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

Example 51 with TensorList

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

the class BatchDerivativeTester method testUnFrozen.

/**
 * Test un frozen.
 *
 * @param component      the component
 * @param inputPrototype the input prototype
 */
public void testUnFrozen(@Nonnull final Layer component, final Tensor[] inputPrototype) {
    @Nonnull final AtomicBoolean reachedInputFeedback = new AtomicBoolean(false);
    @Nonnull final Layer frozen = component.copy().setFrozen(false);
    @Nullable final Result eval = frozen.eval(new Result(TensorArray.create(inputPrototype), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        reachedInputFeedback.set(true);
    }) {

        @Override
        public boolean isAlive() {
            return true;
        }
    });
    @Nonnull final DeltaSet<Layer> buffer = new DeltaSet<Layer>();
    TensorList data = eval.getData();
    eval.accumulate(buffer, data);
    @Nullable final List<double[]> stateList = frozen.state();
    final List<Delta<Layer>> deltas = stateList.stream().map(doubles -> {
        return buffer.stream().filter(x -> x.target == doubles).findFirst().orElse(null);
    }).filter(x -> x != null).collect(Collectors.toList());
    if (deltas.isEmpty() && !stateList.isEmpty()) {
        throw new AssertionError("Nonfrozen component not listed in delta. Deltas: " + deltas);
    }
    if (!reachedInputFeedback.get()) {
        throw new RuntimeException("Nonfrozen component did not pass input backwards");
    }
}

Example 52 with TensorList

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

the class SumMetaLayer method eval.

@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
    final Result input = inObj[0];
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    final int itemCnt = input.getData().length();
    if (null == lastResult || minBatches < itemCnt) {
        @Nonnull final ToDoubleFunction<Coordinate> f = (c) -> IntStream.range(0, itemCnt).mapToDouble(dataIndex -> input.getData().get(dataIndex).get(c)).sum();
        lastResult = input.getData().get(0).mapCoords(f);
    }
    return new Result(TensorArray.wrap(lastResult), (@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()));
            @Nonnull final ToDoubleFunction<Coordinate> f = (inputCoord) -> {
                for (int inputItem = 0; inputItem < itemCnt; inputItem++) {
                    feedback[inputItem].add(inputCoord, delta.get(inputCoord));
                }
                return 0;
            };
            delta.mapCoords(f);
            @Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 53 with TensorList

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

the class ProductInputsLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    assert inObj.length > 1;
    Arrays.stream(inObj).forEach(x -> x.getData().addRef());
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    for (int i = 1; i < inObj.length; i++) {
        final int dim0 = Tensor.length(inObj[0].getData().getDimensions());
        final int dimI = Tensor.length(inObj[i].getData().getDimensions());
        if (dim0 != 1 && dimI != 1 && dim0 != dimI) {
            throw new IllegalArgumentException(Arrays.toString(inObj[0].getData().getDimensions()) + " != " + Arrays.toString(inObj[i].getData().getDimensions()));
        }
    }
    return new Result(Arrays.stream(inObj).parallel().map(x -> {
        TensorList data = x.getData();
        data.addRef();
        return data;
    }).reduce((l, r) -> {
        TensorArray productArray = TensorArray.wrap(IntStream.range(0, Math.max(l.length(), r.length())).parallel().mapToObj(i1 -> {
            @Nullable final Tensor left = l.get(1 == l.length() ? 0 : i1);
            @Nullable final Tensor right = r.get(1 == r.length() ? 0 : i1);
            Tensor product = Tensor.product(left, right);
            left.freeRef();
            right.freeRef();
            return product;
        }).toArray(i -> new Tensor[i]));
        l.freeRef();
        r.freeRef();
        return productArray;
    }).get(), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        for (@Nonnull final Result input : inObj) {
            if (input.isAlive()) {
                @Nonnull TensorList passback = Arrays.stream(inObj).parallel().map(x -> {
                    TensorList tensorList = x == input ? delta : x.getData();
                    tensorList.addRef();
                    return tensorList;
                }).reduce((l, r) -> {
                    TensorArray productList = TensorArray.wrap(IntStream.range(0, Math.max(l.length(), r.length())).parallel().mapToObj(j -> {
                        @Nullable final Tensor left = l.get(1 == l.length() ? 0 : j);
                        @Nullable final Tensor right = r.get(1 == r.length() ? 0 : j);
                        Tensor product = Tensor.product(left, right);
                        left.freeRef();
                        right.freeRef();
                        return product;
                    }).toArray(j -> new Tensor[j]));
                    l.freeRef();
                    r.freeRef();
                    return productList;
                }).get();
                final TensorList inputData = input.getData();
                if (1 == inputData.length() && 1 < passback.length()) {
                    TensorArray newValue = TensorArray.wrap(passback.stream().reduce((a, b) -> {
                        @Nullable Tensor c = a.addAndFree(b);
                        b.freeRef();
                        return c;
                    }).get());
                    passback.freeRef();
                    passback = newValue;
                }
                if (1 == Tensor.length(inputData.getDimensions()) && 1 < Tensor.length(passback.getDimensions())) {
                    TensorArray newValue = TensorArray.wrap(passback.stream().map((a) -> {
                        @Nonnull Tensor b = new Tensor(a.sum());
                        a.freeRef();
                        return b;
                    }).toArray(i -> new Tensor[i]));
                    passback.freeRef();
                    passback = newValue;
                }
                input.accumulate(buffer, passback);
            }
        }
    }) {

        @Override
        public boolean isAlive() {
            for (@Nonnull final Result element : inObj) if (element.isAlive()) {
                return true;
            }
            return false;
        }

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

Example 54 with TensorList

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

the class ReLuActivationLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result input = inObj[0];
    final TensorList indata = input.getData();
    input.addRef();
    indata.addRef();
    weights.addRef();
    final int itemCnt = indata.length();
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).parallel().mapToObj(dataIndex -> {
        @Nullable Tensor tensorElement = indata.get(dataIndex);
        @Nonnull final Tensor tensor = tensorElement.multiply(weights.get(0));
        tensorElement.freeRef();
        @Nullable final double[] outputData = tensor.getData();
        for (int i = 0; i < outputData.length; i++) {
            if (outputData[i] < 0) {
                outputData[i] = 0;
            }
        }
        return tensor;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (!isFrozen()) {
            IntStream.range(0, delta.length()).parallel().forEach(dataIndex -> {
                @Nullable Tensor deltaTensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = deltaTensor.getData();
                @Nullable Tensor inputTensor = indata.get(dataIndex);
                @Nullable final double[] inputData = inputTensor.getData();
                @Nonnull final Tensor weightDelta = new Tensor(weights.getDimensions());
                @Nullable final double[] weightDeltaData = weightDelta.getData();
                for (int i = 0; i < deltaData.length; i++) {
                    weightDeltaData[0] += inputData[i] < 0 ? 0 : deltaData[i] * inputData[i];
                }
                buffer.get(ReLuActivationLayer.this, weights.getData()).addInPlace(weightDeltaData).freeRef();
                deltaTensor.freeRef();
                inputTensor.freeRef();
                weightDelta.freeRef();
            });
        }
        if (input.isAlive()) {
            final double weight = weights.getData()[0];
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).parallel().mapToObj(dataIndex -> {
                @Nullable Tensor deltaTensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = deltaTensor.getData();
                @Nullable Tensor inTensor = indata.get(dataIndex);
                @Nullable final double[] inputData = inTensor.getData();
                @Nonnull final int[] dims = inTensor.getDimensions();
                @Nonnull final Tensor passback = new Tensor(dims);
                for (int i = 0; i < passback.length(); i++) {
                    passback.set(i, inputData[i] < 0 ? 0 : deltaData[i] * weight);
                }
                inTensor.freeRef();
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 55 with TensorList

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

the class RescaledSubnetLayer method eval.

@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
    assert 1 == inObj.length;
    final TensorList batch = inObj[0].getData();
    @Nonnull final int[] inputDims = batch.getDimensions();
    assert 3 == inputDims.length;
    if (1 == scale)
        return subnetwork.eval(inObj);
    @Nonnull final PipelineNetwork network = new PipelineNetwork();
    @Nullable final DAGNode condensed = network.wrap(new ImgReshapeLayer(scale, scale, false));
    network.wrap(new ImgConcatLayer(), IntStream.range(0, scale * scale).mapToObj(subband -> {
        @Nonnull final int[] select = new int[inputDims[2]];
        for (int i = 0; i < inputDims[2]; i++) {
            select[i] = subband * inputDims[2] + i;
        }
        return network.add(subnetwork, network.wrap(new ImgBandSelectLayer(select), condensed));
    }).toArray(i -> new DAGNode[i]));
    network.wrap(new ImgReshapeLayer(scale, scale, true));
    Result eval = network.eval(inObj);
    network.freeRef();
    return eval;
}