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

Example 21 with Layer

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

Example 22 with Layer

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

the class L1NormalizationLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... input) {
    Arrays.stream(input).forEach(nnResult -> nnResult.addRef());
    final Result in = input[0];
    final TensorList inData = in.getData();
    inData.addRef();
    return new Result(TensorArray.wrap(IntStream.range(0, inData.length()).mapToObj(dataIndex -> {
        @Nullable final Tensor value = inData.get(dataIndex);
        try {
            final double sum = value.sum();
            if (!Double.isFinite(sum) || 0 == sum) {
                value.addRef();
                return value;
            } else {
                return value.scale(1.0 / sum);
            }
        } finally {
            value.freeRef();
        }
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList outDelta) -> {
        if (in.isAlive()) {
            final Tensor[] passbackArray = IntStream.range(0, outDelta.length()).mapToObj(dataIndex -> {
                Tensor inputTensor = inData.get(dataIndex);
                @Nullable final double[] value = inputTensor.getData();
                Tensor outputTensor = outDelta.get(dataIndex);
                @Nullable final double[] delta = outputTensor.getData();
                final double dot = ArrayUtil.dot(value, delta);
                final double sum = Arrays.stream(value).sum();
                @Nonnull final Tensor passback = new Tensor(outputTensor.getDimensions());
                @Nullable final double[] passbackData = passback.getData();
                if (0 != sum || Double.isFinite(sum)) {
                    for (int i = 0; i < value.length; i++) {
                        passbackData[i] = (delta[i] - dot / sum) / sum;
                    }
                }
                outputTensor.freeRef();
                inputTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]);
            assert Arrays.stream(passbackArray).flatMapToDouble(x -> Arrays.stream(x.getData())).allMatch(v -> Double.isFinite(v));
            @Nonnull TensorArray tensorArray = TensorArray.wrap(passbackArray);
            in.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 23 with Layer

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

the class DAGNetwork method visitLayers.

/**
 * Visit layers.
 *
 * @param visitor the visitor
 */
public void visitLayers(@Nonnull final Consumer<Layer> visitor) {
    layersById.values().forEach(layer -> {
        Layer unwrapped = layer;
        while (unwrapped instanceof WrapperLayer) {
            unwrapped = ((WrapperLayer) unwrapped).getInner();
        }
        if (unwrapped instanceof DAGNetwork) {
            ((DAGNetwork) unwrapped).visitLayers(visitor);
        }
        visitor.accept(layer);
        while (layer instanceof WrapperLayer) {
            Layer inner = ((WrapperLayer) layer).getInner();
            visitor.accept(inner);
            layer = inner;
        }
    });
}

Example 24 with Layer

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

the class DAGNetwork method add.

/**
 * Add dag node.
 *
 * @param label the label
 * @param layer the layer
 * @param head  the head
 * @return the dag node
 */
public InnerNode add(@Nullable final CharSequence label, @Nonnull final Layer layer, final DAGNode... head) {
    assertAlive();
    assertConsistent();
    assert null != getInput();
    @Nonnull final InnerNode node = new InnerNode(this, layer, head);
    synchronized (layersById) {
        if (!layersById.containsKey(layer.getId())) {
            Layer replaced = layersById.put(layer.getId(), layer);
            layer.addRef();
            if (null != replaced)
                replaced.freeRef();
        }
    }
    DAGNode replaced = nodesById.put(node.getId(), node);
    if (null != replaced)
        replaced.freeRef();
    if (null != label) {
        labels.put(label, node.getId());
    }
    assertConsistent();
    return node;
}

Example 25 with Layer

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

the class LinearSumConstraintTest method train.

@Override
public void train(@Nonnull final NotebookOutput log, @Nonnull final Layer network, @Nonnull final Tensor[][] trainingData, final TrainingMonitor monitor) {
    log.code(() -> {
        @Nonnull final SimpleLossNetwork supervisedNetwork = new SimpleLossNetwork(network, new EntropyLossLayer());
        @Nonnull final Trainable trainable = new SampledArrayTrainable(trainingData, supervisedNetwork, 10000);
        @Nonnull final TrustRegionStrategy trustRegionStrategy = new TrustRegionStrategy() {

            @Override
            public TrustRegion getRegionPolicy(final Layer layer) {
                return new LinearSumConstraint();
            }
        };
        return new IterativeTrainer(trainable).setIterationsPerSample(100).setMonitor(monitor).setOrientation(trustRegionStrategy).setTimeout(3, TimeUnit.MINUTES).setMaxIterations(500).runAndFree();
    });
}