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

Example 46 with TensorArray

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

the class MaxDropoutNoiseLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result in0 = inObj[0];
    final TensorList data0 = in0.getData();
    final int itemCnt = data0.length();
    in0.addRef();
    data0.addRef();
    final Tensor[] mask = IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nullable final Tensor input = data0.get(dataIndex);
        @Nullable final Tensor output = input.map(x -> 0);
        final List<List<Coordinate>> cells = getCellMap_cached.apply(new IntArray(output.getDimensions()));
        cells.forEach(cell -> {
            output.set(cell.stream().max(Comparator.comparingDouble(c -> input.get(c))).get(), 1);
        });
        input.freeRef();
        return output;
    }).toArray(i -> new Tensor[i]);
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        Tensor inputData = data0.get(dataIndex);
        @Nullable final double[] input = inputData.getData();
        @Nullable final double[] maskT = mask[dataIndex].getData();
        @Nonnull final Tensor output = new Tensor(inputData.getDimensions());
        @Nullable final double[] outputData = output.getData();
        for (int i = 0; i < outputData.length; i++) {
            outputData[i] = input[i] * maskT[i];
        }
        inputData.freeRef();
        return output;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (in0.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                Tensor deltaTensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = deltaTensor.getData();
                @Nonnull final int[] dims = data0.getDimensions();
                @Nullable final double[] maskData = mask[dataIndex].getData();
                @Nonnull final Tensor passback = new Tensor(dims);
                for (int i = 0; i < passback.length(); i++) {
                    passback.set(i, maskData[i] * deltaData[i]);
                }
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            in0.accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            in0.freeRef();
            data0.freeRef();
            Arrays.stream(mask).forEach(ReferenceCounting::freeRef);
        }

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

Example 47 with TensorArray

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

the class SoftmaxActivationLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    final int itemCnt = inObj[0].getData().length();
    @Nonnull final double[] sumA = new double[itemCnt];
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    @Nonnull final Tensor[] expA = new Tensor[itemCnt];
    final Tensor[] outputA = IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nullable final Tensor input = inObj[0].getData().get(dataIndex);
        assert 1 < input.length() : "input.length() = " + input.length();
        @Nullable final Tensor exp;
        final DoubleSummaryStatistics summaryStatistics = DoubleStream.of(input.getData()).filter(x -> Double.isFinite(x)).summaryStatistics();
        final double max = summaryStatistics.getMax();
        // final double min = summaryStatistics.getMin();
        exp = input.map(x -> {
            double xx = Math.exp(x - max);
            return Double.isFinite(xx) ? xx : 0;
        });
        input.freeRef();
        assert Arrays.stream(exp.getData()).allMatch(Double::isFinite);
        assert Arrays.stream(exp.getData()).allMatch(v -> v >= 0);
        // assert exp.sum() > 0;
        final double sum = 0 < exp.sum() ? exp.sum() : 1;
        assert Double.isFinite(sum);
        expA[dataIndex] = exp;
        sumA[dataIndex] = sum;
        @Nullable Tensor result = exp.map(x -> x / sum);
        return result;
    }).toArray(i -> new Tensor[i]);
    assert Arrays.stream(outputA).flatMapToDouble(x -> Arrays.stream(x.getData())).allMatch(v -> Double.isFinite(v));
    return new Result(TensorArray.wrap(outputA), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (inObj[0].isAlive()) {
            final Tensor[] passbackA = IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
                Tensor deltaTensor = data.get(dataIndex);
                @Nullable final double[] delta = deltaTensor.getData();
                @Nullable final double[] expdata = expA[dataIndex].getData();
                @Nonnull final Tensor passback = new Tensor(data.getDimensions());
                final int dim = expdata.length;
                double dot = 0;
                for (int i = 0; i < expdata.length; i++) {
                    dot += delta[i] * expdata[i];
                }
                final double sum = sumA[dataIndex];
                for (int i = 0; i < dim; i++) {
                    double value = 0;
                    value = (sum * delta[i] - dot) * expdata[i] / (sum * sum);
                    passback.set(i, value);
                }
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]);
            assert Arrays.stream(passbackA).flatMapToDouble(x -> Arrays.stream(x.getData())).allMatch(v -> Double.isFinite(v));
            @Nonnull TensorArray tensorArray = TensorArray.wrap(passbackA);
            inObj[0].accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 48 with TensorArray

use of com.simiacryptus.mindseye.lang.TensorArray 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 49 with TensorArray

use of com.simiacryptus.mindseye.lang.TensorArray 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 50 with TensorArray

use of com.simiacryptus.mindseye.lang.TensorArray 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;
        }
    };
}