Examples with ArrayTrainable com.simiacryptus.mindseye.eval.ArrayTrainable used on opensource projects

Example 6 with ArrayTrainable

use of com.simiacryptus.mindseye.eval.ArrayTrainable 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);
    });
}

Example 7 with ArrayTrainable

use of com.simiacryptus.mindseye.eval.ArrayTrainable 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;
}

Example 8 with ArrayTrainable

use of com.simiacryptus.mindseye.eval.ArrayTrainable in project MindsEye by SimiaCryptus.

the class TrainingTester method trainMagic.

/**
 * Train lbfgs list.
 *
 * @param log       the log
 * @param trainable the trainable
 * @return the list
 */
@Nonnull
public List<StepRecord> trainMagic(@Nonnull final NotebookOutput log, final Trainable trainable) {
    log.p("Now we train using an experimental optimizer:");
    @Nonnull final List<StepRecord> history = new ArrayList<>();
    @Nonnull final TrainingMonitor monitor = TrainingTester.getMonitor(history);
    try {
        log.code(() -> {
            return new IterativeTrainer(trainable).setLineSearchFactory(label -> new StaticLearningRate(1.0)).setOrientation(new RecursiveSubspace() {

                @Override
                public void train(@Nonnull TrainingMonitor monitor, Layer macroLayer) {
                    @Nonnull Tensor[][] nullData = { { new Tensor() } };
                    @Nonnull BasicTrainable inner = new BasicTrainable(macroLayer);
                    @Nonnull ArrayTrainable trainable1 = new ArrayTrainable(inner, nullData);
                    inner.freeRef();
                    new IterativeTrainer(trainable1).setOrientation(new QQN()).setLineSearchFactory(n -> new QuadraticSearch().setCurrentRate(n.equals(QQN.CURSOR_NAME) ? 1.0 : 1e-4)).setMonitor(new TrainingMonitor() {

                        @Override
                        public void log(String msg) {
                            monitor.log("\t" + msg);
                        }
                    }).setMaxIterations(getIterations()).setIterationsPerSample(getIterations()).runAndFree();
                    trainable1.freeRef();
                    for (@Nonnull Tensor[] tensors : nullData) {
                        for (@Nonnull Tensor tensor : tensors) {
                            tensor.freeRef();
                        }
                    }
                }
            }).setMonitor(monitor).setTimeout(30, TimeUnit.SECONDS).setIterationsPerSample(100).setMaxIterations(250).setTerminateThreshold(0).runAndFree();
        });
    } catch (Throwable e) {
        if (isThrowExceptions())
            throw new RuntimeException(e);
    }
    return history;
}

Example 9 with ArrayTrainable

use of com.simiacryptus.mindseye.eval.ArrayTrainable in project MindsEye by SimiaCryptus.

the class ImageDecompositionLab method train.

/**
 * Train.
 *
 * @param log            the log
 * @param monitor        the monitor
 * @param network        the network
 * @param data           the data
 * @param timeoutMinutes the timeout minutes
 * @param mask           the mask
 */
protected void train(@Nonnull final NotebookOutput log, final TrainingMonitor monitor, final Layer network, @Nonnull final Tensor[][] data, final int timeoutMinutes, final boolean... mask) {
    log.out("Training for %s minutes, mask=%s", timeoutMinutes, Arrays.toString(mask));
    log.code(() -> {
        @Nonnull SampledTrainable trainingSubject = new SampledArrayTrainable(data, network, data.length);
        trainingSubject = (SampledTrainable) ((TrainableDataMask) trainingSubject).setMask(mask);
        @Nonnull final ValidatingTrainer validatingTrainer = new ValidatingTrainer(trainingSubject, new ArrayTrainable(data, network)).setMaxTrainingSize(data.length).setMinTrainingSize(5).setMonitor(monitor).setTimeout(timeoutMinutes, TimeUnit.MINUTES).setMaxIterations(1000);
        validatingTrainer.getRegimen().get(0).setOrientation(new GradientDescent()).setLineSearchFactory(name -> name.equals(QQN.CURSOR_NAME) ? new QuadraticSearch().setCurrentRate(1.0) : new QuadraticSearch().setCurrentRate(1.0));
        validatingTrainer.run();
    });
}

Example 10 with ArrayTrainable

use of com.simiacryptus.mindseye.eval.ArrayTrainable 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;
}