An Empirical Exploration of Recurrent Network Architectures. Jozefowicz, Zaremba, SutskeverAR

  1. Vanilla RNNs are usually difficult to train.  LSTMS are a form of RNN that are easier to train
  2. LSTMs though, have arch that “appears to be ad-hoc so it is not clear if it is optimal, and the significance of its individual components is unclear.”
  3. Tested thousands of different models with different architectures based on LSTM, and also compared new Gated Recurrent Units
  4. “We found that adding a bias of 1 to the LSTM’s forget gate closes the gap between the LSTM and the GRU.”
  5. RNNs suffer from exploding/vanishing gradients (the latter was addressed successfully in LSTMs)
    1. There are many other ways to work on the vanishing gradient, such as regularization, second-order optimization, “giving up on learning the recurrent weights altogether”, as well as careful weight initialization
  6. Exploding gradients were easier to address with “a hard constraint over the norm of the gradient”
    1. Later referred to as “gradient clipping”
  7. “We discovered that the input gate is important, that the output gate is unimportant, and that the forget gate is extremely significant on all problems except language modelling. This is consistent with Mikolov et al. (2014), who showed that a standard RNN with a hard-coded integrator unit (similar to an LSTM without a forget gate) can match the LSTM on language modeling.”
  8. exploding/vanishing gradients “are caused by the RNN’s iterative nature, whose gradient is essentially equal to the recurrent weight matrix raised to a high power. These iterated matrix powers cause the gradient to grow or to shrink at a rate that is exponential in the number of timesteps.”
  9. Vanishing gradient issue in RNNs make it easy to learn short-term interactions but not long-term
  10. Through reparameterizing, LSTM cannot have a gradient that vanishes
  11. Basically, instead of recomputing weights from weights at the previous state, it only computes a weight delta which is added to the previous weights
    1. The network has additional machinery to do so
    2. Many LSTM variants
  12. Random initialization of the forget gate will leave it with some fractional value, which introduces a vanishing gradient.
    1. It is commonly ignored, but initializing it to a “large value” such as 1 or 2 will prevent vanishing gradient over time
  13. Use genetic algorithms to optimize architecture and hyperparams
  14. Evaluated 10,000 architectures, 1,000 made them past the first task (which would allow them to compete genetically).  Total of 230,000 hyperparameter configs tested
  15. Three problems tested:
    1. Arithmetic: read in a string which has numbers with an add or subtract symbol inside, then the network has to feed out the output.  There are distractor symbols in the string that need to be ignored
    2. Completion of a random XML dataset
    3. Penn Tree-Bank (word level modelling)
    4. Then there was an extra task to test generalization <validation?>
  16. “Unrolled” RNNs for 35 timesteps, minibatch of size 20
  17. Had a schedule for adjusting the learning rate once learning stopped on the initial value
    1. <nightmare>
  18. “Though there were architectures that outperformed the LSTM on some problems, we were unable to find an architecture that consistently beat the LSTM and the GRU in all experimental conditions.”
  19. “Importantly, adding a bias of size 1 significantly improved the performance of the LSTM on tasks where it fell behind the GRU and MUT1. Thus we recommend adding a bias of 1 to the forget gate of every LSTM in every application”

6 thoughts on “An Empirical Exploration of Recurrent Network Architectures. Jozefowicz, Zaremba, SutskeverAR

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