title: Unconvential use of a RNN/LSTM
body: <p><strong>If I well understood a RNN</strong> allows to predict the next value considering the last values in a sequence. Let say I want to predict the next value of the function cos(x) and I have a dataset with the results for x in range(0, 1000). First I feed the model with cos(0) to predict cos(1), then cos(1) to predict cos(2) etc.. and at each step the weights are tuned and the model keep a memory of the last values to make the next prediction.</p>
  
  <p><strong>In my case</strong> I want to train a model to predict the quality of videos. For this I have a dataset with videos annotated. For each video, for each frame, I compute a set of 36 features which are not spatially related. So the shape of the inputs is (nb_videos, nb_frames, 36). For each video I have a score representing the global video quality and the shape of the labels is (nb_videos, 1).</p>
  
  <p><strong>I don't know which kind of NN I can use</strong>. n_frames x 36 is far too big I think for a simple multi-layer perceptron. Features may make sense along time axis but not along features axis, so unless I train 36 models with 1D convolutions a CNN is useless. Finally, features come in a sequence but the problem with a RNN is that it need a score for each element of the sequence and the model works only to predict the next values in this sequence in particular.</p>
  
  <p><strong>My idea</strong> is to have 1 RNN model which is trained for any video. I feed the RNN n_frames times with the 36 features in the good order and only after these n_frames iterations the model give a prediction. Then this prediction is used to tune the weights. Then we do this the number of epochs with a video picked randomly in the dataset.</p>
  
  <p>Does it make sense?</p>
  
  <p>Does something similar exist?</p>