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

Example 6 with Tensor

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

the class MaxPoolingLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    final Result in = inObj[0];
    in.getData().length();
    @Nonnull final int[] inputDims = in.getData().getDimensions();
    final List<Tuple2<Integer, int[]>> regions = MaxPoolingLayer.calcRegionsCache.apply(new MaxPoolingLayer.CalcRegionsParameter(inputDims, kernelDims));
    final Tensor[] outputA = IntStream.range(0, in.getData().length()).mapToObj(dataIndex -> {
        final int[] newDims = IntStream.range(0, inputDims.length).map(i -> {
            return (int) Math.ceil(inputDims[i] * 1.0 / kernelDims[i]);
        }).toArray();
        @Nonnull final Tensor output = new Tensor(newDims);
        return output;
    }).toArray(i -> new Tensor[i]);
    Arrays.stream(outputA).mapToInt(x -> x.length()).sum();
    @Nonnull final int[][] gradientMapA = new int[in.getData().length()][];
    IntStream.range(0, in.getData().length()).forEach(dataIndex -> {
        @Nullable final Tensor input = in.getData().get(dataIndex);
        final Tensor output = outputA[dataIndex];
        @Nonnull final IntToDoubleFunction keyExtractor = inputCoords -> input.get(inputCoords);
        @Nonnull final int[] gradientMap = new int[input.length()];
        regions.parallelStream().forEach(tuple -> {
            final Integer from = tuple.getFirst();
            final int[] toList = tuple.getSecond();
            int toMax = -1;
            double bestValue = Double.NEGATIVE_INFINITY;
            for (final int c : toList) {
                final double value = keyExtractor.applyAsDouble(c);
                if (-1 == toMax || bestValue < value) {
                    bestValue = value;
                    toMax = c;
                }
            }
            gradientMap[from] = toMax;
            output.set(from, input.get(toMax));
        });
        input.freeRef();
        gradientMapA[dataIndex] = gradientMap;
    });
    return new Result(TensorArray.wrap(outputA), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (in.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, in.getData().length()).parallel().mapToObj(dataIndex -> {
                @Nonnull final Tensor backSignal = new Tensor(inputDims);
                final int[] ints = gradientMapA[dataIndex];
                @Nullable final Tensor datum = data.get(dataIndex);
                for (int i = 0; i < datum.length(); i++) {
                    backSignal.add(ints[i], datum.get(i));
                }
                datum.freeRef();
                return backSignal;
            }).toArray(i -> new Tensor[i]));
            in.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 7 with Tensor

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

the class MaxMetaLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result input = inObj[0];
    input.addRef();
    final int itemCnt = input.getData().length();
    final Tensor input0Tensor = input.getData().get(0);
    final int vectorSize = input0Tensor.length();
    @Nonnull final int[] indicies = new int[vectorSize];
    for (int i = 0; i < vectorSize; i++) {
        final int itemNumber = i;
        indicies[i] = IntStream.range(0, itemCnt).mapToObj(x -> x).max(Comparator.comparing(dataIndex -> {
            Tensor tensor = input.getData().get(dataIndex);
            double v = tensor.getData()[itemNumber];
            tensor.freeRef();
            return v;
        })).get();
    }
    return new Result(TensorArray.wrap(input0Tensor.mapIndex((v, c) -> {
        Tensor tensor = input.getData().get(indicies[c]);
        double v1 = tensor.getData()[c];
        tensor.freeRef();
        return v1;
    })), (@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()));
            input0Tensor.coordStream(true).forEach((inputCoord) -> {
                feedback[indicies[inputCoord.getIndex()]].add(inputCoord, delta.get(inputCoord));
            });
            @Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
            input.accumulate(buffer, tensorArray);
            delta.freeRef();
        }
    }) {

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

        @Override
        protected void _free() {
            input.freeRef();
            input0Tensor.freeRef();
        }
    };
}

Example 8 with Tensor

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

the class MeanSqLossLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    if (2 != inObj.length)
        throw new IllegalArgumentException();
    final int leftLength = inObj[0].getData().length();
    final int rightLength = inObj[1].getData().length();
    Arrays.stream(inObj).forEach(ReferenceCounting::addRef);
    if (leftLength != rightLength && leftLength != 1 && rightLength != 1) {
        throw new IllegalArgumentException(leftLength + " != " + rightLength);
    }
    @Nonnull final Tensor[] diffs = new Tensor[leftLength];
    return new Result(TensorArray.wrap(IntStream.range(0, leftLength).mapToObj(dataIndex -> {
        @Nullable final Tensor a = inObj[0].getData().get(1 == leftLength ? 0 : dataIndex);
        @Nullable final Tensor b = inObj[1].getData().get(1 == rightLength ? 0 : dataIndex);
        if (a.length() != b.length()) {
            throw new IllegalArgumentException(String.format("%s != %s", Arrays.toString(a.getDimensions()), Arrays.toString(b.getDimensions())));
        }
        @Nonnull final Tensor r = a.minus(b);
        a.freeRef();
        b.freeRef();
        diffs[dataIndex] = r;
        @Nonnull Tensor statsTensor = new Tensor(new double[] { r.sumSq() / r.length() }, 1);
        return statsTensor;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (inObj[0].isAlive()) {
            Stream<Tensor> tensorStream = IntStream.range(0, data.length()).parallel().mapToObj(dataIndex -> {
                @Nullable Tensor tensor = data.get(dataIndex);
                Tensor diff = diffs[dataIndex];
                @Nullable Tensor scale = diff.scale(tensor.get(0) * 2.0 / diff.length());
                tensor.freeRef();
                return scale;
            }).collect(Collectors.toList()).stream();
            if (1 == leftLength) {
                tensorStream = Stream.of(tensorStream.reduce((a, b) -> {
                    @Nullable Tensor c = a.addAndFree(b);
                    b.freeRef();
                    return c;
                }).get());
            }
            @Nonnull final TensorList array = TensorArray.wrap(tensorStream.toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, array);
        }
        if (inObj[1].isAlive()) {
            Stream<Tensor> tensorStream = IntStream.range(0, data.length()).parallel().mapToObj(dataIndex -> {
                @Nullable Tensor tensor = data.get(dataIndex);
                @Nullable Tensor scale = diffs[dataIndex].scale(tensor.get(0) * 2.0 / diffs[dataIndex].length());
                tensor.freeRef();
                return scale;
            }).collect(Collectors.toList()).stream();
            if (1 == rightLength) {
                tensorStream = Stream.of(tensorStream.reduce((a, b) -> {
                    @Nullable Tensor c = a.addAndFree(b);
                    b.freeRef();
                    return c;
                }).get());
            }
            @Nonnull final TensorList array = TensorArray.wrap(tensorStream.map(x -> {
                @Nullable Tensor scale = x.scale(-1);
                x.freeRef();
                return scale;
            }).toArray(i -> new Tensor[i]));
            inObj[1].accumulate(buffer, array);
        }
    }) {

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

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

Example 9 with Tensor

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

the class FullyConnectedLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    final TensorList indata = inObj[0].getData();
    indata.addRef();
    for (@Nonnull Result result : inObj) {
        result.addRef();
    }
    FullyConnectedLayer.this.addRef();
    assert Tensor.length(indata.getDimensions()) == Tensor.length(this.inputDims) : Arrays.toString(indata.getDimensions()) + " == " + Arrays.toString(this.inputDims);
    @Nonnull DoubleMatrix doubleMatrix = new DoubleMatrix(Tensor.length(indata.getDimensions()), Tensor.length(outputDims), this.weights.getData());
    @Nonnull final DoubleMatrix matrixObj = FullyConnectedLayer.transpose(doubleMatrix);
    @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, indata.length()).parallel().mapToObj(dataIndex -> {
        @Nullable final Tensor input = indata.get(dataIndex);
        @Nullable final Tensor output = new Tensor(outputDims);
        matrixObj.mmuli(new DoubleMatrix(input.length(), 1, input.getData()), new DoubleMatrix(output.length(), 1, output.getData()));
        input.freeRef();
        return output;
    }).toArray(i -> new Tensor[i]));
    RecycleBin.DOUBLES.recycle(matrixObj.data, matrixObj.data.length);
    this.weights.addRef();
    return new Result(tensorArray, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (!isFrozen()) {
            final Delta<Layer> deltaBuffer = buffer.get(FullyConnectedLayer.this, this.weights.getData());
            final int threads = 4;
            IntStream.range(0, threads).parallel().mapToObj(x -> x).flatMap(thread -> {
                @Nullable Stream<Tensor> stream = IntStream.range(0, indata.length()).filter(i -> thread == i % threads).mapToObj(dataIndex -> {
                    @Nonnull final Tensor weightDelta = new Tensor(Tensor.length(inputDims), Tensor.length(outputDims));
                    Tensor deltaTensor = delta.get(dataIndex);
                    Tensor inputTensor = indata.get(dataIndex);
                    FullyConnectedLayer.crossMultiplyT(deltaTensor.getData(), inputTensor.getData(), weightDelta.getData());
                    inputTensor.freeRef();
                    deltaTensor.freeRef();
                    return weightDelta;
                });
                return stream;
            }).reduce((a, b) -> {
                @Nullable Tensor c = a.addAndFree(b);
                b.freeRef();
                return c;
            }).map(data -> {
                @Nonnull Delta<Layer> layerDelta = deltaBuffer.addInPlace(data.getData());
                data.freeRef();
                return layerDelta;
            });
            deltaBuffer.freeRef();
        }
        if (inObj[0].isAlive()) {
            @Nonnull final TensorList tensorList = TensorArray.wrap(IntStream.range(0, indata.length()).parallel().mapToObj(dataIndex -> {
                Tensor deltaTensor = delta.get(dataIndex);
                @Nonnull final Tensor passback = new Tensor(indata.getDimensions());
                FullyConnectedLayer.multiply(this.weights.getData(), deltaTensor.getData(), passback.getData());
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, tensorList);
        }
    }) {

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

        @Override
        public boolean isAlive() {
            return !isFrozen() || Arrays.stream(inObj).anyMatch(x -> x.isAlive());
        }
    };
}

Example 10 with Tensor

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

the class HyperbolicActivationLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final TensorList indata = inObj[0].getData();
    indata.addRef();
    inObj[0].addRef();
    weights.addRef();
    HyperbolicActivationLayer.this.addRef();
    final int itemCnt = indata.length();
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nullable final Tensor input = indata.get(dataIndex);
        @Nullable Tensor map = input.map(v -> {
            final int sign = v < 0 ? negativeMode : 1;
            final double a = Math.max(0, weights.get(v < 0 ? 1 : 0));
            return sign * (Math.sqrt(Math.pow(a * v, 2) + 1) - a) / a;
        });
        input.freeRef();
        return map;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (!isFrozen()) {
            IntStream.range(0, delta.length()).forEach(dataIndex -> {
                @Nullable Tensor deltaI = delta.get(dataIndex);
                @Nullable Tensor inputI = indata.get(dataIndex);
                @Nullable final double[] deltaData = deltaI.getData();
                @Nullable final double[] inputData = inputI.getData();
                @Nonnull final Tensor weightDelta = new Tensor(weights.getDimensions());
                for (int i = 0; i < deltaData.length; i++) {
                    final double d = deltaData[i];
                    final double x = inputData[i];
                    final int sign = x < 0 ? negativeMode : 1;
                    final double a = Math.max(0, weights.getData()[x < 0 ? 1 : 0]);
                    weightDelta.add(x < 0 ? 1 : 0, -sign * d / (a * a * Math.sqrt(1 + Math.pow(a * x, 2))));
                }
                deltaI.freeRef();
                inputI.freeRef();
                buffer.get(HyperbolicActivationLayer.this, weights.getData()).addInPlace(weightDelta.getData()).freeRef();
                weightDelta.freeRef();
            });
        }
        if (inObj[0].isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                @Nullable Tensor inputTensor = indata.get(dataIndex);
                Tensor deltaTensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = deltaTensor.getData();
                @Nonnull final int[] dims = indata.getDimensions();
                @Nonnull final Tensor passback = new Tensor(dims);
                for (int i = 0; i < passback.length(); i++) {
                    final double x = inputTensor.getData()[i];
                    final double d = deltaData[i];
                    final int sign = x < 0 ? negativeMode : 1;
                    final double a = Math.max(0, weights.getData()[x < 0 ? 1 : 0]);
                    passback.set(i, sign * d * a * x / Math.sqrt(1 + a * x * a * x));
                }
                deltaTensor.freeRef();
                inputTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            indata.freeRef();
            inObj[0].freeRef();
            weights.freeRef();
            HyperbolicActivationLayer.this.freeRef();
        }

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