{"action":"created","issue":{"url":"https://api.github.com/repos/eaplatanios/tensorflow_scala/issues/88","repository_url":"https://api.github.com/repos/eaplatanios/tensorflow_scala","labels_url":"https://api.github.com/repos/eaplatanios/tensorflow_scala/issues/88/labels{/name}","comments_url":"https://api.github.com/repos/eaplatanios/tensorflow_scala/issues/88/comments","events_url":"https://api.github.com/repos/eaplatanios/tensorflow_scala/issues/88/events","html_url":"https://github.com/eaplatanios/tensorflow_scala/issues/88","id":303199924,"number":88,"title":"Regularization in Tensorflow Scala models","user":{"login":"mandar2812","id":1389553,"avatar_url":"https://avatars3.githubusercontent.com/u/1389553?v=4","gravatar_id":"","url":"https://api.github.com/users/mandar2812","html_url":"https://github.com/mandar2812","followers_url":"https://api.github.com/users/mandar2812/followers","following_url":"https://api.github.com/users/mandar2812/following{/other_user}","gists_url":"https://api.github.com/users/mandar2812/gists{/gist_id}","starred_url":"https://api.github.com/users/mandar2812/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mandar2812/subscriptions","organizations_url":"https://api.github.com/users/mandar2812/orgs","repos_url":"https://api.github.com/users/mandar2812/repos","events_url":"https://api.github.com/users/mandar2812/events{/privacy}","received_events_url":"https://api.github.com/users/mandar2812/received_events","type":"User","site_admin":false},"labels":[{"id":574448681,"url":"https://api.github.com/repos/eaplatanios/tensorflow_scala/labels/enhancement","name":"enhancement","color":"84b6eb","default":true}],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2018-03-07T17:57:43Z","updated_at":"2018-03-15T16:45:33Z","closed_at":null,"author_association":"NONE","body":"This is a bit of an open ended question, but from a practitioners perspective an important one I guess. How do I access/find out the regularization function being used in my model's learning process? I can see from the tensorflow_scala examples that the loss function can be explicitly specified and custom loss functions can be implemented, but how do we do the same for tweaking or extending regularizers?"},"comment":{"url":"https://api.github.com/repos/eaplatanios/tensorflow_scala/issues/comments/373444110","html_url":"https://github.com/eaplatanios/tensorflow_scala/issues/88#issuecomment-373444110","issue_url":"https://api.github.com/repos/eaplatanios/tensorflow_scala/issues/88","id":373444110,"user":{"login":"mandar2812","id":1389553,"avatar_url":"https://avatars3.githubusercontent.com/u/1389553?v=4","gravatar_id":"","url":"https://api.github.com/users/mandar2812","html_url":"https://github.com/mandar2812","followers_url":"https://api.github.com/users/mandar2812/followers","following_url":"https://api.github.com/users/mandar2812/following{/other_user}","gists_url":"https://api.github.com/users/mandar2812/gists{/gist_id}","starred_url":"https://api.github.com/users/mandar2812/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mandar2812/subscriptions","organizations_url":"https://api.github.com/users/mandar2812/orgs","repos_url":"https://api.github.com/users/mandar2812/repos","events_url":"https://api.github.com/users/mandar2812/events{/privacy}","received_events_url":"https://api.github.com/users/mandar2812/received_events","type":"User","site_admin":false},"created_at":"2018-03-15T16:45:33Z","updated_at":"2018-03-15T16:45:33Z","author_association":"NONE","body":"@eaplatanios I guess no matter which package holds the code to build the ops for regularizers, as long as one can specify the desired regularizer instance at the point of layer/network construction, it is fine from the usability point of view. And if we want to keep code expressing construction of layers/networks succinct (from an end user perspective), we can have sensible defaults in places like layer constructors `api.learn.layers.Linear()`.\r\n\r\nWhat do you think? Sorry to push the discussion, but I think this can be a crucial addition, because when using tf-scala for research applications, applying some/any regularization would be paramount."}}
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