{"action":"opened","issue":{"url":"https://api.github.com/repos/Element-Research/rnn/issues/86","labels_url":"https://api.github.com/repos/Element-Research/rnn/issues/86/labels{/name}","comments_url":"https://api.github.com/repos/Element-Research/rnn/issues/86/comments","events_url":"https://api.github.com/repos/Element-Research/rnn/issues/86/events","html_url":"https://github.com/Element-Research/rnn/issues/86","id":122389187,"number":86,"title":"Proper data format","user":{"login":"cwellsarnold","id":14990106,"avatar_url":"https://avatars.githubusercontent.com/u/14990106?v=3","gravatar_id":"","url":"https://api.github.com/users/cwellsarnold","html_url":"https://github.com/cwellsarnold","followers_url":"https://api.github.com/users/cwellsarnold/followers","following_url":"https://api.github.com/users/cwellsarnold/following{/other_user}","gists_url":"https://api.github.com/users/cwellsarnold/gists{/gist_id}","starred_url":"https://api.github.com/users/cwellsarnold/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cwellsarnold/subscriptions","organizations_url":"https://api.github.com/users/cwellsarnold/orgs","repos_url":"https://api.github.com/users/cwellsarnold/repos","events_url":"https://api.github.com/users/cwellsarnold/events{/privacy}","received_events_url":"https://api.github.com/users/cwellsarnold/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-12-15T23:06:51Z","updated_at":"2015-12-15T23:06:51Z","closed_at":null,"body":"Hi all,\r\nI am trying to do sequence tagging (i.e., a label for each token in a sequence) using the `BiSequencer` model with LSTMs and have been running into some trouble trying to determine how to format my input data and targets. I have built a `dp.DataSource` with `dp.DataSet`s. Each `dp.DataSet` contains two `dp.ClassView`s using `torch.IntTensor`s. The shape of my input and target tensors is `num_samples` x `num_timesteps`, where `num_timesteps` is the context size (i.e., the number of words to use before and after the current word being predicted). Each element in the tensor is the index of a word in my collection (or the word's label for the target vector), which I have computed using my own preprocessing tool. I am using a `LookupTable` and `SplitTable` in my model to convert these indices to embeddings.\r\n\r\nWhen I run my data through the network, my output is a table with `num_timestep` rows, with each row containing a `dp.DoubleTensor` of size `batch_size` x `num_classes` (this, by the way, causes a problem with the `Confusion` feedback object as it does not expect a table). However, the target tensor is of size `batch_size`. I would have expected it to be `num_timesteps` x `batch_size`?  \r\n\r\nSo, in short, how should I format my data in this case? Thanks for any help!!!"}}