Examples with Tensor com.simiacryptus.mindseye.lang.Tensor used on opensource projects

Example 86 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor 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 87 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor in project MindsEye by SimiaCryptus.

the class BatchDerivativeTester method testUnFrozen.

/**
 * Test un frozen.
 *
 * @param component      the component
 * @param inputPrototype the input prototype
 */
public void testUnFrozen(@Nonnull final Layer component, final Tensor[] inputPrototype) {
    @Nonnull final AtomicBoolean reachedInputFeedback = new AtomicBoolean(false);
    @Nonnull final Layer frozen = component.copy().setFrozen(false);
    @Nullable final Result eval = frozen.eval(new Result(TensorArray.create(inputPrototype), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        reachedInputFeedback.set(true);
    }) {

        @Override
        public boolean isAlive() {
            return true;
        }
    });
    @Nonnull final DeltaSet<Layer> buffer = new DeltaSet<Layer>();
    TensorList data = eval.getData();
    eval.accumulate(buffer, data);
    @Nullable final List<double[]> stateList = frozen.state();
    final List<Delta<Layer>> deltas = stateList.stream().map(doubles -> {
        return buffer.stream().filter(x -> x.target == doubles).findFirst().orElse(null);
    }).filter(x -> x != null).collect(Collectors.toList());
    if (deltas.isEmpty() && !stateList.isEmpty()) {
        throw new AssertionError("Nonfrozen component not listed in delta. Deltas: " + deltas);
    }
    if (!reachedInputFeedback.get()) {
        throw new RuntimeException("Nonfrozen component did not pass input backwards");
    }
}

Example 88 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor in project MindsEye by SimiaCryptus.

the class BatchDerivativeTester method getLearningGradient.

@Nonnull
private Tensor getLearningGradient(@Nonnull final Layer component, final int layerNum, @Nonnull final Tensor outputPrototype, final Tensor... inputPrototype) {
    component.setFrozen(false);
    final double[] stateArray = component.state().get(layerNum);
    final int stateLen = stateArray.length;
    @Nonnull final Tensor gradient = new Tensor(stateLen, outputPrototype.length());
    for (int j = 0; j < outputPrototype.length(); j++) {
        final int j_ = j;
        @Nonnull final DeltaSet<Layer> buffer = new DeltaSet<Layer>();
        @Nonnull final Tensor data = new Tensor(outputPrototype.getDimensions()).set((k) -> k == j_ ? 1 : 0);
        @Nullable final Result eval = component.eval(ConstantResult.singleResultArray(new Tensor[][] { inputPrototype }));
        eval.getData().get(0);
        @Nonnull TensorArray tensorArray = TensorArray.wrap(data);
        eval.accumulate(buffer, tensorArray);
        final DoubleBuffer<Layer> deltaFlushBuffer = buffer.getMap().values().stream().filter(x -> x.target == stateArray).findFirst().orElse(null);
        if (null != deltaFlushBuffer) {
            for (int i = 0; i < stateLen; i++) {
                gradient.set(new int[] { i, j_ }, deltaFlushBuffer.getDelta()[i]);
            }
        }
    }
    return gradient;
}

Example 89 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor 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 90 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor in project MindsEye by SimiaCryptus.

the class SumMetaLayer method eval.

@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
    final Result input = inObj[0];
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    final int itemCnt = input.getData().length();
    if (null == lastResult || minBatches < itemCnt) {
        @Nonnull final ToDoubleFunction<Coordinate> f = (c) -> IntStream.range(0, itemCnt).mapToDouble(dataIndex -> input.getData().get(dataIndex).get(c)).sum();
        lastResult = input.getData().get(0).mapCoords(f);
    }
    return new Result(TensorArray.wrap(lastResult), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (input.isAlive()) {
            @Nullable final Tensor delta = data.get(0);
            @Nonnull final Tensor[] feedback = new Tensor[itemCnt];
            Arrays.parallelSetAll(feedback, i -> new Tensor(delta.getDimensions()));
            @Nonnull final ToDoubleFunction<Coordinate> f = (inputCoord) -> {
                for (int inputItem = 0; inputItem < itemCnt; inputItem++) {
                    feedback[inputItem].add(inputCoord, delta.get(inputCoord));
                }
                return 0;
            };
            delta.mapCoords(f);
            @Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
            input.accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
        }

        @Override
        public boolean isAlive() {
            return input.isAlive();
        }
    };
}