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Opened 10月 03, 2017 by saxon_zh@saxon_zhGuest2 of 8 tasks completed2/8 tasks

RNN roadmap for refactoring

Created by: Superjomn

RNN is a big concept to support in our refactoring. Based on the current support of our framework, it is better to implement RNNs in following stages:

  • recurrent_op, an RNN operator that takes a tensor as input
  • dynamic_recurrent_op, an RNN that takes variable-length sequences as input, and output sequences as output
    • should replace the recurrent_op
  • built-in beam search, that will be a method in dynamic_recurrent_op and make the dynamic_recurrent_op an equivalent of the old RecurrentGradientMachine.
  • dynamic RNN based on while_loop and some other conditional operators, after we support this, the infrastructure might apply to some other dynamic models such as Tree-LSTM and so on.
  • dynamic beam search, that is a beam search built on while_loop and other conditional operations.

Milestones

  • support neural machine translation model
    • dynamic_recurrent_op with a built-in beam search module should be ready at that point.
  • a text classification model with dynamic RNNs, the operators not limited to the following ones should be ready
    • pd.while_loop
    • pd.equals
    • pd.TensorArray
    • pd.less_than
  • machine translation model based on dynamic beam search (maybe wrapped as generator)
    • that needs more dynamic operators ready
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标识: paddlepaddle/Paddle#4561
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