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

Example 6 with ScalarStatistics

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

the class TestUtil method printDataStatistics.

/**
 * Print data statistics.
 *
 * @param log  the log
 * @param data the data
 */
public static void printDataStatistics(@Nonnull final NotebookOutput log, @Nonnull final Tensor[][] data) {
    for (int col = 1; col < data[0].length; col++) {
        final int c = col;
        log.out("Learned Representation Statistics for Column " + col + " (all bands)");
        log.code(() -> {
            @Nonnull final ScalarStatistics scalarStatistics = new ScalarStatistics();
            Arrays.stream(data).flatMapToDouble(row -> Arrays.stream(row[c].getData())).forEach(v -> scalarStatistics.add(v));
            return scalarStatistics.getMetrics();
        });
        final int _col = col;
        log.out("Learned Representation Statistics for Column " + col + " (by band)");
        log.code(() -> {
            @Nonnull final int[] dimensions = data[0][_col].getDimensions();
            return IntStream.range(0, dimensions[2]).mapToObj(x -> x).flatMap(b -> {
                return Arrays.stream(data).map(r -> r[_col]).map(tensor -> {
                    @Nonnull final ScalarStatistics scalarStatistics = new ScalarStatistics();
                    scalarStatistics.add(new Tensor(dimensions[0], dimensions[1]).setByCoord(coord -> tensor.get(coord.getCoords()[0], coord.getCoords()[1], b)).getData());
                    return scalarStatistics;
                });
            }).map(x -> x.getMetrics().toString()).reduce((a, b) -> a + "\n" + b).get();
        });
    }
}

Example 7 with ScalarStatistics

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

the class EncodingProblem method run.

@Nonnull
@Override
public EncodingProblem run(@Nonnull final NotebookOutput log) {
    @Nonnull final TrainingMonitor monitor = TestUtil.getMonitor(history);
    Tensor[][] trainingData;
    try {
        trainingData = data.trainingData().map(labeledObject -> {
            return new Tensor[] { new Tensor(features).set(this::random), labeledObject.data };
        }).toArray(i -> new Tensor[i][]);
    } catch (@Nonnull final IOException e) {
        throw new RuntimeException(e);
    }
    @Nonnull final DAGNetwork imageNetwork = revFactory.vectorToImage(log, features);
    log.h3("Network Diagram");
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(imageNetwork)).height(400).width(600).render(Format.PNG).toImage();
    });
    @Nonnull final PipelineNetwork trainingNetwork = new PipelineNetwork(2);
    @Nullable final DAGNode image = trainingNetwork.add(imageNetwork, trainingNetwork.getInput(0));
    @Nullable final DAGNode softmax = trainingNetwork.add(new SoftmaxActivationLayer(), trainingNetwork.getInput(0));
    trainingNetwork.add(new SumInputsLayer(), trainingNetwork.add(new EntropyLossLayer(), softmax, softmax), trainingNetwork.add(new NthPowerActivationLayer().setPower(1.0 / 2.0), trainingNetwork.add(new MeanSqLossLayer(), image, trainingNetwork.getInput(1))));
    log.h3("Training");
    log.p("We start by training apply a very small population to improve initial convergence performance:");
    TestUtil.instrumentPerformance(trainingNetwork);
    @Nonnull final Tensor[][] primingData = Arrays.copyOfRange(trainingData, 0, 1000);
    @Nonnull final ValidatingTrainer preTrainer = optimizer.train(log, (SampledTrainable) new SampledArrayTrainable(primingData, trainingNetwork, trainingSize, batchSize).setMinSamples(trainingSize).setMask(true, false), new ArrayTrainable(primingData, trainingNetwork, batchSize), monitor);
    log.code(() -> {
        preTrainer.setTimeout(timeoutMinutes / 2, TimeUnit.MINUTES).setMaxIterations(batchSize).run();
    });
    TestUtil.extractPerformance(log, trainingNetwork);
    log.p("Then our main training phase:");
    TestUtil.instrumentPerformance(trainingNetwork);
    @Nonnull final ValidatingTrainer mainTrainer = optimizer.train(log, (SampledTrainable) new SampledArrayTrainable(trainingData, trainingNetwork, trainingSize, batchSize).setMinSamples(trainingSize).setMask(true, false), new ArrayTrainable(trainingData, trainingNetwork, batchSize), monitor);
    log.code(() -> {
        mainTrainer.setTimeout(timeoutMinutes, TimeUnit.MINUTES).setMaxIterations(batchSize).run();
    });
    TestUtil.extractPerformance(log, trainingNetwork);
    if (!history.isEmpty()) {
        log.code(() -> {
            return TestUtil.plot(history);
        });
        log.code(() -> {
            return TestUtil.plotTime(history);
        });
    }
    try {
        @Nonnull String filename = log.getName().toString() + EncodingProblem.modelNo++ + "_plot.png";
        ImageIO.write(Util.toImage(TestUtil.plot(history)), "png", log.file(filename));
        log.appendFrontMatterProperty("result_plot", filename, ";");
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
    // log.file()
    @Nonnull final String modelName = "encoding_model_" + EncodingProblem.modelNo++ + ".json";
    log.appendFrontMatterProperty("result_model", modelName, ";");
    log.p("Saved model as " + log.file(trainingNetwork.getJson().toString(), modelName, modelName));
    log.h3("Results");
    @Nonnull final PipelineNetwork testNetwork = new PipelineNetwork(2);
    testNetwork.add(imageNetwork, testNetwork.getInput(0));
    log.code(() -> {
        @Nonnull final TableOutput table = new TableOutput();
        Arrays.stream(trainingData).map(tensorArray -> {
            @Nullable final Tensor predictionSignal = testNetwork.eval(tensorArray).getData().get(0);
            @Nonnull final LinkedHashMap<CharSequence, Object> row = new LinkedHashMap<>();
            row.put("Source", log.image(tensorArray[1].toImage(), ""));
            row.put("Echo", log.image(predictionSignal.toImage(), ""));
            return row;
        }).filter(x -> null != x).limit(10).forEach(table::putRow);
        return table;
    });
    log.p("Learned Model Statistics:");
    log.code(() -> {
        @Nonnull final ScalarStatistics scalarStatistics = new ScalarStatistics();
        trainingNetwork.state().stream().flatMapToDouble(x -> Arrays.stream(x)).forEach(v -> scalarStatistics.add(v));
        return scalarStatistics.getMetrics();
    });
    log.p("Learned Representation Statistics:");
    log.code(() -> {
        @Nonnull final ScalarStatistics scalarStatistics = new ScalarStatistics();
        Arrays.stream(trainingData).flatMapToDouble(row -> Arrays.stream(row[0].getData())).forEach(v -> scalarStatistics.add(v));
        return scalarStatistics.getMetrics();
    });
    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 = imageNetwork.eval(input).getData().get(0);
        TestUtil.renderToImages(tensor, true).forEach(img -> {
            log.out(log.image(img, ""));
        });
    }
    return this;
}

Example 8 with ScalarStatistics

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

the class BatchDerivativeTester method testLearning.

/**
 * Test learning tolerance statistics.
 *
 * @param component  the component
 * @param IOPair     the io pair
 * @param statistics the statistics
 * @return the tolerance statistics
 */
public ToleranceStatistics testLearning(@Nonnull Layer component, @Nonnull IOPair IOPair, ToleranceStatistics statistics) {
    final ToleranceStatistics prev = statistics;
    statistics = IntStream.range(0, component.state().size()).mapToObj(i -> {
        @Nullable final Tensor measuredGradient = !verify ? null : measureLearningGradient(component, i, IOPair.getOutputPrototype(), IOPair.getInputPrototype());
        @Nonnull final Tensor implementedGradient = getLearningGradient(component, i, IOPair.getOutputPrototype(), IOPair.getInputPrototype());
        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", new Tensor(component.state().get(i)).prettyPrint()));
                    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", measuredGradient.minus(implementedGradient).prettyPrint()));
                        log.info(String.format("Error Statistics: %s", new ScalarStatistics().add(measuredGradient.minus(implementedGradient).getData())));
                    }
                }
                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", measuredGradient.minus(implementedGradient).prettyPrint()));
                log.info(String.format("Error Statistics: %s", new ScalarStatistics().add(measuredGradient.minus(implementedGradient).getData())));
            }
            throw e;
        }
    }).reduce((a, b) -> a.combine(b)).map(x -> x.combine(prev)).orElseGet(() -> prev);
    return statistics;
}

Example 9 with ScalarStatistics

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

the class BatchDerivativeTester method test.

/**
 * Test tolerance statistics.
 *
 * @param log
 * @param component      the component
 * @param inputPrototype the input prototype
 * @return the tolerance statistics
 */
@Override
public ToleranceStatistics test(@Nonnull final NotebookOutput log, @Nonnull final Layer component, @Nonnull final Tensor... inputPrototype) {
    log.h1("Differential Validation");
    @Nonnull IOPair ioPair = new IOPair(component, inputPrototype[0]).invoke();
    if (verbose) {
        log.code(() -> {
            BatchDerivativeTester.log.info(String.format("Inputs: %s", Arrays.stream(inputPrototype).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).get()));
            BatchDerivativeTester.log.info(String.format("Inputs Statistics: %s", Arrays.stream(inputPrototype).map(x -> new ScalarStatistics().add(x.getData()).toString()).reduce((a, b) -> a + ",\n" + b).get()));
            BatchDerivativeTester.log.info(String.format("Output: %s", ioPair.getOutputPrototype().prettyPrint()));
            BatchDerivativeTester.log.info(String.format("Outputs Statistics: %s", new ScalarStatistics().add(ioPair.getOutputPrototype().getData())));
        });
    }
    ToleranceStatistics _statistics = new ToleranceStatistics();
    if (isTestFeedback()) {
        log.h2("Feedback Validation");
        log.p("We validate the agreement between the implemented derivative _of the inputs_ apply finite difference estimations:");
        ToleranceStatistics statistics = _statistics;
        _statistics = log.code(() -> {
            return testFeedback(component, ioPair, statistics);
        });
    }
    if (isTestLearning()) {
        log.h2("Learning Validation");
        log.p("We validate the agreement between the implemented derivative _of the internal weights_ apply finite difference estimations:");
        ToleranceStatistics statistics = _statistics;
        _statistics = log.code(() -> {
            return testLearning(component, ioPair, statistics);
        });
    }
    log.h2("Total Accuracy");
    log.p("The overall agreement accuracy between the implemented derivative and the finite difference estimations:");
    ToleranceStatistics statistics = _statistics;
    log.code(() -> {
        // log.info(String.format("Component: %s\nInputs: %s\noutput=%s", component, Arrays.toString(inputPrototype), outputPrototype));
        BatchDerivativeTester.log.info(String.format("Finite-Difference Derivative Accuracy:"));
        BatchDerivativeTester.log.info(String.format("absoluteTol: %s", statistics.absoluteTol));
        BatchDerivativeTester.log.info(String.format("relativeTol: %s", statistics.relativeTol));
    });
    log.h2("Frozen and Alive Status");
    log.code(() -> {
        testFrozen(component, ioPair.getInputPrototype());
        testUnFrozen(component, ioPair.getInputPrototype());
    });
    return _statistics;
}

Example 10 with ScalarStatistics

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

the class BatchDerivativeTester method testFeedback.

/**
 * Test feedback tolerance statistics.
 *
 * @param component  the component
 * @param IOPair     the io pair
 * @param statistics the statistics
 * @return the tolerance statistics
 */
public ToleranceStatistics testFeedback(@Nonnull Layer component, @Nonnull IOPair IOPair, ToleranceStatistics statistics) {
    statistics = statistics.combine(IntStream.range(0, IOPair.getInputPrototype().length).mapToObj(i -> {
        @Nullable final Tensor measuredGradient = !verify ? null : measureFeedbackGradient(component, i, IOPair.getOutputPrototype(), IOPair.getInputPrototype());
        @Nonnull final Tensor implementedGradient = getFeedbackGradient(component, i, IOPair.getOutputPrototype(), IOPair.getInputPrototype());
        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", IOPair.getInputPrototype()[i].prettyPrint()));
                log.info(String.format("Value Statistics: %s", new ScalarStatistics().add(IOPair.getInputPrototype()[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", measuredGradient.minus(implementedGradient).prettyPrint()));
                    log.info(String.format("Error Statistics: %s", new ScalarStatistics().add(measuredGradient.minus(implementedGradient).getData())));
                }
            }
            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", IOPair.getInputPrototype()[i].prettyPrint()));
            log.info(String.format("Value Statistics: %s", new ScalarStatistics().add(IOPair.getInputPrototype()[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", measuredGradient.minus(implementedGradient).prettyPrint()));
                log.info(String.format("Error Statistics: %s", new ScalarStatistics().add(measuredGradient.minus(implementedGradient).getData())));
            }
            throw e;
        }
    }).reduce((a, b) -> a.combine(b)).get());
    return statistics;
}