This is the code used for the experiments in the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks". The sentiment analysis experiment relies on a fork of keras which implements Bayesian LSTM, Bayesian GRU, embedding dropout, and MC dropout. The language model experiment extends wojzaremba's lua code.
keras now supports dropout in RNNs following the implementation above. A simplified example of the sentiment analysis experiment using the latest keras implementation is given in here.
The script main_new_dropout_SOTA implements Bayesian LSTM (Gal, 2015) for the large model of Zaremba et al. (2014). In the setting of Zaremba et al. the states are not reset and the testing is done with a single pass through the test set. The only changes I've made to the setting of Zaremba et al. are:
- dropout technique (using a Bayesian LSTM)
- weight decay (which was chosen to be zero in Zaremba et al.)
- a slightly smaller network was used to fit on my GPU (1250 units per layer instead of 1500)
All other hypers being identical to Zaremba et al.: learning rate decay was not tuned for my setting and is used following Zaremba et al., and the sequences are initialised with the previous state following Zaremba et al. (unlike in main_dropout.lua). Dropout parameters were optimised with grid search (tying dropout_x & dropout_h and dropout_i & dropout_o) over validation perplexity (optimal values are 0.3 and 0.5 compared Zaremba et al.'s 0.6).
Single model validation perplexity is improved from Zaremba et al.'s 82.2 to 79.1. Test perplexity is reduced from 78.4 to 76.5, see log. Evaluating the model with MC dropout with 2000 samples, test perplexity is further reduced to 75.06 (with 100 samples test perplexity is 75.3).
I updated the code with the experiments used in the arXiv paper revision from 25 May 2016 (version 3). In the updated code restriction 3 above (smaller network size) was removed, following a Lua update that solved a memory leak. main_new_dropout_SOTA_v3 implements the MC dropout experiment used in the paper, with single model test perplexity improved from Zaremba et al.'s 78.4 to 73.4 (using MC dropout at test time) and 75.2 with the dropout approximation. Validation perplexity is reduced from 82.2 to 77.9.
References:
- Gal, Y, "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks", 2015.
- Zaremba, W, Sutskever, I, Vinyals, O, "Recurrent neural network regularization", 2014.