local overwrite in neurlang

JGNN/src/examples/nodeClassification/GCN.java

@@ -30,7 +30,7 @@ public static void main(String[] args) throws Exception {
 		dataset.graph().setMainDiagonal(1).setToSymmetricNormalization();
 
 		long numClasses = dataset.labels().getCols();
-		/*ModelBuilder modelBuilder = new FastBuilder(dataset.graph(), dataset.features())
+		ModelBuilder modelBuilder = new FastBuilder(dataset.graph(), dataset.features())
 				.config("reg", 0.005)
 				.config("classes", numClasses)
 				.config("hidden", numClasses)
@@ -39,17 +39,6 @@ public static void main(String[] args) throws Exception {
 				.layer("h{l+1}=gcnlayer(A, h{l})")
 				.classify()
 				.autosize(new EmptyTensor(dataset.samples().getSlice().size()));
-		*/
-		ModelBuilder modelBuilder = new TextBuilder()
-				.parse(String.join("\n", Files.readAllLines(Paths.get("../architectures.nn"))))
-				.constant("A", dataset.graph())
-				.constant("h0", dataset.features())
-				.var("nodes")
-				.config("classes", numClasses)
-				.config("hidden", numClasses)
-				.operation("h=gcn(A,h0); out=softmax(h[nodes], row)")
-				.out("out")
-				.autosize(new EmptyTensor(dataset.samples().getSlice().size()));
 
 		ModelTraining trainer = new ModelTraining()
 				.setOptimizer(new Adam(0.01))

JGNN/src/examples/nodeClassification/Scripting.java

@@ -0,0 +1,69 @@
+package nodeClassification;
+
+import java.nio.file.Files;
+import java.nio.file.Paths;
+
+import mklab.JGNN.adhoc.Dataset;
+import mklab.JGNN.adhoc.ModelBuilder;
+import mklab.JGNN.adhoc.datasets.Cora;
+import mklab.JGNN.adhoc.parsers.TextBuilder;
+import mklab.JGNN.core.Matrix;
+import mklab.JGNN.nn.Model;
+import mklab.JGNN.nn.ModelTraining;
+import mklab.JGNN.core.Slice;
+import mklab.JGNN.core.Tensor;
+import mklab.JGNN.core.empy.EmptyTensor;
+import mklab.JGNN.nn.initializers.XavierNormal;
+import mklab.JGNN.nn.loss.CategoricalCrossEntropy;
+import mklab.JGNN.nn.optimizers.Adam;
+
+/**
+ * Demonstrates classification with an architecture defined through the scripting engine.
+ * 
+ * @author Emmanouil Krasanakis
+ */
+public class Scripting {
+	public static void main(String[] args) throws Exception {
+		Dataset dataset = new Cora();
+		dataset.graph().setMainDiagonal(1).setToSymmetricNormalization();
+		long numClasses = dataset.labels().getCols();
+
+		ModelBuilder modelBuilder = new TextBuilder()
+				.parse(String.join("\n", Files.readAllLines(Paths.get("../architectures.nn"))))
+				.constant("A", dataset.graph())
+				.constant("h", dataset.features())
+				.var("nodes")
+				.config("classes", numClasses)
+				.config("hidden", numClasses)
+				.out("classify(nodes, gcn(A,h))");
+		System.out.println(modelBuilder.getExecutionGraphDot());
+		modelBuilder
+				.autosize(new EmptyTensor(dataset.samples().getSlice().size()));
+
+		ModelTraining trainer = new ModelTraining()
+				.setOptimizer(new Adam(modelBuilder.getConfigOrDefault("lr", 0.01)))
+				.setEpochs(modelBuilder.getConfigOrDefault("epochs", 1000))
+				.setPatience(modelBuilder.getConfigOrDefault("patience", 100))
+				.setVerbose(true)
+				.setLoss(new CategoricalCrossEntropy())
+				.setValidationLoss(new CategoricalCrossEntropy());
+
+		long tic = System.currentTimeMillis();
+		Slice nodes = dataset.samples().getSlice().shuffle(100);
+		Model model = modelBuilder.getModel()
+				.init(new XavierNormal())
+				.train(trainer,
+						Tensor.fromRange(nodes.size()).asColumn(), 
+						dataset.labels(), nodes.range(0, 0.6), nodes.range(0.6, 0.8));
+
+		System.out.println("Training time "+(System.currentTimeMillis()-tic)/1000.);
+		Matrix output = model.predict(Tensor.fromRange(0, nodes.size()).asColumn()).get(0).cast(Matrix.class);
+		double acc = 0;
+		for(Long node : nodes.range(0.8, 1)) {
+			Matrix nodeLabels = dataset.labels().accessRow(node).asRow();
+			Tensor nodeOutput = output.accessRow(node).asRow();
+			acc += nodeOutput.argmax()==nodeLabels.argmax()?1:0;
+		}
+		System.out.println("Acc\t "+acc/nodes.range(0.8, 1).size());
+	}
+}