{"action":"closed","issue":{"url":"https://api.github.com/repos/fchollet/keras/issues/1606","labels_url":"https://api.github.com/repos/fchollet/keras/issues/1606/labels{/name}","comments_url":"https://api.github.com/repos/fchollet/keras/issues/1606/comments","events_url":"https://api.github.com/repos/fchollet/keras/issues/1606/events","html_url":"https://github.com/fchollet/keras/issues/1606","id":130168796,"number":1606,"title":"Forward pass with dropout","user":{"login":"beamandrew","id":3758954,"avatar_url":"https://avatars.githubusercontent.com/u/3758954?v=3","gravatar_id":"","url":"https://api.github.com/users/beamandrew","html_url":"https://github.com/beamandrew","followers_url":"https://api.github.com/users/beamandrew/followers","following_url":"https://api.github.com/users/beamandrew/following{/other_user}","gists_url":"https://api.github.com/users/beamandrew/gists{/gist_id}","starred_url":"https://api.github.com/users/beamandrew/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/beamandrew/subscriptions","organizations_url":"https://api.github.com/users/beamandrew/orgs","repos_url":"https://api.github.com/users/beamandrew/repos","events_url":"https://api.github.com/users/beamandrew/events{/privacy}","received_events_url":"https://api.github.com/users/beamandrew/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":1,"created_at":"2016-01-31T19:03:15Z","updated_at":"2016-01-31T19:41:02Z","closed_at":"2016-01-31T19:41:02Z","body":"I'm looking for a way to do something that seems conceptually simple, but I haven't found a way to do it in Keras yet. I have a CNN with dropout applied in various places in the network. After training, I'd like to compute the forward pass on some data and have the dropout mask applied such that each time I compute the forward pass a different dropout mask is applied. \r\n\r\nIf I understand things correctly, this is what is done during training. However, if I use the standard model.predict(X_test) after training, no dropout is applied and the outputs are multiplied by the dropout probability, yielding a non-stochastic set of predictions. \r\n\r\nDoes anyone know how I can achieve this? "}}