Implementation of Simple Recurrent Unit in Keras. Paper - Training RNNs as Fast as CNNs

This is a naive implementation with some speed gains over the generic LSTM cells, however its speed is not yet 10x that of cuDNN LSTMs

• Fix the need to unroll the SRU to get it to work correctly

• -Input dim must exactly match the number of LSTM cells for now. Still working out how to overcome this problem.-

No longer a problem to have different input dimension than output dimension.

• Performance of a single SRU layer is slightly lower (about 0.5% on average over 5 runs) compared to 1 layer LSTM (at least on IMDB, with batch size of 32). Haven't tried staking them yet, but this may improve performance.

Performance degrades substantially with larger batch sizes (about 6-7% on average over 5 runs) compared to 1 layer LSTM with batch size of 128. However, a multi layer SRU (I've tried with 3 layers), while a bit slower than a 1 layer LSTM, gets around the same score on batch size of 32 or 128.

Seems the solution to this is to stack several SRUs together. The authors recommend stacks of 4 SRU layers.

• Speed gains aren't that impressive at small batch size. At batch size of 32, SRU takes around 32-34 seconds. LSTM takes around 60-70 seconds. Thats just 50% reduction in speed, not the 5-10x that was discussed in the paper.

However, once batch size is increased to 128, SRU takes just 7 seconds per epoch compared to LSTM 22 seconds. For comparison, CNNs take 3-4 seconds per epoch.