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

Example 31 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput 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 32 with NotebookOutput

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

the class StandardLayerTests method tests.

private void tests(final NotebookOutput log, final List<ComponentTest<?>> tests, @Nonnull final Invocation invocation, @Nonnull final ArrayList<TestError> exceptions) {
    tests.stream().filter(x -> null != x).forEach((ComponentTest<?> test) -> {
        @Nonnull Layer layer = invocation.getLayer().copy();
        Tensor[] inputs = randomize(invocation.getDims());
        try {
            test.test(log, layer, inputs);
        } catch (LifecycleException e) {
            throw e;
        } catch (Throwable e) {
            exceptions.add(new TestError(e, test, layer));
        } finally {
            for (@Nonnull Tensor tensor : inputs) tensor.freeRef();
            layer.freeRef();
            test.freeRef();
            System.gc();
        }
    });
}

Example 33 with NotebookOutput

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

the class StandardLayerTests method bigTests.

/**
 * Big tests.
 *
 * @param log        the log
 * @param seed       the seed
 * @param perfLayer  the perf layer
 * @param exceptions the exceptions
 */
public void bigTests(NotebookOutput log, long seed, @Nonnull Layer perfLayer, @Nonnull ArrayList<TestError> exceptions) {
    getBigTests().stream().filter(x -> null != x).forEach(test -> {
        @Nonnull Layer layer = perfLayer.copy();
        try {
            Tensor[] input = randomize(getLargeDims(new Random(seed)));
            try {
                test.test(log, layer, input);
            } finally {
                for (@Nonnull Tensor t : input) {
                    t.freeRef();
                }
            }
        } catch (LifecycleException e) {
            throw e;
        } catch (CudaError e) {
            throw e;
        } catch (Throwable e) {
            exceptions.add(new TestError(e, test, layer));
        } finally {
            layer.freeRef();
            test.freeRef();
            System.gc();
        }
    });
}

Example 34 with NotebookOutput

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

the class TrainingTester method trainLBFGS.

/**
 * Train lbfgs list.
 *
 * @param log       the log
 * @param trainable the trainable
 * @return the list
 */
@Nonnull
public List<StepRecord> trainLBFGS(@Nonnull final NotebookOutput log, final Trainable trainable) {
    log.p("Next, we apply the same optimization using L-BFGS, which is nearly ideal for purely second-order or quadratic functions.");
    @Nonnull final List<StepRecord> history = new ArrayList<>();
    @Nonnull final TrainingMonitor monitor = TrainingTester.getMonitor(history);
    try {
        log.code(() -> {
            return new IterativeTrainer(trainable).setLineSearchFactory(label -> new ArmijoWolfeSearch()).setOrientation(new LBFGS()).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 35 with NotebookOutput

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

the class TrainingTester method testModelLearning.

/**
 * Test model learning.
 *
 * @param log            the log
 * @param component      the component
 * @param random         the randomize
 * @param inputPrototype the input prototype
 * @return the apply result
 */
public TestResult testModelLearning(@Nonnull final NotebookOutput log, @Nonnull final Layer component, final Random random, 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("");
    });
    Result[] array = ConstantResult.batchResultArray(input_target);
    Result eval = network_target.eval(array);
    Arrays.stream(array).forEach(ReferenceCounting::freeRef);
    TensorList result = eval.getData();
    eval.freeRef();
    final Tensor[] output_target = result.stream().toArray(i -> new Tensor[i]);
    result.freeRef();
    if (output_target.length != input_target.length) {
        logger.info("Batch layers not supported");
        return null;
    }
    return trainAll("Model Convergence", log, append(input_target, output_target), shuffle(random, component.copy()));
}