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Example 1 with StepRecord

use of com.simiacryptus.mindseye.test.StepRecord in project MindsEye by SimiaCryptus.

the class EncodingUtil method getMonitor.

/**
 * Gets monitor.
 *
 * @param history the history
 * @return the monitor
 */
public static TrainingMonitor getMonitor(@Nonnull final List<StepRecord> history) {
    return new TrainingMonitor() {

        @Override
        public void clear() {
            super.clear();
        }

        @Override
        public void log(final String msg) {
            // Logged MnistProblemData
            log.info(msg);
            // Realtime MnistProblemData
            EncodingUtil.rawOut.println(msg);
        }

        @Override
        public void onStepComplete(@Nonnull final Step currentPoint) {
            history.add(new StepRecord(currentPoint.point.getMean(), currentPoint.time, currentPoint.iteration));
        }
    };
}
Also used : StepRecord(com.simiacryptus.mindseye.test.StepRecord) TrainingMonitor(com.simiacryptus.mindseye.opt.TrainingMonitor) Nonnull(javax.annotation.Nonnull) Step(com.simiacryptus.mindseye.opt.Step)

Example 2 with StepRecord

use of com.simiacryptus.mindseye.test.StepRecord 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();
}
Also used : TrustRegion(com.simiacryptus.mindseye.opt.region.TrustRegion) PipelineNetwork(com.simiacryptus.mindseye.network.PipelineNetwork) Arrays(java.util.Arrays) TrustRegion(com.simiacryptus.mindseye.opt.region.TrustRegion) MeanSqLossLayer(com.simiacryptus.mindseye.layers.cudnn.MeanSqLossLayer) LoggerFactory(org.slf4j.LoggerFactory) Tensor(com.simiacryptus.mindseye.lang.Tensor) TrustRegionStrategy(com.simiacryptus.mindseye.opt.orient.TrustRegionStrategy) HashMap(java.util.HashMap) NullNotebookOutput(com.simiacryptus.util.io.NullNotebookOutput) MultiLayerImageNetwork(com.simiacryptus.mindseye.models.MultiLayerImageNetwork) ArrayList(java.util.ArrayList) Trainable(com.simiacryptus.mindseye.eval.Trainable) Precision(com.simiacryptus.mindseye.lang.cudnn.Precision) Tuple2(com.simiacryptus.util.lang.Tuple2) Map(java.util.Map) Layer(com.simiacryptus.mindseye.lang.Layer) StepRecord(com.simiacryptus.mindseye.test.StepRecord) NotebookOutput(com.simiacryptus.util.io.NotebookOutput) IterativeTrainer(com.simiacryptus.mindseye.opt.IterativeTrainer) Nonnull(javax.annotation.Nonnull) SquareActivationLayer(com.simiacryptus.mindseye.layers.cudnn.SquareActivationLayer) Logger(org.slf4j.Logger) BufferedImage(java.awt.image.BufferedImage) AvgReducerLayer(com.simiacryptus.mindseye.layers.cudnn.AvgReducerLayer) ValueLayer(com.simiacryptus.mindseye.layers.cudnn.ValueLayer) TestUtil(com.simiacryptus.mindseye.test.TestUtil) UUID(java.util.UUID) DAGNode(com.simiacryptus.mindseye.network.DAGNode) StreamNanoHTTPD(com.simiacryptus.util.StreamNanoHTTPD) TimeUnit(java.util.concurrent.TimeUnit) BisectionSearch(com.simiacryptus.mindseye.opt.line.BisectionSearch) List(java.util.List) ArrayTrainable(com.simiacryptus.mindseye.eval.ArrayTrainable) BinarySumLayer(com.simiacryptus.mindseye.layers.cudnn.BinarySumLayer) MultiLayerVGG16(com.simiacryptus.mindseye.models.MultiLayerVGG16) RangeConstraint(com.simiacryptus.mindseye.opt.region.RangeConstraint) DAGNetwork(com.simiacryptus.mindseye.network.DAGNetwork) LayerEnum(com.simiacryptus.mindseye.models.LayerEnum) MultiLayerVGG19(com.simiacryptus.mindseye.models.MultiLayerVGG19) Tensor(com.simiacryptus.mindseye.lang.Tensor) IterativeTrainer(com.simiacryptus.mindseye.opt.IterativeTrainer) Nonnull(javax.annotation.Nonnull) ArrayList(java.util.ArrayList) ArrayTrainable(com.simiacryptus.mindseye.eval.ArrayTrainable) MeanSqLossLayer(com.simiacryptus.mindseye.layers.cudnn.MeanSqLossLayer) Layer(com.simiacryptus.mindseye.lang.Layer) SquareActivationLayer(com.simiacryptus.mindseye.layers.cudnn.SquareActivationLayer) AvgReducerLayer(com.simiacryptus.mindseye.layers.cudnn.AvgReducerLayer) ValueLayer(com.simiacryptus.mindseye.layers.cudnn.ValueLayer) BinarySumLayer(com.simiacryptus.mindseye.layers.cudnn.BinarySumLayer) StepRecord(com.simiacryptus.mindseye.test.StepRecord) RangeConstraint(com.simiacryptus.mindseye.opt.region.RangeConstraint) BisectionSearch(com.simiacryptus.mindseye.opt.line.BisectionSearch) Trainable(com.simiacryptus.mindseye.eval.Trainable) ArrayTrainable(com.simiacryptus.mindseye.eval.ArrayTrainable) TrustRegionStrategy(com.simiacryptus.mindseye.opt.orient.TrustRegionStrategy) Nonnull(javax.annotation.Nonnull)

Example 3 with StepRecord

use of com.simiacryptus.mindseye.test.StepRecord 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);
    });
}
Also used : ActivationLayer(com.simiacryptus.mindseye.layers.cudnn.ActivationLayer) LinearActivationLayer(com.simiacryptus.mindseye.layers.java.LinearActivationLayer) Tensor(com.simiacryptus.mindseye.lang.Tensor) IterativeTrainer(com.simiacryptus.mindseye.opt.IterativeTrainer) Nonnull(javax.annotation.Nonnull) ArrayList(java.util.ArrayList) PipelineNetwork(com.simiacryptus.mindseye.network.PipelineNetwork) ArrayTrainable(com.simiacryptus.mindseye.eval.ArrayTrainable) LinearActivationLayer(com.simiacryptus.mindseye.layers.java.LinearActivationLayer) QQN(com.simiacryptus.mindseye.opt.orient.QQN) StepRecord(com.simiacryptus.mindseye.test.StepRecord) ArmijoWolfeSearch(com.simiacryptus.mindseye.opt.line.ArmijoWolfeSearch) Trainable(com.simiacryptus.mindseye.eval.Trainable) ArrayTrainable(com.simiacryptus.mindseye.eval.ArrayTrainable)

Example 4 with StepRecord

use of com.simiacryptus.mindseye.test.StepRecord in project MindsEye by SimiaCryptus.

the class StyleTransfer method styleTransfer.

/**
 * Style transfer buffered image.
 *
 * @param server          the server
 * @param log             the log
 * @param canvasImage     the canvas image
 * @param styleParameters the style parameters
 * @param trainingMinutes the training minutes
 * @param measureStyle    the measure style
 * @return the buffered image
 */
public BufferedImage styleTransfer(final StreamNanoHTTPD server, @Nonnull final NotebookOutput log, final BufferedImage canvasImage, final StyleSetup<T> styleParameters, final int trainingMinutes, final NeuralSetup measureStyle) {
    BufferedImage result = ArtistryUtil.logExceptionWithDefault(log, () -> {
        log.p("Input Content:");
        log.p(log.image(styleParameters.contentImage, "Content Image"));
        log.p("Style Content:");
        styleParameters.styleImages.forEach((file, styleImage) -> {
            log.p(log.image(styleImage, file));
        });
        log.p("Input Canvas:");
        log.p(log.image(canvasImage, "Input Canvas"));
        System.gc();
        Tensor canvas = Tensor.fromRGB(canvasImage);
        TestUtil.monitorImage(canvas, false, false);
        log.p("Input Parameters:");
        log.code(() -> {
            return ArtistryUtil.toJson(styleParameters);
        });
        Trainable trainable = log.code(() -> {
            PipelineNetwork network = fitnessNetwork(measureStyle);
            network.setFrozen(true);
            ArtistryUtil.setPrecision(network, styleParameters.precision);
            TestUtil.instrumentPerformance(network);
            if (null != server)
                ArtistryUtil.addLayersHandler(network, server);
            return new ArrayTrainable(network, 1).setVerbose(true).setMask(true).setData(Arrays.asList(new Tensor[][] { { canvas } }));
        });
        log.code(() -> {
            @Nonnull ArrayList<StepRecord> history = new ArrayList<>();
            new IterativeTrainer(trainable).setMonitor(TestUtil.getMonitor(history)).setOrientation(new TrustRegionStrategy() {

                @Override
                public TrustRegion getRegionPolicy(final Layer layer) {
                    return new RangeConstraint().setMin(1e-2).setMax(256);
                }
            }).setIterationsPerSample(100).setLineSearchFactory(name -> new BisectionSearch().setSpanTol(1e-1).setCurrentRate(1e6)).setTimeout(trainingMinutes, TimeUnit.MINUTES).setTerminateThreshold(Double.NEGATIVE_INFINITY).runAndFree();
            return TestUtil.plot(history);
        });
        return canvas.toImage();
    }, canvasImage);
    log.p("Output Canvas:");
    log.p(log.image(result, "Output Canvas"));
    return result;
}
Also used : PipelineNetwork(com.simiacryptus.mindseye.network.PipelineNetwork) IntStream(java.util.stream.IntStream) Arrays(java.util.Arrays) TrustRegion(com.simiacryptus.mindseye.opt.region.TrustRegion) MeanSqLossLayer(com.simiacryptus.mindseye.layers.cudnn.MeanSqLossLayer) LoggerFactory(org.slf4j.LoggerFactory) Tensor(com.simiacryptus.mindseye.lang.Tensor) TrustRegionStrategy(com.simiacryptus.mindseye.opt.orient.TrustRegionStrategy) HashMap(java.util.HashMap) NullNotebookOutput(com.simiacryptus.util.io.NullNotebookOutput) MultiLayerImageNetwork(com.simiacryptus.mindseye.models.MultiLayerImageNetwork) ArrayList(java.util.ArrayList) JsonUtil(com.simiacryptus.util.io.JsonUtil) Trainable(com.simiacryptus.mindseye.eval.Trainable) Precision(com.simiacryptus.mindseye.lang.cudnn.Precision) Tuple2(com.simiacryptus.util.lang.Tuple2) Map(java.util.Map) Layer(com.simiacryptus.mindseye.lang.Layer) GateBiasLayer(com.simiacryptus.mindseye.layers.cudnn.GateBiasLayer) StepRecord(com.simiacryptus.mindseye.test.StepRecord) NotebookOutput(com.simiacryptus.util.io.NotebookOutput) IterativeTrainer(com.simiacryptus.mindseye.opt.IterativeTrainer) Nonnull(javax.annotation.Nonnull) Logger(org.slf4j.Logger) BufferedImage(java.awt.image.BufferedImage) ValueLayer(com.simiacryptus.mindseye.layers.cudnn.ValueLayer) TestUtil(com.simiacryptus.mindseye.test.TestUtil) UUID(java.util.UUID) DAGNode(com.simiacryptus.mindseye.network.DAGNode) Collectors(java.util.stream.Collectors) BandAvgReducerLayer(com.simiacryptus.mindseye.layers.cudnn.BandAvgReducerLayer) StreamNanoHTTPD(com.simiacryptus.util.StreamNanoHTTPD) TimeUnit(java.util.concurrent.TimeUnit) BisectionSearch(com.simiacryptus.mindseye.opt.line.BisectionSearch) List(java.util.List) GramianLayer(com.simiacryptus.mindseye.layers.cudnn.GramianLayer) Stream(java.util.stream.Stream) ArrayTrainable(com.simiacryptus.mindseye.eval.ArrayTrainable) BinarySumLayer(com.simiacryptus.mindseye.layers.cudnn.BinarySumLayer) InnerNode(com.simiacryptus.mindseye.network.InnerNode) ScalarStatistics(com.simiacryptus.util.data.ScalarStatistics) MultiLayerVGG16(com.simiacryptus.mindseye.models.MultiLayerVGG16) RangeConstraint(com.simiacryptus.mindseye.opt.region.RangeConstraint) LayerEnum(com.simiacryptus.mindseye.models.LayerEnum) MultiLayerVGG19(com.simiacryptus.mindseye.models.MultiLayerVGG19) Tensor(com.simiacryptus.mindseye.lang.Tensor) IterativeTrainer(com.simiacryptus.mindseye.opt.IterativeTrainer) Nonnull(javax.annotation.Nonnull) ArrayList(java.util.ArrayList) PipelineNetwork(com.simiacryptus.mindseye.network.PipelineNetwork) ArrayTrainable(com.simiacryptus.mindseye.eval.ArrayTrainable) MeanSqLossLayer(com.simiacryptus.mindseye.layers.cudnn.MeanSqLossLayer) Layer(com.simiacryptus.mindseye.lang.Layer) GateBiasLayer(com.simiacryptus.mindseye.layers.cudnn.GateBiasLayer) ValueLayer(com.simiacryptus.mindseye.layers.cudnn.ValueLayer) BandAvgReducerLayer(com.simiacryptus.mindseye.layers.cudnn.BandAvgReducerLayer) GramianLayer(com.simiacryptus.mindseye.layers.cudnn.GramianLayer) BinarySumLayer(com.simiacryptus.mindseye.layers.cudnn.BinarySumLayer) BufferedImage(java.awt.image.BufferedImage) StepRecord(com.simiacryptus.mindseye.test.StepRecord) RangeConstraint(com.simiacryptus.mindseye.opt.region.RangeConstraint) BisectionSearch(com.simiacryptus.mindseye.opt.line.BisectionSearch) Trainable(com.simiacryptus.mindseye.eval.Trainable) ArrayTrainable(com.simiacryptus.mindseye.eval.ArrayTrainable) TrustRegionStrategy(com.simiacryptus.mindseye.opt.orient.TrustRegionStrategy)

Example 5 with StepRecord

use of com.simiacryptus.mindseye.test.StepRecord in project MindsEye by SimiaCryptus.

the class TrainingTester method trainGD.

/**
 * Train gd list.
 *
 * @param log       the log
 * @param trainable the trainable
 * @return the list
 */
@Nonnull
public List<StepRecord> trainGD(@Nonnull final NotebookOutput log, final Trainable trainable) {
    log.p("First, we train using basic gradient descent method apply weak line search conditions.");
    @Nonnull final List<StepRecord> history = new ArrayList<>();
    @Nonnull final TrainingMonitor monitor = TrainingTester.getMonitor(history);
    try {
        log.code(() -> {
            return new IterativeTrainer(trainable).setLineSearchFactory(label -> new ArmijoWolfeSearch()).setOrientation(new GradientDescent()).setMonitor(monitor).setTimeout(30, TimeUnit.SECONDS).setMaxIterations(250).setTerminateThreshold(0).runAndFree();
        });
    } catch (Throwable e) {
        if (isThrowExceptions())
            throw new RuntimeException(e);
    }
    return history;
}
Also used : StepRecord(com.simiacryptus.mindseye.test.StepRecord) TrainingMonitor(com.simiacryptus.mindseye.opt.TrainingMonitor) IterativeTrainer(com.simiacryptus.mindseye.opt.IterativeTrainer) ArmijoWolfeSearch(com.simiacryptus.mindseye.opt.line.ArmijoWolfeSearch) Nonnull(javax.annotation.Nonnull) ArrayList(java.util.ArrayList) GradientDescent(com.simiacryptus.mindseye.opt.orient.GradientDescent) Nonnull(javax.annotation.Nonnull)

Aggregations

StepRecord (com.simiacryptus.mindseye.test.StepRecord)11 Nonnull (javax.annotation.Nonnull)11 IterativeTrainer (com.simiacryptus.mindseye.opt.IterativeTrainer)9 ArrayList (java.util.ArrayList)9 ArrayTrainable (com.simiacryptus.mindseye.eval.ArrayTrainable)7 Trainable (com.simiacryptus.mindseye.eval.Trainable)7 Tensor (com.simiacryptus.mindseye.lang.Tensor)7 PipelineNetwork (com.simiacryptus.mindseye.network.PipelineNetwork)7 TrainingMonitor (com.simiacryptus.mindseye.opt.TrainingMonitor)6 ArmijoWolfeSearch (com.simiacryptus.mindseye.opt.line.ArmijoWolfeSearch)6 Layer (com.simiacryptus.mindseye.lang.Layer)5 DAGNode (com.simiacryptus.mindseye.network.DAGNode)5 GradientDescent (com.simiacryptus.mindseye.opt.orient.GradientDescent)5 QQN (com.simiacryptus.mindseye.opt.orient.QQN)5 TestUtil (com.simiacryptus.mindseye.test.TestUtil)5 NotebookOutput (com.simiacryptus.util.io.NotebookOutput)5 Arrays (java.util.Arrays)5 HashMap (java.util.HashMap)5 List (java.util.List)5 Map (java.util.Map)5