adapted from encoder-decoder-coupling example from Element-research/rnn/examples and char-rnn by Karpathy

This example includes -

• multiple LSTM layered encoder-decoder
• dropout between stacked LSTM layers
• input sequences can be of any length
  • I'm not aware of effects of arbitrary length sequences during training for real world tasks
  • inside a batch, all the sequences should be of the same length or you'll get an exception
  • to form batch from variable length sequences, use padding
    • recommended padding style is: {0,0,0,GO,1,2,3,4} for encoder and {1,2,3,4,EOS,0,0,0} for the decoder
• validation, early-stopping
• using RMSProp, can easily change to another optimization procedure supported by optim package e.g. adam/adagrad for training
• saving model at predefined checkpoints and resuming training from saved model
• running on nvidia GPU
• two Synthetic data sets

NOTE on using a saved model

• If you run your experiment on GPU then before using the saved model, convert it to a cpu model first using convert_gpuCheckpoint_to_cpu.lua

Choose from two tasks

  th seq2seq.lua -synthetic 1
  th seq2seq.lua -synthetic 2

TODO -

• sampling from saved model
• removing teacher forcing