RNN-TD

This repository enables the reproduction of the experiments described in the article:

Yongqing Wang, Shenghua Liu, Huawei Shen, Jinhua Gao and Xueqi Cheng. Marked temporal dynamics modeling based on recurrent neural network. The 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-17). Jeju, South Kore, 2017: pp. 786-798.

The dataset used in the project can be found in my personal website

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Content

• Requirements
• Usage
  • Install the project
  • (Optional) Compile and packaging by Eclipse
  • Running
• Specfication
  • The architecture of "src" directory

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Requirements

• maven==3.*
• jdk==1.8
• (optional) Eclipse

You'd better install maven plugin in eclipse (the lastest version has already installed maven plugin)

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Usage

Install the project

git clone git@github.com:Allen517/rnntd.git
cd rnntd
mvn clean install

If you want to load the project in Eclipse, you should run the following command

mvn eclipse:eclipse

(Optional) Compile and packaging by Eclipse

• Import a project and import "Existing Projects into Workspace"

• Click "Browse", choose "rnntd" project and click "Finish"

• Export a "Runnable JAR file"

Right click on the main procedure

Choose "Export"

Choose "Runnable JAR file"

Completed

Running

java -jar rnntd.jar config

move the runnable jar (e.g., the jar file is called "cyanrnn.jar") into the directory of cyanrnn_project

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Specfication

The architecture of "src" directory

• main.java.com.kingwang.netattrnn

batchderv (When minibatch is finished, batchderv will average the derivation in all batches.)

BatchDerivative.java: interface of BatchDerivative

impl

--GRUBatchDerivative.java: for GRU (RNN)

--InputBatchDerivative.java: for input layer

--LSTMBatchDerivative.java: for LSTM (RNN)

--OutputBatchDerivative.java: for output layer

cell

--Cell.java: interface of RNN layers

--Operator.java: basic operator for RNN layers

impl

--GRU: GRU implementation

--LSTM: LSTM implementation

--InputLayer: Input layer implementation

--OutputLayer: Output layer implementation

main

RNNTD: Main procedure of RNN-TD

comm/utils: Common utilities

cons: Constants

dataset: Implementation of loading dataset

evals: Implementation of RNN-TD validation in tranining process

utils: Common utilities for RNN-TD

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