A course project for CS236 Deep Generative Models at Stanford (Fall 2019). Under development.

Dev Logs

11/07/19

• init
• TODO: figure out the implementation of functions
• TODO: figure out how data are taken in
• TODO: figure out needs for data processing

11/08/19

• figured out working flow of asynchronous many-to-many prediction
• figured out basic network structure
• figured out requirements for time-series data
• tested LSTM with dummy 1d sinusoidal data, worked well
• testing LSTM with dummy 2d sinusoidal data, wait and see
• TODO: test dummy sinusoidal data on BBBRNN

11/09/19

• implemented some data utility functions
• processed stocks data
• ran training on stocks data, not so good for now
• TODO: tune stocks prediction training

11/11/19

• figured out structure of BBBRNN
• implemented time-series prediction model with BBB
• tested the model, training was intractable
• TODO: loss re-weighting with minibatch
• TODO: inspect details in the model
• TODO: debug training

11/12/19

• training of BBBLSTM can run smoothly
• PROBLEM: BBBLSTM always output 'straight lines', without much expressiveness
• PROBLEM: easily slow down
• PROBLEM: graph reset after each loss.backward(), had to use retain_graph=True
• TODO: inspect details in the model thoroughly
• TODO: implement evaluation methods (using MC sampling and using mean weights)

11/13/19

• problem located in loss re-weighting
• TODO: @arec extract data and save them as one torch.Tensor with shape = (full_seq_len, n_tot_sequences, feat_dim). Save the tensor using torch.save(data_tensor, 'data_tensor_name.pt') (to /data/processed).
• IDEA: using shared variance for each pred feat dim
• IDEA: using fixed variance for decoder output?

11/14/19

• outputting means only, data prob is computed using a constant variance (0.1, as homework 2 does in FSVAE)
• BBBLSTM was able to learn sinusoidal data pretty good
• highD data are processed