Tensorflow implementation of Quasi-Recurrent Neural Networks (QRNN). The QRNN layer is implemented in the qrnn.py file. The original blog post with code reference can be found here.

A QRNN layer is composed of a convolutional stage (red blocks in the figure) and a pooling stage (blue blocks in the figure):

• The convolutional stage can perform the different activations in parallel (i.e. layer activations and gate activations) through time, such that each red sub-block is independent.

• The pooling stage imposes the ordering information across time, and although it is a sequential process (as depicted by the arrow) the heavy computation has already been performed in a single forward pass in the convolutional stage!

The figure below shows that QRNN is a mixture between CNN and LSTM, where we get the best of both worlds: make all activation computations in parallel with convolutions and merge sequentially, with no recursive weight operations.

This work contains an implementation of the language model experiment on the Pen TreeBank (PTB) dataset.

To execute the PTB language model experiment w/ train and test stages altogether:

python train_lm.py

To re-make the data tensors for any corpus, remove the vocab.pkl.gz file generated within the corpus data dir. That will re-set the vocab and dataset generation.

The following training is done a bit differently than in original paper for PTB task: A zoneout factor of 0.1 was applied without dropout between hidden layers nor L2 reg.

python train_lm.py --zoneout 0.1 --dropout 0

• Work in the sentiment analysis task too
• Implement a wrapper to stack multiple QRNNs
• Set up the possibility of dense connections within the stack
• Implement a seq2seq wrapper

Santi Pdp ( @santty128 )