{"action":"created","issue":{"url":"https://api.github.com/repos/fchollet/keras/issues/4762","repository_url":"https://api.github.com/repos/fchollet/keras","labels_url":"https://api.github.com/repos/fchollet/keras/issues/4762/labels{/name}","comments_url":"https://api.github.com/repos/fchollet/keras/issues/4762/comments","events_url":"https://api.github.com/repos/fchollet/keras/issues/4762/events","html_url":"https://github.com/fchollet/keras/issues/4762","id":196305502,"number":4762,"title":"trainable flag does not work for batch normalization layer","user":{"login":"nes123","id":7921778,"avatar_url":"https://avatars2.githubusercontent.com/u/7921778?v=3","gravatar_id":"","url":"https://api.github.com/users/nes123","html_url":"https://github.com/nes123","followers_url":"https://api.github.com/users/nes123/followers","following_url":"https://api.github.com/users/nes123/following{/other_user}","gists_url":"https://api.github.com/users/nes123/gists{/gist_id}","starred_url":"https://api.github.com/users/nes123/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/nes123/subscriptions","organizations_url":"https://api.github.com/users/nes123/orgs","repos_url":"https://api.github.com/users/nes123/repos","events_url":"https://api.github.com/users/nes123/events{/privacy}","received_events_url":"https://api.github.com/users/nes123/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":16,"created_at":"2016-12-18T23:02:39Z","updated_at":"2017-05-06T03:43:02Z","closed_at":"2017-05-06T01:23:58Z","body":"I work with keras 1.0.2 with tensorflow.\r\nI try to freeze some layers in the network, it works well for convolutions and FC but not for the batch normalization layer. I print the weights of the layer before and after one epoch and I see a changes. any ideas ?"},"comment":{"url":"https://api.github.com/repos/fchollet/keras/issues/comments/299613104","html_url":"https://github.com/fchollet/keras/issues/4762#issuecomment-299613104","issue_url":"https://api.github.com/repos/fchollet/keras/issues/4762","id":299613104,"user":{"login":"fchollet","id":710255,"avatar_url":"https://avatars0.githubusercontent.com/u/710255?v=3","gravatar_id":"","url":"https://api.github.com/users/fchollet","html_url":"https://github.com/fchollet","followers_url":"https://api.github.com/users/fchollet/followers","following_url":"https://api.github.com/users/fchollet/following{/other_user}","gists_url":"https://api.github.com/users/fchollet/gists{/gist_id}","starred_url":"https://api.github.com/users/fchollet/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/fchollet/subscriptions","organizations_url":"https://api.github.com/users/fchollet/orgs","repos_url":"https://api.github.com/users/fchollet/repos","events_url":"https://api.github.com/users/fchollet/events{/privacy}","received_events_url":"https://api.github.com/users/fchollet/received_events","type":"User","site_admin":false},"created_at":"2017-05-06T03:43:02Z","updated_at":"2017-05-06T03:43:02Z","body":"If you doing fine-tuning and so on, then setting `trainable` to False is exactly what you want. The fact that BN will adapt to the statistics of your new data is precisely what you want."}}
{"actor":{"display_login":"fchollet"}}