28 Neural Networks

jHepWork contains the Joone packagehttp://www.jooneworld.com/, which is a neural network (NN) framework to create, train and test artificial neural networks. jHepWork attempts to use this package using Jython scripts.

A complete example how to train and verify the trained NN is located in the directory "macros/examples/neural_net": Click on the file "NN_train.py", and run it using the jHepWork editor. You will see a new tab with the log file, which contains the result of the NN training. This script reads the file "wine_train.txt" and the NN is trained using the first 150 raws of the data in this file. The first column represents the output, while other columns are input data. Note that the input data are normalized by the script. Then the NN is tested using 28 raws of the lines, and the result of this test is again written to the log file.

This "NN_train.py" script writes the NN in the serialized file "nn_wine.snet". To perform a forecast using the set of the data in the file "wine_forecast.txt", run the scrip "NN_forecast.py" (in this file, the output numbers are all set to zero).

This example is rather similar to that located in the file "Validation_using_stream.java" of the directory "samples/engine/helpers/" of the Joone package. There are a lot of explanations in the jHepWork NN scripts, but you still should read the API of Joone packagehttp://www.jooneworld.com/, and especially its manual. The NN scripts were build using the package "org.joone.samples.engine.helpers"