Examples with ScalarStatistics com.simiacryptus.util.data.ScalarStatistics used on opensource projects

Example 1 with ScalarStatistics

use of com.simiacryptus.util.data.ScalarStatistics in project MindsEye by SimiaCryptus.

the class MonitoringWrapperLayer method getMetrics.

@Nonnull
@Override
public Map<CharSequence, Object> getMetrics() {
    @Nonnull final HashMap<CharSequence, Object> map = new HashMap<>();
    map.put("class", getInner().getClass().getName());
    map.put("totalBatches", totalBatches);
    map.put("totalItems", totalItems);
    map.put("outputStatistics", forwardSignal.getMetrics());
    map.put("backpropStatistics", backwardSignal.getMetrics());
    if (verbose) {
        map.put("forwardPerformance", forwardPerformance.getMetrics());
        map.put("backwardPerformance", backwardPerformance.getMetrics());
    }
    final double batchesPerItem = totalBatches * 1.0 / totalItems;
    map.put("avgMsPerItem", 1000 * batchesPerItem * forwardPerformance.getMean());
    map.put("medianMsPerItem", 1000 * batchesPerItem * forwardPerformance.getPercentile(0.5));
    final double backpropMean = backwardPerformance.getMean();
    final double backpropMedian = backwardPerformance.getPercentile(0.5);
    map.put("avgMsPerItem_Backward", 1000 * batchesPerItem * backpropMean);
    map.put("medianMsPerItem_Backward", 1000 * batchesPerItem * backpropMedian);
    @Nullable final List<double[]> state = state();
    @Nonnull final ScalarStatistics statistics = new PercentileStatistics();
    for (@Nonnull final double[] s : state) {
        for (final double v : s) {
            statistics.add(v);
        }
    }
    if (statistics.getCount() > 0) {
        @Nonnull final HashMap<CharSequence, Object> weightStats = new HashMap<>();
        weightStats.put("buffers", state.size());
        weightStats.putAll(statistics.getMetrics());
        map.put("weights", weightStats);
    }
    return map;
}

Example 2 with ScalarStatistics

use of com.simiacryptus.util.data.ScalarStatistics in project MindsEye by SimiaCryptus.

the class BatchingTester method test.

/**
 * Test tolerance statistics.
 *
 * @param reference      the reference
 * @param inputPrototype the input prototype
 * @return the tolerance statistics
 */
@Nonnull
public ToleranceStatistics test(@Nullable final Layer reference, @Nonnull final Tensor[] inputPrototype) {
    if (null == reference)
        return new ToleranceStatistics();
    final TensorList[] inputTensorLists = Arrays.stream(inputPrototype).map(t -> TensorArray.wrap(IntStream.range(0, getBatchSize()).mapToObj(i -> t.map(v -> getRandom())).toArray(i -> new Tensor[i]))).toArray(i -> new TensorList[i]);
    @Nonnull final SimpleResult asABatch;
    final List<SimpleEval> oneAtATime;
    try {
        asABatch = SimpleListEval.run(reference, inputTensorLists);
        oneAtATime = IntStream.range(0, getBatchSize()).mapToObj(batch -> {
            Tensor[] inputTensors = IntStream.range(0, inputTensorLists.length).mapToObj(i -> inputTensorLists[i].get(batch)).toArray(i -> new Tensor[i]);
            @Nonnull SimpleEval eval = SimpleEval.run(reference, inputTensors);
            for (@Nonnull Tensor tensor : inputTensors) {
                tensor.freeRef();
            }
            return eval;
        }).collect(Collectors.toList());
    } finally {
        for (@Nonnull TensorList tensorList : inputTensorLists) {
            tensorList.freeRef();
        }
    }
    try {
        TensorList batchOutput = asABatch.getOutput();
        @Nonnull IntFunction<ToleranceStatistics> toleranceStatisticsIntFunction = batch -> {
            @Nullable Tensor batchTensor = batchOutput.get(batch);
            @Nonnull ToleranceStatistics accumulate = new ToleranceStatistics().accumulate(batchTensor.getData(), oneAtATime.get(batch).getOutput().getData());
            batchTensor.freeRef();
            return accumulate;
        };
        int batchLength = batchOutput.length();
        @Nonnull final ToleranceStatistics outputAgreement = IntStream.range(0, Math.min(getBatchSize(), batchLength)).mapToObj(toleranceStatisticsIntFunction).reduce((a, b) -> a.combine(b)).get();
        if (!(outputAgreement.absoluteTol.getMax() < tolerance)) {
            logger.info("Batch Output: " + batchOutput.stream().map(x -> {
                String str = x.prettyPrint();
                x.freeRef();
                return str;
            }).collect(Collectors.toList()));
            logger.info("Singular Output: " + oneAtATime.stream().map(x -> x.getOutput().prettyPrint()).collect(Collectors.toList()));
            throw new AssertionError("Output Corrupt: " + outputAgreement);
        }
        ToleranceStatistics derivativeAgreement = IntStream.range(0, Math.min(getBatchSize(), batchLength)).mapToObj(batch -> {
            IntFunction<ToleranceStatistics> statisticsFunction = input -> {
                @Nullable Tensor a = asABatch.getInputDerivative()[input].get(batch);
                Tensor b = oneAtATime.get(batch).getDerivative()[input];
                @Nonnull Tensor diff = a.minus(b);
                logger.info("Error: " + diff.prettyPrint());
                logger.info("Scalar Statistics: " + new ScalarStatistics().add(diff.getData()).getMetrics());
                double[][] points = Arrays.stream(diff.getData()).mapToObj(x -> new double[] { x }).toArray(i -> new double[i][]);
                // logger.info("Density: " + new DensityTree("x").setMinSplitFract(1e-8).setSplitSizeThreshold(2).new Node(points));
                diff.freeRef();
                @Nonnull ToleranceStatistics toleranceStatistics = new ToleranceStatistics().accumulate(a.getData(), b.getData());
                a.freeRef();
                return toleranceStatistics;
            };
            return IntStream.range(0, Math.min(inputPrototype.length, batchLength)).mapToObj(statisticsFunction).reduce((a, b) -> a.combine(b)).orElse(null);
        }).filter(x -> x != null).reduce((a, b) -> a.combine(b)).orElse(null);
        if (null != derivativeAgreement && !(derivativeAgreement.absoluteTol.getMax() < tolerance)) {
            throw new AssertionError("Derivatives Corrupt: " + derivativeAgreement);
        }
        return null != derivativeAgreement ? derivativeAgreement.combine(outputAgreement) : outputAgreement;
    } finally {
        asABatch.freeRef();
        oneAtATime.forEach(x -> x.freeRef());
    }
}

Example 3 with ScalarStatistics

use of com.simiacryptus.util.data.ScalarStatistics in project MindsEye by SimiaCryptus.

the class SingleDerivativeTester method testFeedback.

/**
 * Test feedback tolerance statistics.
 *
 * @param statistics      the statistics
 * @param component       the component
 * @param inputPrototype  the input prototype
 * @param outputPrototype the output prototype
 * @return the tolerance statistics
 */
@Nonnull
public ToleranceStatistics testFeedback(@Nonnull ToleranceStatistics statistics, @Nonnull Layer component, @Nonnull Tensor[] inputPrototype, @Nonnull Tensor outputPrototype) {
    Optional<ToleranceStatistics> optional = IntStream.range(0, inputPrototype.length).mapToObj(i -> {
        @Nullable final Tensor measuredGradient = !verify ? null : measureFeedbackGradient(component, i, outputPrototype, inputPrototype);
        @Nonnull final Tensor implementedGradient = getFeedbackGradient(component, i, outputPrototype, inputPrototype);
        @Nonnull Tensor difference = measuredGradient.minus(implementedGradient);
        try {
            final ToleranceStatistics result = IntStream.range(0, null == measuredGradient ? 0 : measuredGradient.length()).mapToObj(i1 -> {
                return new ToleranceStatistics().accumulate(measuredGradient.getData()[i1], implementedGradient.getData()[i1]);
            }).reduce((a, b) -> a.combine(b)).orElse(new ToleranceStatistics());
            if (!(result.absoluteTol.getMax() < tolerance))
                throw new AssertionError(result.toString());
            // log.info(String.format("Component: %s", component));
            if (verbose) {
                log.info(String.format("Feedback for input %s", i));
                log.info(String.format("Inputs Values: %s", inputPrototype[i].prettyPrint()));
                log.info(String.format("Value Statistics: %s", new ScalarStatistics().add(inputPrototype[i].getData())));
                log.info(String.format("Implemented Feedback: %s", implementedGradient.prettyPrint()));
                log.info(String.format("Implemented Statistics: %s", new ScalarStatistics().add(implementedGradient.getData())));
                if (null != measuredGradient) {
                    log.info(String.format("Measured Feedback: %s", measuredGradient.prettyPrint()));
                    log.info(String.format("Measured Statistics: %s", new ScalarStatistics().add(measuredGradient.getData())));
                    log.info(String.format("Feedback Error: %s", difference.prettyPrint()));
                    log.info(String.format("Error Statistics: %s", new ScalarStatistics().add(difference.getData())));
                }
            }
            difference.freeRef();
            measuredGradient.freeRef();
            implementedGradient.freeRef();
            return result;
        } catch (@Nonnull final Throwable e) {
            // log.info(String.format("Component: %s", component));
            log.info(String.format("Feedback for input %s", i));
            log.info(String.format("Inputs Values: %s", inputPrototype[i].prettyPrint()));
            log.info(String.format("Value Statistics: %s", new ScalarStatistics().add(inputPrototype[i].getData())));
            log.info(String.format("Implemented Feedback: %s", implementedGradient.prettyPrint()));
            log.info(String.format("Implemented Statistics: %s", new ScalarStatistics().add(implementedGradient.getData())));
            if (null != measuredGradient) {
                log.info(String.format("Measured: %s", measuredGradient.prettyPrint()));
                log.info(String.format("Measured Statistics: %s", new ScalarStatistics().add(measuredGradient.getData())));
                log.info(String.format("Feedback Error: %s", difference.prettyPrint()));
                log.info(String.format("Error Statistics: %s", new ScalarStatistics().add(difference.getData())));
            }
            measuredGradient.freeRef();
            implementedGradient.freeRef();
            difference.freeRef();
            throw e;
        }
    }).reduce((a, b) -> a.combine(b));
    if (!optional.isPresent())
        return statistics;
    return statistics.combine(optional.orElse(null));
}

Example 4 with ScalarStatistics

use of com.simiacryptus.util.data.ScalarStatistics in project MindsEye by SimiaCryptus.

the class SingleDerivativeTester method testLearning.

/**
 * Test learning tolerance statistics.
 *
 * @param prev            the prev
 * @param component       the component
 * @param inputPrototype  the input prototype
 * @param outputPrototype the output prototype
 * @return the tolerance statistics
 */
public ToleranceStatistics testLearning(@Nonnull ToleranceStatistics prev, @Nonnull Layer component, Tensor[] inputPrototype, @Nonnull Tensor outputPrototype) {
    return IntStream.range(0, component.state().size()).mapToObj(i -> {
        @Nullable final Tensor measuredGradient = !verify ? null : measureLearningGradient(component, i, outputPrototype, inputPrototype);
        @Nonnull final Tensor implementedGradient = getLearningGradient(component, i, outputPrototype, inputPrototype);
        @Nonnull Tensor difference = measuredGradient.minus(implementedGradient);
        try {
            final ToleranceStatistics result = IntStream.range(0, null == measuredGradient ? 0 : measuredGradient.length()).mapToObj(i1 -> {
                return new ToleranceStatistics().accumulate(measuredGradient.getData()[i1], implementedGradient.getData()[i1]);
            }).reduce((a, b) -> a.combine(b)).orElse(new ToleranceStatistics());
            if (!(result.absoluteTol.getMax() < tolerance)) {
                throw new AssertionError(result.toString());
            } else {
                // log.info(String.format("Component: %s", component));
                if (verbose) {
                    log.info(String.format("Learning Gradient for weight setByCoord %s", i));
                    log.info(String.format("Weights: %s", Tensor.prettyPrint(component.state().get(i))));
                    log.info(String.format("Implemented Gradient: %s", implementedGradient.prettyPrint()));
                    log.info(String.format("Implemented Statistics: %s", new ScalarStatistics().add(implementedGradient.getData())));
                    if (null != measuredGradient) {
                        log.info(String.format("Measured Gradient: %s", measuredGradient.prettyPrint()));
                        log.info(String.format("Measured Statistics: %s", new ScalarStatistics().add(measuredGradient.getData())));
                        log.info(String.format("Gradient Error: %s", difference.prettyPrint()));
                        log.info(String.format("Error Statistics: %s", new ScalarStatistics().add(difference.getData())));
                    }
                }
                difference.freeRef();
                return result;
            }
        } catch (@Nonnull final Throwable e) {
            // log.info(String.format("Component: %s", component));
            log.info(String.format("Learning Gradient for weight setByCoord %s", i));
            log.info(String.format("Implemented Gradient: %s", implementedGradient.prettyPrint()));
            log.info(String.format("Implemented Statistics: %s", new ScalarStatistics().add(implementedGradient.getData())));
            if (null != measuredGradient) {
                log.info(String.format("Measured Gradient: %s", measuredGradient.prettyPrint()));
                log.info(String.format("Measured Statistics: %s", new ScalarStatistics().add(measuredGradient.getData())));
                log.info(String.format("Gradient Error: %s", difference.prettyPrint()));
                log.info(String.format("Error Statistics: %s", new ScalarStatistics().add(difference.getData())));
            }
            difference.freeRef();
            throw e;
        } finally {
            measuredGradient.freeRef();
            implementedGradient.freeRef();
        }
    }).reduce((a, b) -> a.combine(b)).map(x -> x.combine(prev)).orElseGet(() -> prev);
}

Example 5 with ScalarStatistics

use of com.simiacryptus.util.data.ScalarStatistics in project MindsEye by SimiaCryptus.

the class StyleTransfer method measureStyle.

/**
 * Measure style neural setup.
 *
 * @param style the style
 * @return the neural setup
 */
public NeuralSetup measureStyle(final StyleSetup<T> style) {
    NeuralSetup<T> self = new NeuralSetup(style);
    List<CharSequence> keyList = style.styleImages.keySet().stream().collect(Collectors.toList());
    Tensor contentInput = Tensor.fromRGB(style.contentImage);
    List<Tensor> styleInputs = keyList.stream().map(x -> style.styleImages.get(x)).map(img -> Tensor.fromRGB(img)).collect(Collectors.toList());
    IntStream.range(0, keyList.size()).forEach(i -> {
        self.styleTargets.put(keyList.get(i), new StyleTarget());
    });
    self.contentTarget = new ContentTarget();
    for (final T layerType : getLayerTypes()) {
        System.gc();
        final PipelineNetwork network = layerType.texture();
        ArtistryUtil.setPrecision(network, style.precision);
        Tensor content = network.eval(contentInput).getDataAndFree().getAndFree(0);
        self.contentTarget.content.put(layerType, content);
        logger.info(String.format("%s : target content = %s", layerType.name(), content.prettyPrint()));
        logger.info(String.format("%s : content statistics = %s", layerType.name(), JsonUtil.toJson(new ScalarStatistics().add(content.getData()).getMetrics())));
        for (int i = 0; i < styleInputs.size(); i++) {
            Tensor styleInput = styleInputs.get(i);
            CharSequence key = keyList.get(i);
            StyleTarget<T> styleTarget = self.styleTargets.get(key);
            if (0 == self.style.styles.entrySet().stream().filter(e1 -> e1.getKey().contains(key)).map(x -> (LayerStyleParams) x.getValue().params.get(layerType)).filter(x -> null != x).filter(x -> x.mean != 0 || x.cov != 0).count())
                continue;
            System.gc();
            Tensor mean = ArtistryUtil.wrapTilesAvg(ArtistryUtil.avg(network.copy())).eval(styleInput).getDataAndFree().getAndFree(0);
            styleTarget.mean.put(layerType, mean);
            logger.info(String.format("%s : style mean = %s", layerType.name(), mean.prettyPrint()));
            logger.info(String.format("%s : mean statistics = %s", layerType.name(), JsonUtil.toJson(new ScalarStatistics().add(mean.getData()).getMetrics())));
            if (0 == self.style.styles.entrySet().stream().filter(e1 -> e1.getKey().contains(key)).map(x -> (LayerStyleParams) x.getValue().params.get(layerType)).filter(x -> null != x).filter(x -> x.cov != 0).count())
                continue;
            System.gc();
            Tensor cov0 = ArtistryUtil.wrapTilesAvg(ArtistryUtil.gram(network.copy())).eval(styleInput).getDataAndFree().getAndFree(0);
            Tensor cov1 = ArtistryUtil.wrapTilesAvg(ArtistryUtil.gram(network.copy(), mean)).eval(styleInput).getDataAndFree().getAndFree(0);
            styleTarget.cov0.put(layerType, cov0);
            styleTarget.cov1.put(layerType, cov1);
            int featureBands = mean.getDimensions()[2];
            int covarianceElements = cov1.getDimensions()[2];
            int selectedBands = covarianceElements / featureBands;
            logger.info(String.format("%s : target cov0 = %s", layerType.name(), cov0.reshapeCast(featureBands, selectedBands, 1).prettyPrint()));
            logger.info(String.format("%s : cov0 statistics = %s", layerType.name(), JsonUtil.toJson(new ScalarStatistics().add(cov0.getData()).getMetrics())));
            logger.info(String.format("%s : target cov1 = %s", layerType.name(), cov1.reshapeCast(featureBands, selectedBands, 1).prettyPrint()));
            logger.info(String.format("%s : cov1 statistics = %s", layerType.name(), JsonUtil.toJson(new ScalarStatistics().add(cov1.getData()).getMetrics())));
        }
    }
    return self;
}