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

Example 96 with Tensor

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

the class ScaleMetaLayer method eval.

@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
    final int itemCnt = inObj[0].getData().length();
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    Arrays.stream(inObj).forEach(x -> x.getData().addRef());
    final Tensor[] tensors = IntStream.range(0, itemCnt).mapToObj(dataIndex -> inObj[0].getData().get(dataIndex).mapIndex((v, c) -> v * inObj[1].getData().get(0).get(c))).toArray(i -> new Tensor[i]);
    Tensor tensor0 = tensors[0];
    tensor0.addRef();
    return new Result(TensorArray.wrap(tensors), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (inObj[0].isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(data.stream().map(t -> {
                @Nullable Tensor t1 = inObj[1].getData().get(0);
                @Nullable Tensor tensor = t.mapIndex((v, c) -> {
                    return v * t1.get(c);
                });
                t.freeRef();
                t1.freeRef();
                return tensor;
            }).toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, tensorArray);
        }
        if (inObj[1].isAlive()) {
            @Nullable final Tensor passback = tensor0.mapIndex((v, c) -> {
                return IntStream.range(0, itemCnt).mapToDouble(i -> data.get(i).get(c) * inObj[0].getData().get(i).get(c)).sum();
            });
            tensor0.freeRef();
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, inObj[1].getData().length()).mapToObj(i -> i == 0 ? passback : passback.map(v -> 0)).toArray(i -> new Tensor[i]));
            inObj[1].accumulate(buffer, tensorArray);
        }
        Arrays.stream(inObj).forEach(x -> x.getData().addRef());
    }) {

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

        @Override
        public boolean isAlive() {
            return inObj[0].isAlive() || inObj[1].isAlive();
        }
    };
}

Example 97 with Tensor

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

the class SimpleActivationLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    final TensorList indata0 = inObj[0].getData();
    final int itemCnt = indata0.length();
    assert 0 < itemCnt;
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    Arrays.stream(inObj).forEach(nnResult -> nnResult.getData().addRef());
    @Nonnull final Tensor[] inputGradientA = new Tensor[itemCnt];
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).parallel().mapToObj(dataIndex -> {
        @Nullable final Tensor input = indata0.get(dataIndex);
        @Nonnull final Tensor output = new Tensor(indata0.getDimensions());
        @Nonnull final Tensor inputGradient = new Tensor(input.length());
        inputGradientA[dataIndex] = inputGradient;
        @Nonnull final double[] results = new double[2];
        for (int i = 0; i < input.length(); i++) {
            eval(input.getData()[i], results);
            inputGradient.set(i, results[1]);
            output.set(i, results[0]);
        }
        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 double[] gradientData = inputGradientA[dataIndex].getData();
                @Nullable Tensor tensor = data.get(dataIndex);
                IntStream.range(0, passback.length()).forEach(i -> {
                    final double v = gradientData[i];
                    if (Double.isFinite(v)) {
                        passback.set(i, tensor.get(i) * v);
                    }
                });
                tensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
            Arrays.stream(inObj).forEach(nnResult -> nnResult.getData().freeRef());
            for (@Nonnull Tensor tensor : inputGradientA) {
                tensor.freeRef();
            }
        }

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

Example 98 with Tensor

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

the class StochasticSamplingSubnetLayer method average.

/**
 * Average result.
 *
 * @param samples the samples
 * @return the result
 */
public static Result average(final Result[] samples) {
    PipelineNetwork gateNetwork = new PipelineNetwork(1);
    gateNetwork.wrap(new ProductLayer(), gateNetwork.getInput(0), gateNetwork.wrap(new ValueLayer(new Tensor(1, 1, 1).mapAndFree(v -> 1.0 / samples.length)), new DAGNode[] {}));
    SumInputsLayer sumInputsLayer = new SumInputsLayer();
    try {
        return gateNetwork.evalAndFree(sumInputsLayer.evalAndFree(samples));
    } finally {
        sumInputsLayer.freeRef();
        gateNetwork.freeRef();
    }
}

Example 99 with Tensor

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

the class SumInputsLayer 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());
    return new Result(Arrays.stream(inObj).parallel().map(x -> {
        TensorList data = x.getData();
        data.addRef();
        return data;
    }).reduce((l, r) -> {
        assert l.length() == r.length() || 1 == l.length() || 1 == r.length();
        @Nonnull TensorArray sum = TensorArray.wrap(IntStream.range(0, l.length()).parallel().mapToObj(i -> {
            @Nullable final Tensor left = l.get(1 == l.length() ? 0 : i);
            @Nullable final Tensor right = r.get(1 == r.length() ? 0 : i);
            @Nullable Tensor tensor;
            if (right.length() == 1) {
                tensor = left.mapParallel(v -> v + right.get(0));
            } else {
                tensor = left.reduceParallel(right, (v1, v2) -> v1 + v2);
            }
            left.freeRef();
            right.freeRef();
            return tensor;
        }).toArray(i -> new Tensor[i]));
        l.freeRef();
        r.freeRef();
        return sum;
    }).get(), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        for (@Nonnull final Result input : inObj) {
            if (input.isAlive()) {
                @Nonnull TensorList projectedDelta = delta;
                if (1 < projectedDelta.length() && input.getData().length() == 1) {
                    projectedDelta = TensorArray.wrap(projectedDelta.stream().parallel().reduce((a, b) -> {
                        @Nullable Tensor c = a.addAndFree(b);
                        b.freeRef();
                        return c;
                    }).get());
                } else {
                    projectedDelta.addRef();
                }
                if (1 < Tensor.length(projectedDelta.getDimensions()) && Tensor.length(input.getData().getDimensions()) == 1) {
                    Tensor[] data = projectedDelta.stream().map(t -> new Tensor(new double[] { t.sum() })).toArray(i -> new Tensor[i]);
                    @Nonnull TensorArray data2 = TensorArray.wrap(data);
                    projectedDelta.freeRef();
                    projectedDelta = data2;
                }
                input.accumulate(buffer, projectedDelta);
            }
        }
    }) {

        @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 100 with Tensor

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

the class FullyConnectedLayer method explode.

/**
 * Explode pipeline network.
 *
 * @return the pipeline network
 */
@Nonnull
public Layer explode() {
    int inputVol = Tensor.length(inputDims);
    int outVol = Tensor.length(outputDims);
    @Nonnull PipelineNetwork network = new PipelineNetwork(1);
    network.wrap(new ReshapeLayer(1, 1, inputVol));
    @Nullable Tensor tensor = this.weights.reshapeCast(1, 1, inputVol * outVol);
    @Nonnull ConvolutionLayer convolutionLayer = new ConvolutionLayer(1, 1, inputVol, outVol).set(tensor).setBatchBands(getBatchBands());
    @Nonnull ExplodedConvolutionGrid grid = convolutionLayer.getExplodedNetwork();
    convolutionLayer.freeRef();
    tensor.freeRef();
    grid.add(network.getHead());
    grid.freeRef();
    network.wrap(new ReshapeLayer(outputDims));
    network.setName(getName());
    return network;
}