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

Example 21 with Tensor

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

the class BinaryNoiseLayer method eval.

@Override
public Result eval(@Nonnull final Result... inObj) {
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    final Result input = inObj[0];
    if (!enabled)
        return input;
    @Nonnull final int[] dimensions = input.getData().getDimensions();
    if (maskList.size() > 1 && !Arrays.equals(maskList.get(0).getDimensions(), dimensions)) {
        maskList.clear();
    }
    final int length = input.getData().length();
    @Nonnull final Tensor tensorPrototype = new Tensor(dimensions);
    while (length > maskList.size()) {
        maskList.add(tensorPrototype.map(v -> FastRandom.INSTANCE.random() < getValue() ? 0 : (1.0 / getValue())));
    }
    @Nonnull final TensorList mask = TensorArray.create(maskList.stream().limit(length).toArray(i -> new Tensor[i]));
    return new Result(mask, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        data.addRef();
        input.accumulate(buffer, data);
    }) {

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

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

Example 22 with Tensor

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

the class CrossDotMetaLayer method eval.

@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
    final Result input = inObj[0];
    final TensorList indata = input.getData();
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    indata.addRef();
    final int itemCnt = indata.length();
    final int dim = Tensor.length(indata.getDimensions());
    @Nonnull final Tensor results = new Tensor(dim, dim);
    for (int i = 0; i < dim; i++) {
        for (int j = 0; j < dim; j++) {
            if (i == j) {
                continue;
            }
            double v = 0;
            for (int k = 0; k < itemCnt; k++) {
                Tensor tensor = indata.get(k);
                @Nullable final double[] kk = tensor.getData();
                v += kk[i] * kk[j];
                tensor.freeRef();
            }
            results.set(new int[] { i, j }, v);
        }
    }
    return new Result(TensorArray.wrap(results), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (input.isAlive()) {
            @Nullable final Tensor deltaTensor = delta.get(0);
            @Nonnull final Tensor[] feedback = new Tensor[itemCnt];
            Arrays.parallelSetAll(feedback, i -> new Tensor(dim));
            for (int i = 0; i < dim; i++) {
                for (int j = 0; j < dim; j++) {
                    if (i == j) {
                        continue;
                    }
                    final double v = deltaTensor.get(i, j);
                    for (int k = 0; k < itemCnt; k++) {
                        Tensor tensor = indata.get(k);
                        @Nullable final double[] kk = tensor.getData();
                        feedback[k].add(i, v * kk[j]);
                        feedback[k].add(j, v * kk[i]);
                        tensor.freeRef();
                    }
                }
            }
            deltaTensor.freeRef();
            @Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 23 with Tensor

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

the class DropoutNoiseLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result inputResult = inObj[0];
    inputResult.addRef();
    final TensorList inputData = inputResult.getData();
    final int itemCnt = inputData.length();
    final Tensor[] mask = IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nonnull final Random random = new Random(seed);
        @Nullable final Tensor input = inputData.get(dataIndex);
        @Nullable final Tensor output = input.map(x -> {
            if (seed == -1)
                return 1;
            return random.nextDouble() < getValue() ? 0 : (1.0 / getValue());
        });
        input.freeRef();
        return output;
    }).toArray(i -> new Tensor[i]);
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        Tensor inputTensor = inputData.get(dataIndex);
        @Nullable final double[] input = inputTensor.getData();
        @Nullable final double[] maskT = mask[dataIndex].getData();
        @Nonnull final Tensor output = new Tensor(inputTensor.getDimensions());
        @Nullable final double[] outputData = output.getData();
        for (int i = 0; i < outputData.length; i++) {
            outputData[i] = input[i] * maskT[i];
        }
        inputTensor.freeRef();
        return output;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (inputResult.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                Tensor deltaTensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = deltaTensor.getData();
                @Nullable final double[] maskData = mask[dataIndex].getData();
                @Nonnull final Tensor passback = new Tensor(deltaTensor.getDimensions());
                for (int i = 0; i < passback.length(); i++) {
                    passback.set(i, maskData[i] * deltaData[i]);
                }
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            inputResult.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 24 with Tensor

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

the class ImgPixelGateLayer method eval.

/**
 * Eval nn result.
 *
 * @param input the input
 * @param gate  the gate
 * @return the nn result
 */
@Nonnull
public Result eval(@Nonnull final Result input, @Nonnull final Result gate) {
    final TensorList inputData = input.getData();
    final TensorList gateData = gate.getData();
    inputData.addRef();
    input.addRef();
    gate.addRef();
    gateData.addRef();
    int[] inputDims = inputData.getDimensions();
    assert 3 == inputDims.length;
    return new Result(TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(i -> {
        Tensor inputTensor = inputData.get(i);
        Tensor gateTensor = gateData.get(i);
        Tensor result = new Tensor(inputDims[0], inputDims[1], 1).setByCoord(c -> {
            return IntStream.range(0, inputDims[2]).mapToDouble(b -> {
                int[] coords = c.getCoords();
                return inputTensor.get(coords[0], coords[1], b) * gateTensor.get(coords[0], coords[1], 0);
            }).sum();
        });
        inputTensor.freeRef();
        gateTensor.freeRef();
        return result;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (input.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(i -> {
                Tensor deltaTensor = delta.get(i);
                Tensor gateTensor = gateData.get(i);
                Tensor result = new Tensor(input.getData().getDimensions()).setByCoord(c -> {
                    int[] coords = c.getCoords();
                    return deltaTensor.get(coords[0], coords[1], 0) * gateTensor.get(coords[0], coords[1], 0);
                });
                deltaTensor.freeRef();
                gateTensor.freeRef();
                return result;
            }).toArray(i -> new Tensor[i]));
            input.accumulate(buffer, tensorArray);
        }
        if (gate.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(i -> {
                Tensor deltaTensor = delta.get(i);
                Tensor inputTensor = inputData.get(i);
                Tensor result = new Tensor(gateData.getDimensions()).setByCoord(c -> IntStream.range(0, inputDims[2]).mapToDouble(b -> {
                    int[] coords = c.getCoords();
                    return deltaTensor.get(coords[0], coords[1], 0) * inputTensor.get(coords[0], coords[1], b);
                }).sum());
                deltaTensor.freeRef();
                inputTensor.freeRef();
                return result;
            }).toArray(i -> new Tensor[i]));
            gate.accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            inputData.freeRef();
            input.freeRef();
            gate.freeRef();
            gateData.freeRef();
        }

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

Example 25 with Tensor

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

the class ImgTileAssemblyLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    assert 3 == inObj[0].getData().getDimensions().length;
    int[] outputDims = getOutputDims(inObj);
    return new Result(TensorArray.wrap(IntStream.range(0, inObj[0].getData().length()).parallel().mapToObj(dataIndex -> {
        @Nonnull final Tensor outputData = new Tensor(outputDims);
        int totalWidth = 0;
        int totalHeight = 0;
        int inputIndex = 0;
        for (int row = 0; row < rows; row++) {
            int positionX = 0;
            int rowHeight = 0;
            for (int col = 0; col < columns; col++) {
                TensorList tileTensor = inObj[inputIndex].getData();
                int[] tileDimensions = tileTensor.getDimensions();
                rowHeight = Math.max(rowHeight, tileDimensions[1]);
                Tensor inputData = tileTensor.get(dataIndex);
                ImgTileAssemblyLayer.copy(inputData, outputData, positionX, totalHeight);
                inputData.freeRef();
                positionX += tileDimensions[0];
                inputIndex += 1;
            }
            totalHeight += rowHeight;
            totalWidth = Math.max(totalWidth, positionX);
        }
        return outputData;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        int totalHeight = 0;
        int inputIndex = 0;
        for (int row = 0; row < rows; row++) {
            int positionX = 0;
            int rowHeight = 0;
            for (int col = 0; col < columns; col++) {
                Result in = inObj[inputIndex];
                int[] inputDataDimensions = in.getData().getDimensions();
                rowHeight = Math.max(rowHeight, inputDataDimensions[1]);
                if (in.isAlive()) {
                    int _positionX = positionX;
                    int _totalHeight = totalHeight;
                    @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).parallel().mapToObj(dataIndex -> {
                        @Nullable final Tensor deltaTensor = delta.get(dataIndex);
                        @Nonnull final Tensor passbackTensor = new Tensor(inputDataDimensions);
                        ImgTileAssemblyLayer.copy(deltaTensor, passbackTensor, -_positionX, -_totalHeight);
                        deltaTensor.freeRef();
                        return passbackTensor;
                    }).toArray(i -> new Tensor[i]));
                    in.accumulate(buffer, tensorArray);
                }
                positionX += inputDataDimensions[0];
                inputIndex += 1;
            }
            totalHeight += rowHeight;
        }
    }) {

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

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