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

Example 11 with ScalarStatistics

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

the class SingleDerivativeTester method test.

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

Example 12 with ScalarStatistics

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

the class EncodingUtil method printModel.

/**
 * Print model.
 *
 * @param log     the log
 * @param network the network
 * @param modelNo the model no
 */
public static void printModel(@Nonnull final NotebookOutput log, @Nonnull final Layer network, final int modelNo) {
    log.out("Learned Model Statistics: ");
    log.code(() -> {
        @Nonnull final ScalarStatistics scalarStatistics = new ScalarStatistics();
        network.state().stream().flatMapToDouble(x -> Arrays.stream(x)).forEach(v -> scalarStatistics.add(v));
        return scalarStatistics.getMetrics();
    });
    @Nonnull final String modelName = "model" + modelNo + ".json";
    log.p("Saved model as " + log.file(network.getJson().toString(), modelName, modelName));
}