docs 85% complete

JGNN/src/examples/nodeClassification/Scripting.java

@@ -14,6 +14,7 @@
 import mklab.JGNN.core.empy.EmptyTensor;
 import mklab.JGNN.nn.initializers.XavierNormal;
 import mklab.JGNN.nn.loss.CategoricalCrossEntropy;
+import mklab.JGNN.nn.loss.report.VerboseLoss;
 import mklab.JGNN.nn.optimizers.Adam;
 
 /**
@@ -31,18 +32,12 @@ fn classify(nodes, h, epochs: !3000, patience: !100, lr: !0.01) {
 				return softmax(h[nodes], dim: "row");
 			}
 			fn gcnlayer(A, h, hidden: 16, reg: 0.005) {
-				h = A@h@matrix(?, hidden, reg) + vector(hidden);
-				return h;
+				return A@h@matrix(?, hidden, reg) + vector(hidden);
 			}
 			fn gcn(A, h, classes: extern) {
 				h = gcnlayer(A, h);
 				h = dropout(relu(h), 0.5);
-				h = gcnlayer(A, h, hidden: classes);
-				return h;
-			}
-			fn ngcn(A, h, nodes) {
-				h = classify(nodes, gcn(A,h));
-				return h;
+				return gcnlayer(A, h, hidden: classes);
 			}
 		""";
 
@@ -55,14 +50,14 @@ fn ngcn(A, h, nodes) {
 				.var("nodes")
 				.config("classes", numClasses)
 				.config("hidden", numClasses+2)
-				.out("ngcn(A,h, nodes)")
+				.out("classify(nodes, gcn(A,h))")
 				.autosize(new EmptyTensor(numSamples));
+		System.out.println("Preferred learning rate: "+modelBuilder.getConfig("lr"));
 
 		ModelTraining trainer = new ModelTraining()
 				.configFrom(modelBuilder)
-				.setVerbose(true)
 				.setLoss(new CategoricalCrossEntropy())
-				.setValidationLoss(new CategoricalCrossEntropy());
+				.setValidationLoss(new VerboseLoss(new CategoricalCrossEntropy()));
 
 		long tic = System.currentTimeMillis();
 		Slice nodes = dataset.samples().getSlice().shuffle(100);

JGNN/src/examples/tutorial/Quickstart.java

@@ -42,6 +42,8 @@ public static void main(String[] args) throws Exception {
 			.classify()
 			.autosize(new EmptyTensor(numSamples));
 
+		System.out.println(modelBuilder.getConfig("lr"));
+
 		ModelTraining trainer = new ModelTraining()
 				.setOptimizer(new Adam(0.01))
 				.setEpochs(3000)

JGNN/src/main/java/mklab/JGNN/adhoc/ModelBuilder.java

@@ -94,6 +94,13 @@ public ModelBuilder(Model model) {
 	public Model getModel() {
 		return model;
 	}
+
+	/**
+	 * Serializes the model builder instance into a Path, such as
+	 * <code>Paths.get("example.jgnn")</code>.
+	 * @param path A serialized path.
+	 * @return This builder's instance.
+	 */
 	public ModelBuilder save(Path path) {
 		try(BufferedWriter writer = Files.newBufferedWriter(path)){
 			writer.write(this.getClass().getCanonicalName()+"\n");
@@ -140,6 +147,13 @@ public ModelBuilder save(Path path) {
 		return this;
 	}
 
+	/**
+	 * Loads a ModelBuilder instance from the provided path, such as <code>Paths.get("example.jgnn")</code>. 
+	 * The instance may have been serialized with any class that extends the model builder.
+	 * 
+	 * @param path The provided path.
+	 * @return The loaded ModelBuilder instance.
+	 */
 	public static ModelBuilder load(Path path) {
 		ModelBuilder builder;
 		try(BufferedReader reader = Files.newBufferedReader(path)){
@@ -350,14 +364,16 @@ public ModelBuilder param(String name, double regularization, Tensor value) {
 	/**
 	 * Declares a configuration hyperparameter, which can be used to declare
 	 * matrix and vector parameters during {@link #operation(String)} expressions.
-	 * For in-expression use of hyperparameters, delcare them with {@link #constant(String, double)}.
+	 * For in-expression use of hyperparameters, declare them with {@link #constant(String, double)}.
+	 * In Neuralang terms, this is implements the broadcasting operation.
 	 * @param name The name of the configuration hyperparameter.
 	 * @param value The value to be assigned to the hyperparameter.
-	 * 	Typically, provide a long number.
+	 *  This may also be a long number.
 	 * @return The builder's instance.
 	 * @see #operation(String)
 	 * @see #param(String, Tensor)
 	 * @see #param(String, double, Tensor)
+	 * @see #config(String, String)
 	 */
 	public ModelBuilder config(String name, double value) {
 		if(name.equals("?"))
@@ -366,19 +382,41 @@ public ModelBuilder config(String name, double value) {
 		return this;
 	}
 
-
+	/**
+	 * Applies {@link #config(String, double)} where the set value
+	 * is obtained from another configuration hyperaparameter.
+	 * @param name The name of the configuration hyperparameter to set.
+	 * @param value The name of the configuration hyperparameter whose value should be copied.
+	 * @return The builder's instance.
+	 * @see #config(String, double)
+	 */
 	public ModelBuilder config(String name, String value) {
-		Double val = configurations.get(value);
+		return config(name, getConfig(value));
+	}
+
+	/**
+	 * Retrieves a configuration hyperparameter's value.
+	 * @param name  The configuration's name.
+	 * @return The retrieved value;
+	 * @throws RuntimeException If a no configuration with the given name was found.
+	 * @see #getConfigOrDefault(String, double)
+	 */
+	public double getConfig(String name) {
+		Double val = configurations.get(name);
 		if(val==null)
-			throw new RuntimeException("No configuration "+value+" found");
+			throw new RuntimeException("No configuration "+name+" found");
 		this.configurations.put(name, val);
-		return this;
-	}
-
-	public int getConfigOrDefault(String name, int defaultValue) {
-		return (int)(double)configurations.getOrDefault(name, (double) defaultValue);
+		return val;
 	}
 
+	/**
+	 * Retrieves a configuration hyperparameter's value. If no such configuration
+	 * exists, a default value is returned instead.
+	 * @param name The configuration's name.
+	 * @param defaultValue The default to be retrieved if no such configuration was found.
+	 * @return The retrieved value;
+	 * @see #getConfig(String)
+	 */
 	public double getConfigOrDefault(String name, double defaultValue) {
 		return configurations.getOrDefault(name, defaultValue);
 	}

JGNN/src/main/java/mklab/JGNN/adhoc/parsers/Neuralang.java

@@ -3,18 +3,33 @@
 import java.io.IOException;
 import java.nio.file.Files;
 import java.nio.file.Path;
-import java.nio.file.Paths;
-
 import mklab.JGNN.adhoc.ModelBuilder;
 import mklab.JGNN.core.Tensor;
 
+/**
+ * Extends the base {@link ModelBuilder} with the full capabilities of the Neuralang 
+ * scripting language.
+ * 
+ * @author Emmanouil Krasanakis
+ * @see #parse(String)
+ * @see #parse(Path)
+ */
 public class Neuralang extends ModelBuilder {
 	public Neuralang() {
 	}
 	public Neuralang config(String name, double value) {
 		super.config(name, value);
 		return this;
 	}
+	/**
+	 * Parses a Neuralang source code file.
+	 * Reads a file like <code>Paths.get("models.nn")</code> 
+	 * from disk with {@link Files#readAllLines(Path)}, and parses
+	 * the loaded String.
+	 * @param path The source code file.
+	 * @return The Neuralang builder's instance.
+	 * @see #parse(String)
+	 */
 	public Neuralang parse(Path path) {
 		try {
 			parse(String.join("\n", Files.readAllLines(path)));
@@ -24,6 +39,12 @@ public Neuralang parse(Path path) {
 		return this;
 	}
 
+	/**
+	 * Parses Neuralang source code by handling function declarations in addition to
+	 * other expressions.
+	 * @param text The source code to parse.
+	 * @return The Neuralang builder's instance.
+	 */
 	public Neuralang parse(String text) {
 		int depth = 0;
 		String progress = "";

JGNN/src/main/java/mklab/JGNN/nn/ModelTraining.java

@@ -224,9 +224,9 @@ public void run() {
 	}
 	public ModelTraining configFrom(ModelBuilder modelBuilder) {
 		setOptimizer(new Adam(modelBuilder.getConfigOrDefault("lr", 0.01)));
-		setEpochs(modelBuilder.getConfigOrDefault("epochs", epochs));
-		numBatches = modelBuilder.getConfigOrDefault("batches", numBatches);
-		setPatience(modelBuilder.getConfigOrDefault("patience", patience));
+		setEpochs((int)modelBuilder.getConfigOrDefault("epochs", epochs));
+		numBatches = (int)modelBuilder.getConfigOrDefault("batches", numBatches);
+		setPatience((int)modelBuilder.getConfigOrDefault("patience", patience));
 		return this;
 	}
 }