I am implementing the sequence-to-sequence model introduced by this paper 27 from Google Magenta:
My code is here:

  
      
      GitHub
  
  
    

alexis-jacq/Pytorch-Sketch-RNN 144

a pytorch implementation of https://arxiv.org/abs/1704.03477 - alexis-jacq/Pytorch-Sketch-RNN


  
  
    
    
  
  


As I don’t have Google’s power of computation, I changed a bit some hyperparameters of the papers (the number of nodes in the decoder LSTM and the learning rate) and it takes me half a day to run 10.000 epochs. However, according to the default hyperparameters suggested by the autor’s TF implementation 15, 512 neurons in decoder LSTM and lr = 0.0001 should be ok to get good results.
But, at least after 40.000 epochs, it still generates random straight strokes and even the reconstruction (giving true inputs from the training set to the decoder) is similar to the drawing but not satisfying. Something must be wrong somewhere… If any of you is interested by state-of-the-art seq2seq drawing generation, I would be glad to receive some help.
I really need to make it work, its for a robotic project (making collaborative child-robot hand-drawing)