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finished training abstraction schemas
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JGNN/src/examples/graphClassification/SortPoolingManual.java
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| package graphClassification; | ||
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| import java.util.Arrays; | ||
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| import mklab.JGNN.adhoc.ModelBuilder; | ||
| import mklab.JGNN.adhoc.parsers.LayeredBuilder; | ||
| import mklab.JGNN.core.Matrix; | ||
| import mklab.JGNN.core.Tensor; | ||
| import mklab.JGNN.core.ThreadPool; | ||
| import mklab.JGNN.nn.Loss; | ||
| import mklab.JGNN.nn.Model; | ||
| import mklab.JGNN.nn.initializers.XavierNormal; | ||
| import mklab.JGNN.nn.loss.CategoricalCrossEntropy; | ||
| import mklab.JGNN.nn.optimizers.Adam; | ||
| import mklab.JGNN.nn.optimizers.BatchOptimizer; | ||
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| /** | ||
| * | ||
| * @author github.com/gavalian | ||
| * @author Emmanouil Krasanakis | ||
| */ | ||
| public class SortPoolingManual { | ||
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| public static void main(String[] args){ | ||
| long reduced = 5; // input graphs need to have at least that many nodes, lower values decrease accuracy | ||
| long hidden = 8; // since this library does not use GPU parallelization, many latent dims reduce speed | ||
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| ModelBuilder builder = new LayeredBuilder() | ||
| .var("A") | ||
| .config("features", 1) | ||
| .config("classes", 2) | ||
| .config("reduced", reduced) | ||
| .config("hidden", hidden) | ||
| .config("reg", 0.005) | ||
| .layer("h{l+1}=relu(A@(h{l}@matrix(features, hidden, reg))+vector(hidden))") | ||
| .layer("h{l+1}=relu(A@(h{l}@matrix(hidden, hidden, reg))+vector(hidden))") | ||
| .concat(2) // concatenates the outputs of the last 2 layers | ||
| .config("hiddenReduced", hidden*2*reduced) // 2* due to concatenation | ||
| .operation("z{l}=sort(h{l}, reduced)") // currently, the parser fails to understand full expressions within next step's gather, so we need to create this intermediate variable | ||
| .layer("h{l+1}=reshape(h{l}[z{l}], 1, hiddenReduced)") // | ||
| .layer("h{l+1}=h{l}@matrix(hiddenReduced, classes)") | ||
| .layer("h{l+1}=softmax(h{l}, dim: 'row')") | ||
| //.layer("h{l+1}=softmax(sum(h{l}@matrix(hiddenReduced, classes), row))")//this is mean pooling to replace the above sort pooling | ||
| .out("h{l}"); | ||
| TrajectoryData dtrain = new TrajectoryData(8000); | ||
| TrajectoryData dtest = new TrajectoryData(2000); | ||
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| Model model = builder.getModel().init(new XavierNormal()); | ||
| BatchOptimizer optimizer = new BatchOptimizer(new Adam(0.01)); | ||
| Loss loss = new CategoricalCrossEntropy(); | ||
| for(int epoch=0; epoch<600; epoch++) { | ||
| // gradient update over all graphs | ||
| for(int graphId=0; graphId<dtrain.graphs.size(); graphId++) { | ||
| int graphIdentifier = graphId; | ||
| // each gradient calculation into a new thread pool task | ||
| ThreadPool.getInstance().submit(new Runnable() { | ||
| @Override | ||
| public void run() { | ||
| //System.out.println(dtrain.graphs.get(graphIdentifier).sum()); | ||
| Matrix adjacency = dtrain.graphs.get(graphIdentifier); | ||
| Matrix features= dtrain.features.get(graphIdentifier); | ||
| Tensor graphLabel = dtrain.labels.get(graphIdentifier).asRow(); | ||
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| model.train(loss, optimizer, | ||
| Arrays.asList(features, adjacency), | ||
| Arrays.asList(graphLabel)); | ||
| } | ||
| }); | ||
| ThreadPool.getInstance().waitForConclusion(); // wait for all gradients to compute | ||
| } | ||
| optimizer.updateAll(); // apply gradients on model parameters | ||
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| double acc = 0.0; | ||
| for(int graphId=0; graphId<dtest.graphs.size(); graphId++) { | ||
| Matrix adjacency = dtest.graphs.get(graphId); | ||
| Matrix features= dtest.features.get(graphId); | ||
| Tensor graphLabel = dtest.labels.get(graphId); | ||
| if(model.predict(Arrays.asList(features, adjacency)).get(0).argmax()==graphLabel.argmax()) | ||
| acc += 1; | ||
| } | ||
| System.out.println("iter = " + epoch + " " + acc/dtest.graphs.size()); | ||
| } | ||
| } | ||
| } |
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