Example code: Sequence to Sequence mapping using encoder-decoder RNNs using dpnn and rnn libraries

• 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