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

Example 86 with DeltaSet

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

the class StaticScalarLossLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    if (1 != inObj.length)
        throw new IllegalArgumentException();
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    // if (inObj[0].getData().length() != 1) throw new IllegalArgumentException();
    final Result in0 = inObj[0];
    TensorList indata = in0.getData();
    indata.addRef();
    return new Result(TensorArray.wrap(IntStream.range(0, indata.length()).parallel().mapToObj(dataIndex -> {
        @Nullable final Tensor a = indata.get(dataIndex);
        final double diff = Math.abs(a.get(0) - getTarget());
        a.freeRef();
        return new Tensor(new double[] { diff }, 1);
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (in0.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, data.length()).parallel().mapToObj(dataIndex -> {
                @Nullable final Tensor a = indata.get(dataIndex);
                Tensor tensor = data.get(dataIndex);
                final double deriv = tensor.get(0) * (a.get(0) - getTarget() < 0 ? -1 : 1);
                tensor.freeRef();
                a.freeRef();
                return new Tensor(new double[] { deriv }, 1);
            }).toArray(i -> new Tensor[i]));
            in0.accumulate(buffer, tensorArray);
        }
    }) {

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

        @Override
        public boolean isAlive() {
            return in0.isAlive();
        }
    };
}

Example 87 with DeltaSet

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

the class LayerRateDiagnosticTrainer method run.

/**
 * Run map.
 *
 * @return the map
 */
@Nonnull
public Map<Layer, LayerStats> run() {
    final long timeoutMs = System.currentTimeMillis() + timeout.toMillis();
    PointSample measure = measure();
    @Nonnull final ArrayList<Layer> layers = new ArrayList<>(measure.weights.getMap().keySet());
    while (timeoutMs > System.currentTimeMillis() && measure.sum > terminateThreshold) {
        if (currentIteration.get() > maxIterations) {
            break;
        }
        final PointSample initialPhasePoint = measure();
        measure = initialPhasePoint;
        for (int subiteration = 0; subiteration < iterationsPerSample; subiteration++) {
            if (currentIteration.incrementAndGet() > maxIterations) {
                break;
            }
            {
                @Nonnull final SimpleLineSearchCursor orient = (SimpleLineSearchCursor) getOrientation().orient(subject, measure, monitor);
                final double stepSize = 1e-12 * orient.origin.sum;
                @Nonnull final DeltaSet<Layer> pointB = orient.step(stepSize, monitor).point.delta.copy();
                @Nonnull final DeltaSet<Layer> pointA = orient.step(0.0, monitor).point.delta.copy();
                @Nonnull final DeltaSet<Layer> d1 = pointA;
                @Nonnull final DeltaSet<Layer> d2 = d1.add(pointB.scale(-1)).scale(1.0 / stepSize);
                @Nonnull final Map<Layer, Double> steps = new HashMap<>();
                final double overallStepEstimate = d1.getMagnitude() / d2.getMagnitude();
                for (final Layer layer : layers) {
                    final DoubleBuffer<Layer> a = d2.get(layer, (double[]) null);
                    final DoubleBuffer<Layer> b = d1.get(layer, (double[]) null);
                    final double bmag = Math.sqrt(b.deltaStatistics().sumSq());
                    final double amag = Math.sqrt(a.deltaStatistics().sumSq());
                    final double dot = a.dot(b) / (amag * bmag);
                    final double idealSize = bmag / (amag * dot);
                    steps.put(layer, idealSize);
                    monitor.log(String.format("Layers stats: %s (%s, %s, %s) => %s", layer, amag, bmag, dot, idealSize));
                }
                monitor.log(String.format("Estimated ideal rates for layers: %s (%s overall; probed at %s)", steps, overallStepEstimate, stepSize));
            }
            @Nullable SimpleLineSearchCursor bestOrient = null;
            @Nullable PointSample bestPoint = null;
            layerLoop: for (@Nonnull final Layer layer : layers) {
                @Nonnull SimpleLineSearchCursor orient = (SimpleLineSearchCursor) getOrientation().orient(subject, measure, monitor);
                @Nonnull final DeltaSet<Layer> direction = filterDirection(orient.direction, layer);
                if (direction.getMagnitude() == 0) {
                    monitor.log(String.format("Zero derivative for layer %s; skipping", layer));
                    continue layerLoop;
                }
                orient = new SimpleLineSearchCursor(orient.subject, orient.origin, direction);
                final PointSample previous = measure;
                measure = getLineSearchStrategy().step(orient, monitor);
                if (isStrict()) {
                    monitor.log(String.format("Iteration %s reverting. Error: %s", currentIteration.get(), measure.sum));
                    monitor.log(String.format("Optimal rate for layer %s: %s", layer.getName(), measure.getRate()));
                    if (null == bestPoint || bestPoint.sum < measure.sum) {
                        bestOrient = orient;
                        bestPoint = measure;
                    }
                    getLayerRates().put(layer, new LayerStats(measure.getRate(), initialPhasePoint.sum - measure.sum));
                    orient.step(0, monitor);
                    measure = previous;
                } else if (previous.sum == measure.sum) {
                    monitor.log(String.format("Iteration %s failed. Error: %s", currentIteration.get(), measure.sum));
                } else {
                    monitor.log(String.format("Iteration %s complete. Error: %s", currentIteration.get(), measure.sum));
                    monitor.log(String.format("Optimal rate for layer %s: %s", layer.getName(), measure.getRate()));
                    getLayerRates().put(layer, new LayerStats(measure.getRate(), initialPhasePoint.sum - measure.sum));
                }
            }
            monitor.log(String.format("Ideal rates: %s", getLayerRates()));
            if (null != bestPoint) {
                bestOrient.step(bestPoint.rate, monitor);
            }
            monitor.onStepComplete(new Step(measure, currentIteration.get()));
        }
    }
    return getLayerRates();
}

Example 88 with DeltaSet

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

the class NthPowerActivationLayer method eval.

@Override
public Result eval(@Nonnull final Result... inObj) {
    final int itemCnt = inObj[0].getData().length();
    assert 0 < itemCnt;
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    @Nonnull final Tensor[] inputGradientA = new Tensor[itemCnt];
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).parallel().mapToObj(dataIndex -> {
        @Nullable final Tensor input = inObj[0].getData().get(dataIndex);
        @Nonnull final Tensor output = new Tensor(inObj[0].getData().getDimensions());
        @Nonnull final Tensor gradient = new Tensor(input.length());
        @Nullable final double[] inputData = input.getData();
        @Nullable final double[] gradientData = gradient.getData();
        @Nullable final double[] outputData = output.getData();
        inputGradientA[dataIndex] = gradient;
        if (power == 2) {
            NthPowerActivationLayer.square(input, inputData, gradientData, outputData);
        } else if (power == 0.5) {
            NthPowerActivationLayer.squareRoot(input, inputData, gradientData, outputData);
        } else if (power == 0.0) {
            NthPowerActivationLayer.unity(input, inputData, gradientData, outputData);
        } else {
            NthPowerActivationLayer.nthPower(power, input, inputData, gradientData, outputData);
        }
        input.freeRef();
        return output;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (inObj[0].isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, itemCnt).parallel().mapToObj(dataIndex -> {
                @Nonnull final Tensor passback = new Tensor(data.getDimensions());
                @Nullable final Tensor tensor = data.get(dataIndex);
                @Nullable double[] tensorData = tensor.getData();
                @Nullable final double[] gradientData = inputGradientA[dataIndex].getData();
                IntStream.range(0, passback.length()).forEach(i -> {
                    final double v = gradientData[i];
                    if (Double.isFinite(v)) {
                        passback.set(i, tensorData[i] * v);
                    }
                });
                tensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            Arrays.stream(inObj).forEach(ReferenceCounting::freeRef);
            Arrays.stream(inputGradientA).forEach(ReferenceCounting::freeRef);
        }

        @Override
        public boolean isAlive() {
            return 0.0 != power && inObj[0].isAlive();
        }
    };
}

Example 89 with DeltaSet

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

the class ProductLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    Arrays.stream(inObj).forEach(x -> x.getData().addRef());
    final Result in0 = inObj[0];
    @Nonnull final double[] sum_A = new double[in0.getData().length()];
    final Tensor[] outputA = IntStream.range(0, in0.getData().length()).mapToObj(dataIndex -> {
        double sum = 1;
        for (@Nonnull final Result element : inObj) {
            Tensor tensor = element.getData().get(dataIndex);
            @Nullable final double[] input = tensor.getData();
            for (final double element2 : input) {
                sum *= element2;
            }
            tensor.freeRef();
        }
        sum_A[dataIndex] = sum;
        return new Tensor(new double[] { sum }, 1);
    }).toArray(i -> new Tensor[i]);
    return new Result(TensorArray.wrap(outputA), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        for (@Nonnull final Result in_l : inObj) {
            if (in_l.isAlive()) {
                @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                    Tensor dataTensor = delta.get(dataIndex);
                    Tensor lTensor = in_l.getData().get(dataIndex);
                    @Nonnull final Tensor passback = new Tensor(lTensor.getDimensions());
                    for (int i = 0; i < lTensor.length(); i++) {
                        passback.set(i, dataTensor.get(0) * sum_A[dataIndex] / lTensor.getData()[i]);
                    }
                    dataTensor.freeRef();
                    lTensor.freeRef();
                    return passback;
                }).toArray(i -> new Tensor[i]));
                in_l.accumulate(buffer, tensorArray);
            }
        }
    }) {

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

        @Override
        public boolean isAlive() {
            for (@Nonnull final Result element : inObj) if (element.isAlive()) {
                return true;
            }
            return false;
        }
    };
}

Example 90 with DeltaSet

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

the class ReshapeLayer method evalAndFree.

@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
    assert 1 == inObj.length;
    TensorList data = inObj[0].getData();
    @Nonnull int[] inputDims = data.getDimensions();
    ReshapedTensorList reshapedTensorList = new ReshapedTensorList(data, outputDims);
    data.freeRef();
    return new Result(reshapedTensorList, (DeltaSet<Layer> buffer, TensorList delta) -> {
        @Nonnull ReshapedTensorList tensorList = new ReshapedTensorList(delta, inputDims);
        inObj[0].accumulate(buffer, tensorList);
    }) {

        @Override
        protected void _free() {
            for (@Nonnull Result result : inObj) {
                result.freeRef();
            }
        }

        @Override
        public boolean isAlive() {
            return inObj[0].isAlive();
        }
    };
}