{"action":"created","issue":{"url":"https://api.github.com/repos/craffel/nntools/issues/15","labels_url":"https://api.github.com/repos/craffel/nntools/issues/15/labels{/name}","comments_url":"https://api.github.com/repos/craffel/nntools/issues/15/comments","events_url":"https://api.github.com/repos/craffel/nntools/issues/15/events","html_url":"https://github.com/craffel/nntools/issues/15","id":59270542,"number":15,"title":"Should we include sequence prediction ability using single input for LSTM(or generally RNN)","user":{"login":"gaoyuankidult","id":1174296,"avatar_url":"https://avatars.githubusercontent.com/u/1174296?v=3","gravatar_id":"","url":"https://api.github.com/users/gaoyuankidult","html_url":"https://github.com/gaoyuankidult","followers_url":"https://api.github.com/users/gaoyuankidult/followers","following_url":"https://api.github.com/users/gaoyuankidult/following{/other_user}","gists_url":"https://api.github.com/users/gaoyuankidult/gists{/gist_id}","starred_url":"https://api.github.com/users/gaoyuankidult/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/gaoyuankidult/subscriptions","organizations_url":"https://api.github.com/users/gaoyuankidult/orgs","repos_url":"https://api.github.com/users/gaoyuankidult/repos","events_url":"https://api.github.com/users/gaoyuankidult/events{/privacy}","received_events_url":"https://api.github.com/users/gaoyuankidult/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":16,"created_at":"2015-02-27T17:50:55Z","updated_at":"2015-08-13T13:27:23Z","closed_at":null,"body":"Normally when we train LSTM, we unfold it for certain time steps and train it with same length data.\r\nHowever after training, we would like to just give an initial data to let the LSTM network to predict whole sequence.\r\n\r\nDo we have this function in this library? If not, I think I can implement this function in code.\r\n\r\nthanks.\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/craffel/nntools/issues/comments/130670283","html_url":"https://github.com/craffel/nntools/issues/15#issuecomment-130670283","issue_url":"https://api.github.com/repos/craffel/nntools/issues/15","id":130670283,"user":{"login":"skaae","id":2623134,"avatar_url":"https://avatars.githubusercontent.com/u/2623134?v=3","gravatar_id":"","url":"https://api.github.com/users/skaae","html_url":"https://github.com/skaae","followers_url":"https://api.github.com/users/skaae/followers","following_url":"https://api.github.com/users/skaae/following{/other_user}","gists_url":"https://api.github.com/users/skaae/gists{/gist_id}","starred_url":"https://api.github.com/users/skaae/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/skaae/subscriptions","organizations_url":"https://api.github.com/users/skaae/orgs","repos_url":"https://api.github.com/users/skaae/repos","events_url":"https://api.github.com/users/skaae/events{/privacy}","received_events_url":"https://api.github.com/users/skaae/received_events","type":"User","site_admin":false},"created_at":"2015-08-13T13:27:23Z","updated_at":"2015-08-13T13:27:23Z","body":"No. The encoder model wil encode your model into some hidden representation. The decode will then somehow use that representation to produce the output. In the simplest case you'll use a RNN as encoder and use the last hidden state of the encoder to initialize the decoder which is also an RNN. Other variants use softmax attention etc. \r\n\r\nIn the language model you try to predict the next word given the previous words. This can easily be done in Lasagne. Take a look at the language model pull reguest in lasagne recipe's.\r\n\r\nYou can also have a look at http://karpathy.github.io/2015/05/21/rnn-effectiveness/"}}