation is not converges to lungs-like projection) . So why it is could happen? Also, I should tell, that I have not use built-in serialization method and instead load weights by my own (and when I rewrite code for the v.1.0.0 support one thing I have done was replacement of get_weights() method to weights() and call init_weights() after network creation and weights upload). Sorry for such long description    \n"},"comment":{"url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/comments/261460598","html_url":"https://github.com/tiny-dnn/tiny-dnn/issues/332#issuecomment-261460598","issue_url":"https://api.github.com/repos/tiny-dnn/tiny-dnn/issues/332","id":261460598,"user":{"login":"pi-null-mezon","id":5991537,"avatar_url":"https://avatars.githubusercontent.com/u/5991537?v=3","gravatar_id":"","url":"https://api.github.com/users/pi-null-mezon","html_url":"https://github.com/pi-null-mezon","followers_url":"https://api.github.com/users/pi-null-mezon/followers","following_url":"https://api.github.com/users/pi-null-mezon/following{/other_user}","gists_url":"https://api.github.com/users/pi-null-mezon/gists{/gist_id}","starred_url":"https://api.github.com/users/pi-null-mezon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pi-null-mezon/subscriptions","organizations_url":"https://api.github.com/users/pi-null-mezon/orgs","repos_url":"https://api.github.com/users/pi-null-mezon/repos","events_url":"https://api.github.com/users/pi-null-mezon/events{/privacy}","received_events_url":"https://api.github.com/users/pi-null-mezon/received_events","type":"User","site_admin":false},"created_at":"2016-11-18T06:41:46Z","updated_at":"2016-11-18T06:41:46Z","body":"Thanks, I will try."}}
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