Tensorflow Layer Normalization and Hyper Networks

================================= Tensorflow implementation of Layer Normalization and Hyper Networks.

This implementation contains:

1. Layer Normalization for GRU

2. Layer Normalization for LSTM

   • Currently normalizing c causes lot of nan's in the model, thus commenting it out for now.

3. Hyper Networks for LSTM

4. Layer Normalization and Hyper Networks (combined) for LSTM

Prerequisites

• Python 2.7 or Python 3.3+
• NLTK
• TensorFlow >= 0.9

MNIST

To evaluate the new model, we train it on MNIST. Here is the model and results using Layer Normalized GRU

Usage

To train a mnist model with different cell_types:

$ python mnist.py --hidden 128 summaries_dir log/ --cell_type LNGRU

To train a mnist model with HyperNetworks:

$ python mnist.py --hidden 128 summaries_dir log/ --cell_type HyperLnLSTMCell --layer_norm 0

To train a mnist model with HyperNetworks and Layer Normalization:

$ python mnist.py --hidden 128 summaries_dir log/ --cell_type HyperLnLSTMCell --layer_norm 1

cell_type = [LNGRU, LNLSTM, LSTM , GRU, BasicRNN, HyperLnLSTMCell]

To view graph:

$ tensorboard --logdir log/train/

Todo

1. Add attention based models ( in progress ).