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

Example 16 with Layer

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

the class AvgMetaLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result input = inObj[0];
    input.addRef();
    TensorList inputData = input.getData();
    final int itemCnt = inputData.length();
    @Nullable Tensor thisResult;
    boolean passback;
    if (null == lastResult || inputData.length() > minBatchCount) {
        @Nonnull final ToDoubleFunction<Coordinate> f = (c) -> IntStream.range(0, itemCnt).mapToDouble(dataIndex -> {
            Tensor tensor = inputData.get(dataIndex);
            double v = tensor.get(c);
            tensor.freeRef();
            return v;
        }).sum() / itemCnt;
        Tensor tensor = inputData.get(0);
        thisResult = tensor.mapCoords(f);
        tensor.freeRef();
        passback = true;
        if (null != lastResult)
            lastResult.freeRef();
        lastResult = thisResult;
        lastResult.addRef();
    } else {
        passback = false;
        thisResult = lastResult;
        thisResult.freeRef();
    }
    return new Result(TensorArray.create(thisResult), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (passback && input.isAlive()) {
            @Nullable final Tensor delta = data.get(0);
            @Nonnull final Tensor[] feedback = new Tensor[itemCnt];
            Arrays.parallelSetAll(feedback, i -> new Tensor(delta.getDimensions()));
            thisResult.coordStream(true).forEach((inputCoord) -> {
                for (int inputItem = 0; inputItem < itemCnt; inputItem++) {
                    feedback[inputItem].add(inputCoord, delta.get(inputCoord) / itemCnt);
                }
            });
            delta.freeRef();
            @Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 17 with Layer

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

the class BiasMetaLayer method eval.

@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
    final int itemCnt = inObj[0].getData().length();
    Tensor tensor1 = inObj[1].getData().get(0);
    final Tensor[] tensors = IntStream.range(0, itemCnt).parallel().mapToObj(dataIndex -> {
        Tensor tensor = inObj[0].getData().get(dataIndex);
        Tensor mapIndex = tensor.mapIndex((v, c) -> {
            return v + tensor1.get(c);
        });
        tensor.freeRef();
        return mapIndex;
    }).toArray(i -> new Tensor[i]);
    tensor1.freeRef();
    Tensor tensor0 = tensors[0];
    tensor0.addRef();
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    return new Result(TensorArray.wrap(tensors), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (inObj[0].isAlive()) {
            data.addRef();
            inObj[0].accumulate(buffer, data);
        }
        if (inObj[1].isAlive()) {
            @Nonnull final ToDoubleFunction<Coordinate> f = (c) -> {
                return IntStream.range(0, itemCnt).mapToDouble(i -> {
                    Tensor tensor = data.get(i);
                    double v = tensor.get(c);
                    tensor.freeRef();
                    return v;
                }).sum();
            };
            @Nullable final Tensor passback = tensor0.mapCoords(f);
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, inObj[1].getData().length()).mapToObj(i -> {
                if (i == 0)
                    return passback;
                else {
                    @Nullable Tensor map = passback.map(v -> 0);
                    passback.freeRef();
                    return map;
                }
            }).toArray(i -> new Tensor[i]));
            inObj[1].accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            tensor0.freeRef();
            Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
        }

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

Example 18 with Layer

use of com.simiacryptus.mindseye.lang.Layer 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 19 with Layer

use of com.simiacryptus.mindseye.lang.Layer 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 20 with Layer

use of com.simiacryptus.mindseye.lang.Layer 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();
        }
    };
}