Examples with NotebookOutput com.simiacryptus.util.io.NotebookOutput used on opensource projects

Example 21 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class TrainingTester method testInputLearning.

/**
 * Test input learning.
 *
 * @param log            the log
 * @param component      the component
 * @param random         the randomize
 * @param inputPrototype the input prototype
 * @return the apply result
 */
public TestResult testInputLearning(@Nonnull final NotebookOutput log, @Nonnull final Layer component, final Random random, @Nonnull final Tensor[] inputPrototype) {
    @Nonnull final Layer network = shuffle(random, component.copy()).freeze();
    final Tensor[][] input_target = shuffleCopy(random, inputPrototype);
    log.p("In this apply, we use a network to learn this target input, given it's pre-evaluated output:");
    log.code(() -> {
        return Arrays.stream(input_target).flatMap(x -> Arrays.stream(x)).map(x -> x.prettyPrint()).reduce((a, b) -> a + "\n" + b).orElse("");
    });
    Result[] array = ConstantResult.batchResultArray(input_target);
    @Nullable Result eval = network.eval(array);
    TensorList result = eval.getData();
    final Tensor[] output_target = result.stream().toArray(i -> new Tensor[i]);
    result.freeRef();
    eval.freeRef();
    if (output_target.length != getBatches()) {
        logger.info(String.format("Meta layers not supported. %d != %d", output_target.length, getBatches()));
        return null;
    }
    for (@Nonnull Result nnResult : array) {
        nnResult.getData().freeRef();
        nnResult.freeRef();
    }
    for (@Nonnull Tensor[] tensors : input_target) {
        for (@Nonnull Tensor tensor : tensors) {
            tensor.freeRef();
        }
    }
    // if (output_target.length != inputPrototype.length) return null;
    Tensor[][] trainingInput = append(shuffleCopy(random, inputPrototype), output_target);
    @Nonnull TestResult testResult = trainAll("Input Convergence", log, trainingInput, network, buildMask(inputPrototype.length));
    Arrays.stream(trainingInput).flatMap(x -> Arrays.stream(x)).forEach(x -> x.freeRef());
    return testResult;
}

Example 22 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput 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 23 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class TrainingTester method testCompleteLearning.

/**
 * Test complete learning apply result.
 *
 * @param log            the log
 * @param component      the component
 * @param random         the random
 * @param inputPrototype the input prototype
 * @return the apply result
 */
@Nonnull
public TestResult testCompleteLearning(@Nonnull final NotebookOutput log, @Nonnull final Layer component, final Random random, @Nonnull final Tensor[] inputPrototype) {
    @Nonnull final Layer network_target = shuffle(random, component.copy()).freeze();
    final Tensor[][] input_target = shuffleCopy(random, inputPrototype);
    log.p("In this apply, attempt to train a network to emulate a randomized network given an example input/output. The target state is:");
    log.code(() -> {
        return network_target.state().stream().map(Arrays::toString).reduce((a, b) -> a + "\n" + b).orElse("");
    });
    log.p("We simultaneously regress this target input:");
    log.code(() -> {
        return Arrays.stream(input_target).flatMap(x -> Arrays.stream(x)).map(x -> x.prettyPrint()).reduce((a, b) -> a + "\n" + b).orElse("");
    });
    log.p("Which produces the following output:");
    Result[] inputs = ConstantResult.batchResultArray(input_target);
    Result eval = network_target.eval(inputs);
    network_target.freeRef();
    Arrays.stream(inputs).forEach(ReferenceCounting::freeRef);
    TensorList result = eval.getData();
    eval.freeRef();
    final Tensor[] output_target = result.stream().toArray(i -> new Tensor[i]);
    log.code(() -> {
        return Stream.of(output_target).map(x -> x.prettyPrint()).reduce((a, b) -> a + "\n" + b).orElse("");
    });
    // if (output_target.length != inputPrototype.length) return null;
    return trainAll("Integrated Convergence", log, append(shuffleCopy(random, inputPrototype), output_target), shuffle(random, component.copy()), buildMask(inputPrototype.length));
}

Example 24 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class TrainingTester method test.

/**
 * Test.
 *
 * @param log            the log
 * @param component      the component
 * @param inputPrototype the input prototype
 */
@Override
public ComponentResult test(@Nonnull final NotebookOutput log, @Nonnull final Layer component, @Nonnull final Tensor... inputPrototype) {
    printHeader(log);
    final boolean testModel = !component.state().isEmpty();
    if (testModel && isZero(component.state().stream().flatMapToDouble(x1 -> Arrays.stream(x1)))) {
        throw new AssertionError("Weights are all zero?");
    }
    if (isZero(Arrays.stream(inputPrototype).flatMapToDouble(x -> Arrays.stream(x.getData())))) {
        throw new AssertionError("Inputs are all zero?");
    }
    @Nonnull final Random random = new Random();
    final boolean testInput = Arrays.stream(inputPrototype).anyMatch(x -> x.length() > 0);
    @Nullable TestResult inputLearning;
    if (testInput) {
        log.h2("Input Learning");
        inputLearning = testInputLearning(log, component, random, inputPrototype);
    } else {
        inputLearning = null;
    }
    @Nullable TestResult modelLearning;
    if (testModel) {
        log.h2("Model Learning");
        modelLearning = testModelLearning(log, component, random, inputPrototype);
    } else {
        modelLearning = null;
    }
    @Nullable TestResult completeLearning;
    if (testInput && testModel) {
        log.h2("Composite Learning");
        completeLearning = testCompleteLearning(log, component, random, inputPrototype);
    } else {
        completeLearning = null;
    }
    log.h2("Results");
    log.code(() -> {
        return grid(inputLearning, modelLearning, completeLearning);
    });
    ComponentResult result = log.code(() -> {
        return new ComponentResult(null == inputLearning ? null : inputLearning.value, null == modelLearning ? null : modelLearning.value, null == completeLearning ? null : completeLearning.value);
    });
    log.setFrontMatterProperty("training_analysis", result.toString());
    if (throwExceptions) {
        assert result.complete.map.values().stream().allMatch(x -> x.type == ResultType.Converged);
        assert result.input.map.values().stream().allMatch(x -> x.type == ResultType.Converged);
        assert result.model.map.values().stream().allMatch(x -> x.type == ResultType.Converged);
    }
    return result;
}

Example 25 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class StyleTransfer_VGG19 method run.

/**
 * Test.
 *
 * @param log the log
 */
public void run(@Nonnull NotebookOutput log) {
    StyleTransfer.VGG19 styleTransfer = new StyleTransfer.VGG19();
    init(log);
    Precision precision = Precision.Float;
    int imageSize = 400;
    styleTransfer.parallelLossFunctions = true;
    double growthFactor = Math.sqrt(1.5);
    CharSequence lakeAndForest = "H:\\SimiaCryptus\\Artistry\\Owned\\IMG_20170624_153541213-EFFECTS.jpg";
    String monkey = "H:\\SimiaCryptus\\Artistry\\capuchin-monkey-2759768_960_720.jpg";
    CharSequence vanGogh1 = "H:\\SimiaCryptus\\Artistry\\portraits\\vangogh\\Van_Gogh_-_Portrait_of_Pere_Tanguy_1887-8.jpg";
    CharSequence vanGogh2 = "H:\\SimiaCryptus\\Artistry\\portraits\\vangogh\\800px-Vincent_van_Gogh_-_Dr_Paul_Gachet_-_Google_Art_Project.jpg";
    CharSequence threeMusicians = "H:\\SimiaCryptus\\Artistry\\portraits\\picasso\\800px-Pablo_Picasso,_1921,_Nous_autres_musiciens_(Three_Musicians),_oil_on_canvas,_204.5_x_188.3_cm,_Philadelphia_Museum_of_Art.jpg";
    CharSequence maJolie = "H:\\SimiaCryptus\\Artistry\\portraits\\picasso\\Ma_Jolie_Pablo_Picasso.jpg";
    Map<List<CharSequence>, StyleTransfer.StyleCoefficients> styles = new HashMap<>();
    double coeff_mean = 1e1;
    double coeff_cov = 1e0;
    styles.put(Arrays.asList(// threeMusicians, maJolie
    vanGogh1, vanGogh2), new StyleTransfer.StyleCoefficients(StyleTransfer.CenteringMode.Origin).set(MultiLayerVGG19.LayerType.Layer_1b, coeff_mean, coeff_cov).set(MultiLayerVGG19.LayerType.Layer_1d, coeff_mean, coeff_cov));
    StyleTransfer.ContentCoefficients contentCoefficients = new StyleTransfer.ContentCoefficients().set(MultiLayerVGG19.LayerType.Layer_1c, 1e0);
    int trainingMinutes = 90;
    log.h1("Phase 0");
    BufferedImage canvasImage = ArtistryUtil.load(monkey, imageSize);
    canvasImage = TestUtil.resize(canvasImage, imageSize, true);
    canvasImage = TestUtil.resize(TestUtil.resize(canvasImage, 25, true), imageSize, true);
    // canvasImage = randomize(canvasImage, x -> 10 * (FastRandom.INSTANCE.random()) * (FastRandom.INSTANCE.random() < 0.9 ? 1 : 0));
    canvasImage = ArtistryUtil.randomize(canvasImage, x -> x + 2 * 1 * (FastRandom.INSTANCE.random() - 0.5));
    // canvasImage = randomize(canvasImage, x -> 10*(FastRandom.INSTANCE.random()-0.5));
    // canvasImage = randomize(canvasImage, x -> x*(FastRandom.INSTANCE.random()));
    Map<CharSequence, BufferedImage> styleImages = new HashMap<>();
    styleImages.clear();
    styleImages.putAll(styles.keySet().stream().flatMap(x -> x.stream()).collect(Collectors.toMap(x -> x, file -> ArtistryUtil.load(file))));
    StyleTransfer.StyleSetup styleSetup = new StyleTransfer.StyleSetup(precision, ArtistryUtil.load(monkey, canvasImage.getWidth(), canvasImage.getHeight()), contentCoefficients, styleImages, styles);
    StyleTransfer.NeuralSetup measureStyle = styleTransfer.measureStyle(styleSetup);
    canvasImage = styleTransfer.styleTransfer(server, log, canvasImage, styleSetup, trainingMinutes, measureStyle);
    for (int i = 1; i < 10; i++) {
        log.h1("Phase " + i);
        imageSize = (int) (imageSize * growthFactor);
        canvasImage = TestUtil.resize(canvasImage, imageSize, true);
        canvasImage = styleTransfer.styleTransfer(server, log, canvasImage, styleSetup, trainingMinutes, measureStyle);
    }
    log.setFrontMatterProperty("status", "OK");
}