Added example of JRuby calling Java neural network code

mark-watson · mark-watson · commit 617a2eb15bef · 2013-06-30T08:21:45.000-07:00
diff --git a/jruby_examples/neural_network/Makefile b/jruby_examples/neural_network/Makefile
@@ -0,0 +1,10 @@
+clean:
+	rm -r -f *.jar temp_build
+
+jar:
+	rm -r -f temp_build
+	mkdir -p temp_build/neuralnetworks
+	cp ../../src/neuralnetworks/Neural_2H_momentum.java temp_build/neuralnetworks/
+	(cd temp_build; javac neuralnetworks/Neural_2H_momentum.java)
+	(cd temp_build; jar cvf ../nn.jar .)
+	rm -r -f temp_build
diff --git a/jruby_examples/neural_network/README.md b/jruby_examples/neural_network/README.md
@@ -0,0 +1,17 @@
+# Using the Backpropagation neural network example in JRuby
+
+One reason why I like Java is that it is easy to package up Java code and use with
+other JVM languages like JRuby, Clojure, and Scala.
+
+Here is an example using a Makefile (works on Max OS X and Linux - for Windows developers, it would
+be great if someone would provide me with a Windows .bat command file) to build a JAR file
+and using it in a small JRuby test program
+
+~~~~~~~~
+make jar
+jruby test.rb
+~~~~~~~~
+
+This is a simple example with a tiny amount of training data. The Java code that this example uses
+can be used to train very large networks but you must increase the number of training iterations.
+
diff --git a/jruby_examples/neural_network/nn.jar b/jruby_examples/neural_network/nn.jar
diff --git a/jruby_examples/neural_network/test.rb b/jruby_examples/neural_network/test.rb
@@ -0,0 +1,35 @@
+require 'java'  # for Java interop
+
+require 'nn.jar' # load the neural netowrk compiled Java code
+java_import 'neuralnetworks.Neural_2H_momentum'
+
+## training data to rotate three input values:
+
+in1 = [0.1, 0.1, 0.9]
+in2 = [0.1, 0.9, 0.1]
+in3 = [0.9, 0.1, 0.1]
+
+out1 = [0.9, 0.1, 0.1]
+out2 = [0.1, 0.1, 0.9]
+out3 = [0.1, 0.9, 0.1]
+
+test1 = [0.1, 0.1, 0.9]
+test2 = [0.1, 0.9, 0.1]
+test3 = [0.9, 0.1, 0.1]
+
+nn = Neural_2H_momentum.new(3, 3, 3, 3, 0.75)
+p nn
+
+nn.addTrainingExample(in1, out1)
+nn.addTrainingExample(in2, out2)
+nn.addTrainingExample(in3, out3)
+
+100.times do |training_iteration|
+   p nn.train() # train and print the error at output neurons
+end
+
+## test to make sure we have learned the input patterns:
+
+p nn.recall(test1)
+p nn.recall(test2)
+p nn.recall(test3)
diff --git a/src/neuralnetworks/Neural_2H_momentum.java b/src/neuralnetworks/Neural_2H_momentum.java
@@ -48,7 +48,7 @@
     public float TRAINING_RATE = 0.5f;
     private float alpha = 0f;  // momentum scaling term that is applied to last delta weight
     
-    Neural_2H_momentum(int num_in, int num_hidden1, int num_hidden2, int num_output,
+    public Neural_2H_momentum(int num_in, int num_hidden1, int num_hidden2, int num_output,
                        float alpha) {
         this.alpha = alpha;
         numInputs = num_in;
@@ -81,6 +81,12 @@ public void addTrainingExample(float[] inputs, float[] outputs) {
         outputTraining.add(outputs);
     }
 
+  /**
+   * Load a trained network from a serialized file
+   *
+   * @param serialized_file_name
+   * @return
+   */
     public static Neural_2H_momentum Factory(String serialized_file_name) {
         Neural_2H_momentum nn = null;
         try {
@@ -102,6 +108,11 @@ public static Neural_2H_momentum Factory(String serialized_file_name) {
         return nn;
     }
 
+  /**
+   * Save a trained network to a serialized file for re-use without re-training
+   *
+   * @param file_name
+   */
     public void save(String file_name) {
         try {
             FileOutputStream ostream = new FileOutputStream(file_name);
