Examples with DropoutNoiseLayer com.simiacryptus.mindseye.layers.java.DropoutNoiseLayer used on opensource projects

Example 1 with DropoutNoiseLayer

use of com.simiacryptus.mindseye.layers.java.DropoutNoiseLayer in project MindsEye by SimiaCryptus.

the class AutoencodingProblem method run.

@Nonnull
@Override
public AutoencodingProblem run(@Nonnull final NotebookOutput log) {
    @Nonnull final DAGNetwork fwdNetwork = fwdFactory.imageToVector(log, features);
    @Nonnull final DAGNetwork revNetwork = revFactory.vectorToImage(log, features);
    @Nonnull final PipelineNetwork echoNetwork = new PipelineNetwork(1);
    echoNetwork.add(fwdNetwork);
    echoNetwork.add(revNetwork);
    @Nonnull final PipelineNetwork supervisedNetwork = new PipelineNetwork(1);
    supervisedNetwork.add(fwdNetwork);
    @Nonnull final DropoutNoiseLayer dropoutNoiseLayer = new DropoutNoiseLayer().setValue(dropout);
    supervisedNetwork.add(dropoutNoiseLayer);
    supervisedNetwork.add(revNetwork);
    supervisedNetwork.add(new MeanSqLossLayer(), supervisedNetwork.getHead(), supervisedNetwork.getInput(0));
    log.h3("Network Diagrams");
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(fwdNetwork)).height(400).width(600).render(Format.PNG).toImage();
    });
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(revNetwork)).height(400).width(600).render(Format.PNG).toImage();
    });
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(supervisedNetwork)).height(400).width(600).render(Format.PNG).toImage();
    });
    @Nonnull final TrainingMonitor monitor = new TrainingMonitor() {

        @Nonnull
        TrainingMonitor inner = TestUtil.getMonitor(history);

        @Override
        public void log(final String msg) {
            inner.log(msg);
        }

        @Override
        public void onStepComplete(final Step currentPoint) {
            dropoutNoiseLayer.shuffle(StochasticComponent.random.get().nextLong());
            inner.onStepComplete(currentPoint);
        }
    };
    final Tensor[][] trainingData = getTrainingData(log);
    // MonitoredObject monitoringRoot = new MonitoredObject();
    // TestUtil.addMonitoring(supervisedNetwork, monitoringRoot);
    log.h3("Training");
    TestUtil.instrumentPerformance(supervisedNetwork);
    @Nonnull final ValidatingTrainer trainer = optimizer.train(log, new SampledArrayTrainable(trainingData, supervisedNetwork, trainingData.length / 2, batchSize), new ArrayTrainable(trainingData, supervisedNetwork, batchSize), monitor);
    log.code(() -> {
        trainer.setTimeout(timeoutMinutes, TimeUnit.MINUTES).setMaxIterations(10000).run();
    });
    if (!history.isEmpty()) {
        log.code(() -> {
            return TestUtil.plot(history);
        });
        log.code(() -> {
            return TestUtil.plotTime(history);
        });
    }
    TestUtil.extractPerformance(log, supervisedNetwork);
    {
        @Nonnull final String modelName = "encoder_model" + AutoencodingProblem.modelNo++ + ".json";
        log.p("Saved model as " + log.file(fwdNetwork.getJson().toString(), modelName, modelName));
    }
    @Nonnull final String modelName = "decoder_model" + AutoencodingProblem.modelNo++ + ".json";
    log.p("Saved model as " + log.file(revNetwork.getJson().toString(), modelName, modelName));
    // log.h3("Metrics");
    // log.code(() -> {
    // return TestUtil.toFormattedJson(monitoringRoot.getMetrics());
    // });
    log.h3("Validation");
    log.p("Here are some re-encoded examples:");
    log.code(() -> {
        @Nonnull final TableOutput table = new TableOutput();
        data.validationData().map(labeledObject -> {
            return toRow(log, labeledObject, echoNetwork.eval(labeledObject.data).getData().get(0).getData());
        }).filter(x -> null != x).limit(10).forEach(table::putRow);
        return table;
    });
    log.p("Some rendered unit vectors:");
    for (int featureNumber = 0; featureNumber < features; featureNumber++) {
        @Nonnull final Tensor input = new Tensor(features).set(featureNumber, 1);
        @Nullable final Tensor tensor = revNetwork.eval(input).getData().get(0);
        log.out(log.image(tensor.toImage(), ""));
    }
    return this;
}