{"action":"closed","issue":{"url":"https://api.github.com/repos/fastai/fastai/issues/583","repository_url":"https://api.github.com/repos/fastai/fastai","labels_url":"https://api.github.com/repos/fastai/fastai/issues/583/labels{/name}","comments_url":"https://api.github.com/repos/fastai/fastai/issues/583/comments","events_url":"https://api.github.com/repos/fastai/fastai/issues/583/events","html_url":"https://github.com/fastai/fastai/issues/583","id":335757334,"node_id":"MDU6SXNzdWUzMzU3NTczMzQ=","number":583,"title":"how to  implement Averaging Weights Leads to Wider Optima and Better Generalization in keras ","user":{"login":"fryng","id":25688907,"node_id":"MDQ6VXNlcjI1Njg4OTA3","avatar_url":"https://avatars0.githubusercontent.com/u/25688907?v=4","gravatar_id":"","url":"https://api.github.com/users/fryng","html_url":"https://github.com/fryng","followers_url":"https://api.github.com/users/fryng/followers","following_url":"https://api.github.com/users/fryng/following{/other_user}","gists_url":"https://api.github.com/users/fryng/gists{/gist_id}","starred_url":"https://api.github.com/users/fryng/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/fryng/subscriptions","organizations_url":"https://api.github.com/users/fryng/orgs","repos_url":"https://api.github.com/users/fryng/repos","events_url":"https://api.github.com/users/fryng/events{/privacy}","received_events_url":"https://api.github.com/users/fryng/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-06-26T10:34:25Z","updated_at":"2018-06-26T21:45:16Z","closed_at":"2018-06-26T21:45:16Z","author_association":"NONE","body":"I'm currently implementing Averaging Weights Leads to Wider Optima and Better Generalization in keras ,the source code(Pytorch) is here [https://github.com/fastai/fastai/blob/master/fastai/swa.py]( ) , but i'm stucking in the **fix_batchnorm()** function. how could i update the params(moving_mean,moving_variance) in batchnorm layers by passing the inputs? In Pytorch, u just do like this `model(inputs)`, In keras , `model.predict(inputs)` doesn't work\r\n"}}
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