Let’s say i have a data field named movie_genre for each sample  movie , it is selected from the following genres:
Action
Adventure
Animation
Comedy
...

And for each  movie , it might contain multiple genres:
mid    genres
1      Action | Adventure
2      Animation
3      Comedy | Adventure | Action

which means, the movie’s genres is a variable list.
If i use one hot vector to encode the  genre , Action can be encoded as (1, 0, 0, 0), Adventure can be encoded as(0, 1, 0, 0), and so on.
So movie with mid1 can be encoded as (1, 1, 0, 0), mid2’s genre can be encoded as (0, 0, 1, 0), and so on.
However, the pytorch embedding layer  nn.Embedding  takes tensor containing the indices as input, but not one-hot vector. So how should i encode the data so that it can be fetched into the embedding layer?
here’s the relative link in stackoverflow

  
      stackoverflow.com
  
  
      
    
  

  How to encode categorical data that have variable length so could be fetched to nn.Embedding in PyTorch 103



  machine-learning, pytorch, data-mining, encode



  asked by
  
  
    user8510613
  
  on 02:06PM - 10 Jul 19 UTC