Demo code for creating WordVector models through connecting DeepLearning4J with Postgres on the Reuters-21578 dataset

The purpose of this code is to primarily show how DeepLearning4J can train a WordVector model from data contained in a common RDBMS database rather than flat files or a distributed database system.

Additionally, the code shows a custom pre-processor and custom iterators over the database ResultSet object.

1. Download the SGM files: ./downloadReutersSGM.sh

2. Setup the Database:

• Copy the code from setupDbTable.sql into your database server
• Optional: Change the pom.xml file if you are using something other than postgres as your database

3. Build the project: mvn package

4. Extract the SGM files into the DB - this creates 21,788 rows in the table

java -cp target/reutersDbWvDemo-1.0-SNAPSHOT.jar au.edu.unsw.cse.ExtractSGM 'jdbc:postgresql://server.domain:port/database?user=username&password=password' reuters21578sgm/ <jdbcClassName>

java -cp target/reutersDbWvDemo-1.0-SNAPSHOT.jar au.edu.unsw.cse.BuildWordVectorsFromDatabase 'jdbc:postgresql://server.domain:port/database?user=username&password=password' <iterations> <layer size> <output filename> <sql query> <sql column name> <jdbcClassName>

java -cp target/reutersDbWvDemo-1.0-SNAPSHOT.jar au.edu.unsw.cse.WordVectorChecker