Examples with IterativeTrainer com.simiacryptus.mindseye.opt.IterativeTrainer used on opensource projects

Example 6 with IterativeTrainer

use of com.simiacryptus.mindseye.opt.IterativeTrainer in project MindsEye by SimiaCryptus.

the class BisectionLineSearchTest method train.

@Override
public void train(@Nonnull final NotebookOutput log, @Nonnull final Layer network, @Nonnull final Tensor[][] trainingData, final TrainingMonitor monitor) {
    log.code(() -> {
        @Nonnull final SimpleLossNetwork supervisedNetwork = new SimpleLossNetwork(network, new EntropyLossLayer());
        @Nonnull final Trainable trainable = new SampledArrayTrainable(trainingData, supervisedNetwork, 1000);
        return new IterativeTrainer(trainable).setMonitor(monitor).setOrientation(new GradientDescent()).setLineSearchFactory((@Nonnull final CharSequence name) -> new BisectionSearch()).setTimeout(3, TimeUnit.MINUTES).setMaxIterations(500).runAndFree();
    });
}

Example 7 with IterativeTrainer

use of com.simiacryptus.mindseye.opt.IterativeTrainer in project MindsEye by SimiaCryptus.

the class OWLQNTest method train.

@Override
public void train(@Nonnull final NotebookOutput log, @Nonnull final Layer network, @Nonnull final Tensor[][] trainingData, final TrainingMonitor monitor) {
    log.code(() -> {
        @Nonnull final SimpleLossNetwork supervisedNetwork = new SimpleLossNetwork(network, new EntropyLossLayer());
        @Nonnull final Trainable trainable = new SampledArrayTrainable(trainingData, supervisedNetwork, 10000);
        return new IterativeTrainer(trainable).setIterationsPerSample(100).setMonitor(monitor).setOrientation(new ValidatingOrientationWrapper(new OwlQn())).setTimeout(5, TimeUnit.MINUTES).setMaxIterations(500).runAndFree();
    });
}

Example 8 with IterativeTrainer

use of com.simiacryptus.mindseye.opt.IterativeTrainer in project MindsEye by SimiaCryptus.

the class SingleOrthantTrustRegionTest method train.

@Override
public void train(@Nonnull final NotebookOutput log, @Nonnull final Layer network, @Nonnull final Tensor[][] trainingData, final TrainingMonitor monitor) {
    log.code(() -> {
        @Nonnull final SimpleLossNetwork supervisedNetwork = new SimpleLossNetwork(network, new EntropyLossLayer());
        @Nonnull final Trainable trainable = new SampledArrayTrainable(trainingData, supervisedNetwork, 10000);
        @Nonnull final TrustRegionStrategy trustRegionStrategy = new TrustRegionStrategy() {

            @Override
            public TrustRegion getRegionPolicy(final Layer layer) {
                return new SingleOrthant();
            }
        };
        return new IterativeTrainer(trainable).setIterationsPerSample(100).setMonitor(monitor).setOrientation(trustRegionStrategy).setTimeout(3, TimeUnit.MINUTES).setMaxIterations(500).runAndFree();
    });
}

Example 9 with IterativeTrainer

use of com.simiacryptus.mindseye.opt.IterativeTrainer in project MindsEye by SimiaCryptus.

the class DeepDream method train.

/**
 * Train buffered image.
 *
 * @param server          the server
 * @param log             the log
 * @param canvasImage     the canvas image
 * @param network         the network
 * @param precision       the precision
 * @param trainingMinutes the training minutes
 * @return the buffered image
 */
@Nonnull
public BufferedImage train(final StreamNanoHTTPD server, @Nonnull final NotebookOutput log, final BufferedImage canvasImage, final PipelineNetwork network, final Precision precision, final int trainingMinutes) {
    System.gc();
    Tensor canvas = Tensor.fromRGB(canvasImage);
    TestUtil.monitorImage(canvas, false, false);
    network.setFrozen(true);
    ArtistryUtil.setPrecision(network, precision);
    @Nonnull Trainable trainable = new ArrayTrainable(network, 1).setVerbose(true).setMask(true).setData(Arrays.asList(new Tensor[][] { { canvas } }));
    TestUtil.instrumentPerformance(network);
    if (null != server)
        ArtistryUtil.addLayersHandler(network, server);
    log.code(() -> {
        @Nonnull ArrayList<StepRecord> history = new ArrayList<>();
        new IterativeTrainer(trainable).setMonitor(TestUtil.getMonitor(history)).setIterationsPerSample(100).setOrientation(new TrustRegionStrategy() {

            @Override
            public TrustRegion getRegionPolicy(final Layer layer) {
                return new RangeConstraint();
            }
        }).setLineSearchFactory(name -> new BisectionSearch().setSpanTol(1e-1).setCurrentRate(1e3)).setTimeout(trainingMinutes, TimeUnit.MINUTES).setTerminateThreshold(Double.NEGATIVE_INFINITY).runAndFree();
        return TestUtil.plot(history);
    });
    return canvas.toImage();
}

Example 10 with IterativeTrainer

use of com.simiacryptus.mindseye.opt.IterativeTrainer in project MindsEye by SimiaCryptus.

the class ImageClassifier method deepDream.

/**
 * Deep dream.
 *
 * @param log   the log
 * @param image the image
 */
public void deepDream(@Nonnull final NotebookOutput log, final Tensor image) {
    log.code(() -> {
        @Nonnull ArrayList<StepRecord> history = new ArrayList<>();
        @Nonnull PipelineNetwork clamp = new PipelineNetwork(1);
        clamp.add(new ActivationLayer(ActivationLayer.Mode.RELU));
        clamp.add(new LinearActivationLayer().setBias(255).setScale(-1).freeze());
        clamp.add(new ActivationLayer(ActivationLayer.Mode.RELU));
        clamp.add(new LinearActivationLayer().setBias(255).setScale(-1).freeze());
        @Nonnull PipelineNetwork supervised = new PipelineNetwork(1);
        supervised.add(getNetwork().freeze(), supervised.wrap(clamp, supervised.getInput(0)));
        // CudaTensorList gpuInput = CudnnHandle.apply(gpu -> {
        // Precision precision = Precision.Float;
        // return CudaTensorList.wrap(gpu.getPtr(TensorArray.wrap(image), precision, MemoryType.Managed), 1, image.getDimensions(), precision);
        // });
        // @Nonnull Trainable trainable = new TensorListTrainable(supervised, gpuInput).setVerbosity(1).setMask(true);
        @Nonnull Trainable trainable = new ArrayTrainable(supervised, 1).setVerbose(true).setMask(true, false).setData(Arrays.<Tensor[]>asList(new Tensor[] { image }));
        new IterativeTrainer(trainable).setMonitor(getTrainingMonitor(history, supervised)).setOrientation(new QQN()).setLineSearchFactory(name -> new ArmijoWolfeSearch()).setTimeout(60, TimeUnit.MINUTES).runAndFree();
        return TestUtil.plot(history);
    });
}