rdevooght
commented
7 years ago Hi,
Regarding your first question: Firstly I have to confess that all the parts about movies_features, optional_features, etc. is kind of legacy code that I used in my experiments to exploit extra information from the movielens dataset, but I didn't want it in this public repo because it worked only on movielens. I removed some of it from the code, but some pieces stayed in (partly due to laziness, and partly because I hope to be able to generalize it to any dataset at some point). That being said, here is what's happening in the lines you pointed to: using the argument "true_input_size" when constructing the recurrent layer will cause the recurrent layer to accept a sparse encoding of the inputs. Unfortunately, in my implementation all the inputs must have the same number of non-zero element (e.g. one for the movie id, one for the rating and one for the user age = 3 non-zero element). This doesn't work when we use the "movies_features" of movielens, because those features are actually movies genres, and a movie can have any number of genres, which means that the number of non-zero elements in the input is not the same for every movie. Therefore when you use those movie features the recurrent layer will use a dense encoding of the input (you can see this difference reflected in the _get_features method of the RNNBase class). This could have been avoided with a more flexible type of sparse encoding, but it required more involved modification of the Lasagne recurrent layers.
Second question: self.recurrent_layer() does work as intended. It indeed returns only one layer (the last one), but this layer has a reference to its input layer, which has a reference to its input layer, and so on. So by returning only the last layer I actually return the whole network.
Third one: Indeed, l_last_slice is exactly the same as l_recurrent and I could have simply used l_recurrent. This is a relic of a time when they referred to different things, but I should clean that up now.
I hope this helps !
kwonmha
hello. I read your paper and am looking into your codes. And got some questions about rnn.
in rnn_one_hot.py : _prepare_networks()
at your code : if not self.use_movies_features: l_recurrent = self.recurrent_layer(self.l_in, self.l_mask, true_input_size = self.n_items + self.n_optional_features(), only_return_final=True) there's NOT in if condition. why did you set true_input_size like above when use_movies_features is false?
self.recurrent_layer() calls call () of recurrent_layers.py It seems that it only returns 1 layer - prev_layer, though you write for loop in that part. I think it returns multiple layers when RecurrentLayers.layers is set like 100-50-50 like your example Does it work as you intended?
what does l_last_slice do? Should it exist?
TY for your help.