diff --git a/README.md b/README.md
index 7dceb7e..4226d50 100644
--- a/README.md
+++ b/README.md
@@ -45,7 +45,7 @@ Dataset dataset = new Cora();
 Matrix adjacency = dataset.graph().setMainDiagonal(1).setToSymmetricNormalization();
 Matrix nodeFeatures = dataset.features();
 Matrix nodeLabels = dataset.labels();
-Slice nodes = new Slice(0, nodeLabels.getRows()).shuffle(100);
+Slice nodes = dataset.samples().getSlice().shuffle(100);
 long numClasses = nodeLabels.getCols();
 
 ModelBuilder modelBuilder = new FastBuilder(adjacency, nodeFeatures)
@@ -64,22 +64,23 @@ ModelTraining trainer = new ModelTraining()
 		.setEpochs(300)
 		.setPatience(100)
 		.setLoss(new CategoricalCrossEntropy())
+		.setVerbose(true)
 		.setValidationLoss(new CategoricalCrossEntropy());
 
 Model model = modelBuilder.getModel()
 		.init(new XavierNormal())
 		.train(trainer,
-				nodes.samplesAsFeatures(), 
-				nodeLabels, 
-				nodes.range(0, 0.6), 
-				nodes.range(0.6, 0.8));
+			nodes.samplesAsFeatures(), 
+			nodeLabels, 
+			nodes.range(0, 0.6), 
+			nodes.range(0.6, 0.8));
 
 Matrix output = model.predict(nodes.samplesAsFeatures()).get(0).cast(Matrix.class);
 double acc = 0;
 for(Long node : nodes.range(0.8, 1)) {
-	Matrix nodeLabels = dataset.labels().accessRow(node).asRow();
+	Matrix trueLabels = dataset.labels().accessRow(node).asRow();
 	Tensor nodeOutput = output.accessRow(node).asRow();
-	acc += nodeOutput.argmax()==nodeLabels.argmax()?1:0;
+	acc += nodeOutput.argmax()==trueLabels.argmax()?1:0;
 }
 System.out.println("Acc\t "+acc/nodes.range(0.8, 1).size());
 ```