{"action":"opened","issue":{"url":"https://api.github.com/repos/tensorflow/tensorflow/issues/5831","repository_url":"https://api.github.com/repos/tensorflow/tensorflow","labels_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/5831/labels{/name}","comments_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/5831/comments","events_url":"https://api.github.com/repos/tensorflow/tensorflow/issues/5831/events","html_url":"https://github.com/tensorflow/tensorflow/issues/5831","id":191525868,"number":5831,"title":"Is there a backpropagation-method for all math-layers i.e. tf.fft2d?","user":{"login":"beniroquai","id":4345528,"avatar_url":"https://avatars.githubusercontent.com/u/4345528?v=3","gravatar_id":"","url":"https://api.github.com/users/beniroquai","html_url":"https://github.com/beniroquai","followers_url":"https://api.github.com/users/beniroquai/followers","following_url":"https://api.github.com/users/beniroquai/following{/other_user}","gists_url":"https://api.github.com/users/beniroquai/gists{/gist_id}","starred_url":"https://api.github.com/users/beniroquai/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/beniroquai/subscriptions","organizations_url":"https://api.github.com/users/beniroquai/orgs","repos_url":"https://api.github.com/users/beniroquai/repos","events_url":"https://api.github.com/users/beniroquai/events{/privacy}","received_events_url":"https://api.github.com/users/beniroquai/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-11-24T13:55:30Z","updated_at":"2016-11-24T13:55:30Z","closed_at":null,"body":"When building a deep NN, where one of the layer is represented by a fouriertransform (i.e. tf.fft), does tensorflow provide a backpropagation method for it? In this case it could/would be complex I guess. \r\nHow does the optimizer work on that? Are there any documents which could explain this behavior? \r\n\r\nIn theory, I understand how the gradient of a multiplication is carried out, but doing this for a more complicated function makes things more difficult for me. \r\n\r\nThank you very much :) "}}
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