TensorFlow With The Latest Papers Implemented

###Implementation of RNN and NLP Related Neural Network Papers

Currently Implemented Papers:

Highway Networks
Recurrent Highway Networks
Multiplicative Integration Within RNNs
Recurrent Dropout
GRU Mutants

More Papers to come as they are published. If you have any requests, please use the issues section.

###Contact Information:

skype: lea vesbr eat he thisisjunk(eliminate all spaces and ignore junk part)

email: sh a hn s [at ] m ail.u c.ed u thisisjunk(eliminate all spaces and ignore junk part)

To Test These New Papers

If you would like to test these new features, you can:

python ptb_word_lm.py

Simply modify the rnn_cell variable under the PTBModel

Please run with Tensorflow 0.8 or higher

Highway Networks

https://arxiv.org/abs/1505.00387

Allows greater depth of neural network freely without penalty from upper layers. Ensures shortcut connections within deeper layers.

Note, there is an optional flag use_inputs_on_each_layer that is boolean. Turning this option to False saves network parameters but also may not yield most optimal results. If you would like to replicate the paper, leave this option as False.

import highway_networks_modern

output = highway_networks_modern.highway(inputs, num_layers = 3)

Recurrent Highway Networks

http://arxiv.org/abs/1607.03474

Allows multiple stacking of layers within one cell to increase depth per timestep.

import rnn_cell_modern

cell = rnn_cell_modern.HighwayRNNCell(num_units, num_highway_layers = 3)

Multiplicative Integration Within RNNs

https://arxiv.org/abs/1606.06630

Allows faster convergence within RNNs by utilizing the combination of two separate weight matrices in a multiplicative setting

import rnn_cell_mulint_modern

cell = rnn_cell_mulint_modern.BasicRNNCell_MulInt(num_units)
#OR
cell = rnn_cell_mulint_modern.GRUCell_MulInt(num_units)
#OR
cell = rnn_cell_mulint_modern.BasicLSTMCell_MulInt(num_units)
#OR
cell = rnn_cell_mulint_modern.HighwayRNNCell_MulInt(num_units, num_highway_layers = 3)

Recurrent Dropout Without Memory Loss

http://arxiv.org/pdf/1603.05118v1.pdf

Implement recurrent dropout within multiplicative integration rnn cells. Will allow rnn cell's memory to be more versatile.

import rnn_cell_mulint_modern

#be sure to change recurrent_dropout_value to 1.0 during testing or validation
#alternatively, you can set the is_training argument to False during testing or validation but this requires the reconstruction of the model

cell = rnn_cell_mulint_modern.BasicLSTMCell_MulInt(num_units, use_recurrent_dropout = True, recurrent_dropout_value = 0.90)
#OR
cell = rnn_cell_mulint_modern.GRUCell_MulInt(num_units,  use_recurrent_dropout = True, recurrent_dropout_value = 0.90)
#OR
cell = rnn_cell_mulint_modern.BasicLSTMCell_MulInt(num_units, use_recurrent_dropout = True, recurrent_dropout_value = 0.90)
#OR
cell = rnn_cell_mulint_modern.HighwayRNNCell_MulInt(num_units, num_highway_layers = 3,  use_recurrent_dropout = True, recurrent_dropout_value = 0.90)

GRU Mutants

http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf

Mutants of GRU that may work better in different scenarios:

import rnn_cell_modern

cell = rnn_cell_modern.JZS1Cell(num_units)
#Or
cell = rnn_cell_modern.JZS2Cell(num_units)
#Or
cell = rnn_cell_modern.JZS3Cell(num_units)

zhmz90 / tensorflow_with_latest_papers