Examples with NeuralLayer org.dmg.pmml.neural_network.NeuralLayer used on opensource projects

Example 1 with NeuralLayer

use of org.dmg.pmml.neural_network.NeuralLayer in project shifu by ShifuML.

the class PMMLEncogNeuralNetworkModel method adaptMLModelToPMML.

/**
 * The function which converts an Encog NeuralNetwork to a PMML
 * NeuralNetwork Model.
 * <p>
 * This function reads the weights from the Encog NeuralNetwork model and assign them to the corresponding
 * connections of Neurons in PMML model.
 *
 * @param bNetwork
 *            Encog NeuralNetwork
 * @param pmmlModel
 *            DataFieldUtility that provides supplementary data field for
 *            the model conversion
 * @return The generated PMML NeuralNetwork Model
 */
public org.dmg.pmml.neural_network.NeuralNetwork adaptMLModelToPMML(org.encog.neural.networks.BasicNetwork bNetwork, org.dmg.pmml.neural_network.NeuralNetwork pmmlModel) {
    network = bNetwork.getFlat();
    pmmlModel = new NeuralNetworkModelIntegrator().adaptPMML(pmmlModel);
    int[] layerCount = network.getLayerCounts();
    int[] layerFeedCount = network.getLayerFeedCounts();
    double[] weights = network.getWeights();
    ActivationFunction[] functionList = transformActivationFunction(network.getActivationFunctions());
    int numLayers = layerCount.length;
    int weightID = 0;
    List<NeuralLayer> layerList = new ArrayList<NeuralLayer>();
    // pmmlModel.withFunctionName(MiningFunctionType.REGRESSION);
    for (int i = 0; i < numLayers - 1; i++) {
        NeuralLayer layer = new NeuralLayer();
        layer.setNumberOfNeurons(layerFeedCount[i]);
        layer.setActivationFunction(functionList[i]);
        int layerID = numLayers - i - 1;
        for (int j = 0; j < layerFeedCount[i]; j++) {
            Neuron neuron = new Neuron();
            neuron.setId(String.valueOf(layerID + "," + j));
            for (int k = 0; k < layerFeedCount[i + 1]; k++) {
                neuron.addConnections(new Connection(String.valueOf(layerID - 1 + "," + k), weights[weightID++]));
            }
            // weights
            int tmp = layerCount[i + 1] - layerFeedCount[i + 1];
            for (int k = 0; k < tmp; k++) {
                neuron.setBias(weights[weightID++]);
            }
            // bias neuron for each layer
            layer.addNeurons(neuron);
        }
        // finish build Neuron
        layerList.add(layer);
    }
    // finish build layer
    // reserve the layer list to fit fot PMML format
    Collections.reverse(layerList);
    pmmlModel.addNeuralLayers(layerList.toArray(new NeuralLayer[layerList.size()]));
    // set neural output based on target id
    pmmlModel.setNeuralOutputs(PMMLAdapterCommonUtil.getOutputFields(pmmlModel.getMiningSchema(), numLayers - 1));
    return pmmlModel;
}

Example 2 with NeuralLayer

use of org.dmg.pmml.neural_network.NeuralLayer in project jpmml-r by jpmml.

the class ElmNNConverter method encodeModel.

@Override
public NeuralNetwork encodeModel(Schema schema) {
    RGenericVector elmNN = getObject();
    RDoubleVector inpweight = elmNN.getDoubleElement("inpweight");
    RDoubleVector biashid = elmNN.getDoubleElement("biashid");
    RDoubleVector outweight = elmNN.getDoubleElement("outweight");
    RStringVector actfun = elmNN.getStringElement("actfun");
    RDoubleVector nhid = elmNN.getDoubleElement("nhid");
    Label label = schema.getLabel();
    List<? extends Feature> features = schema.getFeatures();
    switch(actfun.asScalar()) {
        case "purelin":
            break;
        default:
            throw new IllegalArgumentException();
    }
    NeuralInputs neuralInputs = NeuralNetworkUtil.createNeuralInputs(features, DataType.DOUBLE);
    List<? extends NeuralEntity> entities = neuralInputs.getNeuralInputs();
    List<NeuralLayer> neuralLayers = new ArrayList<>(2);
    NeuralLayer hiddenNeuralLayer = new NeuralLayer();
    int rows = ValueUtil.asInt(nhid.asScalar());
    int columns = 1 + features.size();
    for (int row = 0; row < rows; row++) {
        List<Double> weights = FortranMatrixUtil.getRow(inpweight.getValues(), rows, columns, row);
        Double bias = biashid.getValue(row);
        bias += weights.remove(0);
        Neuron neuron = NeuralNetworkUtil.createNeuron(entities, weights, bias).setId("hidden/" + String.valueOf(row + 1));
        hiddenNeuralLayer.addNeurons(neuron);
    }
    neuralLayers.add(hiddenNeuralLayer);
    entities = hiddenNeuralLayer.getNeurons();
    NeuralLayer outputNeuralLayer = new NeuralLayer();
    // XXX
    columns = 1;
    for (int column = 0; column < columns; column++) {
        List<Double> weights = FortranMatrixUtil.getColumn(outweight.getValues(), rows, columns, column);
        Double bias = null;
        Neuron neuron = NeuralNetworkUtil.createNeuron(entities, weights, bias).setId("output/" + String.valueOf(column + 1));
        outputNeuralLayer.addNeurons(neuron);
    }
    neuralLayers.add(outputNeuralLayer);
    entities = outputNeuralLayer.getNeurons();
    NeuralOutputs neuralOutputs = NeuralNetworkUtil.createRegressionNeuralOutputs(entities, (ContinuousLabel) label);
    NeuralNetwork neuralNetwork = new NeuralNetwork(MiningFunction.REGRESSION, NeuralNetwork.ActivationFunction.IDENTITY, ModelUtil.createMiningSchema(label), neuralInputs, neuralLayers).setNeuralOutputs(neuralOutputs);
    return neuralNetwork;
}

Example 3 with NeuralLayer

use of org.dmg.pmml.neural_network.NeuralLayer in project jpmml-r by jpmml.

the class NNetConverter method encodeNeuralLayer.

private static NeuralLayer encodeNeuralLayer(String prefix, int n, List<? extends NeuralEntity> entities, RDoubleVector wts, int offset) {
    NeuralLayer neuralLayer = new NeuralLayer();
    for (int i = 0; i < n; i++) {
        List<Double> weights = (wts.getValues()).subList(offset + 1, offset + (entities.size() + 1));
        Double bias = wts.getValue(offset);
        Neuron neuron = NeuralNetworkUtil.createNeuron(entities, weights, bias).setId(prefix + "/" + String.valueOf(i + 1));
        neuralLayer.addNeurons(neuron);
        offset += (entities.size() + 1);
    }
    return neuralLayer;
}

Example 4 with NeuralLayer

use of org.dmg.pmml.neural_network.NeuralLayer in project jpmml-sparkml by jpmml.

the class MultilayerPerceptronClassificationModelConverter method encodeModel.

@Override
public NeuralNetwork encodeModel(Schema schema) {
    MultilayerPerceptronClassificationModel model = getTransformer();
    int[] layers = model.layers();
    Vector weights = model.weights();
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    if (categoricalLabel.size() != layers[layers.length - 1]) {
        throw new IllegalArgumentException();
    }
    List<? extends Feature> features = schema.getFeatures();
    if (features.size() != layers[0]) {
        throw new IllegalArgumentException();
    }
    NeuralInputs neuralInputs = NeuralNetworkUtil.createNeuralInputs(features, DataType.DOUBLE);
    List<? extends Entity> entities = neuralInputs.getNeuralInputs();
    List<NeuralLayer> neuralLayers = new ArrayList<>();
    int weightPos = 0;
    for (int layer = 1; layer < layers.length; layer++) {
        NeuralLayer neuralLayer = new NeuralLayer();
        int rows = entities.size();
        int columns = layers[layer];
        List<List<Double>> weightMatrix = new ArrayList<>();
        for (int column = 0; column < columns; column++) {
            List<Double> weightVector = new ArrayList<>();
            for (int row = 0; row < rows; row++) {
                weightVector.add(weights.apply(weightPos + (row * columns) + column));
            }
            weightMatrix.add(weightVector);
        }
        weightPos += (rows * columns);
        for (int column = 0; column < columns; column++) {
            List<Double> weightVector = weightMatrix.get(column);
            Double bias = weights.apply(weightPos);
            Neuron neuron = NeuralNetworkUtil.createNeuron(entities, weightVector, bias).setId(String.valueOf(layer) + "/" + String.valueOf(column + 1));
            neuralLayer.addNeurons(neuron);
            weightPos++;
        }
        if (layer == (layers.length - 1)) {
            neuralLayer.setActivationFunction(NeuralNetwork.ActivationFunction.IDENTITY).setNormalizationMethod(NeuralNetwork.NormalizationMethod.SOFTMAX);
        }
        neuralLayers.add(neuralLayer);
        entities = neuralLayer.getNeurons();
    }
    if (weightPos != weights.size()) {
        throw new IllegalArgumentException();
    }
    NeuralNetwork neuralNetwork = new NeuralNetwork(MiningFunction.CLASSIFICATION, NeuralNetwork.ActivationFunction.LOGISTIC, ModelUtil.createMiningSchema(categoricalLabel), neuralInputs, neuralLayers).setNeuralOutputs(NeuralNetworkUtil.createClassificationNeuralOutputs(entities, categoricalLabel));
    return neuralNetwork;
}

Example 5 with NeuralLayer

use of org.dmg.pmml.neural_network.NeuralLayer in project jpmml-r by jpmml.

the class NNConverter method encodeModel.

@Override
public Model encodeModel(Schema schema) {
    RGenericVector nn = getObject();
    RExp actFct = nn.getElement("act.fct");
    RBooleanVector linearOutput = nn.getBooleanElement("linear.output");
    RGenericVector weights = nn.getGenericElement("weights");
    RStringVector actFctType = actFct.getStringAttribute("type");
    // Select the first repetition
    weights = weights.getGenericValue(0);
    NeuralNetwork.ActivationFunction activationFunction = NeuralNetwork.ActivationFunction.LOGISTIC;
    switch(actFctType.asScalar()) {
        case "logistic":
            activationFunction = NeuralNetwork.ActivationFunction.LOGISTIC;
            break;
        case "tanh":
            activationFunction = NeuralNetwork.ActivationFunction.TANH;
            break;
        default:
            throw new IllegalArgumentException();
    }
    ContinuousLabel continuousLabel = (ContinuousLabel) schema.getLabel();
    List<? extends Feature> features = schema.getFeatures();
    NeuralInputs neuralInputs = NeuralNetworkUtil.createNeuralInputs(features, DataType.DOUBLE);
    List<NeuralLayer> neuralLayers = new ArrayList<>();
    List<? extends NeuralEntity> entities = neuralInputs.getNeuralInputs();
    for (int i = 0; i < weights.size(); i++) {
        boolean hidden = (i < (weights.size() - 1));
        NeuralLayer neuralLayer = new NeuralLayer();
        if (hidden || (linearOutput != null && !linearOutput.asScalar())) {
            neuralLayer.setActivationFunction(activationFunction);
        }
        RDoubleVector layerWeights = weights.getDoubleValue(i);
        RIntegerVector layerDim = layerWeights.dim();
        int layerRows = layerDim.getValue(0);
        int layerColumns = layerDim.getValue(1);
        for (int j = 0; j < layerColumns; j++) {
            List<Double> neuronWeights = FortranMatrixUtil.getColumn(layerWeights.getValues(), layerRows, layerColumns, j);
            String id;
            if (hidden) {
                id = "hidden/" + String.valueOf(i) + "/" + String.valueOf(j);
            } else {
                id = "output/" + String.valueOf(j);
            }
            Neuron neuron = NeuralNetworkUtil.createNeuron(entities, neuronWeights.subList(1, neuronWeights.size()), neuronWeights.get(0)).setId(id);
            neuralLayer.addNeurons(neuron);
        }
        neuralLayers.add(neuralLayer);
        entities = neuralLayer.getNeurons();
    }
    NeuralNetwork neuralNetwork = new NeuralNetwork(MiningFunction.REGRESSION, NeuralNetwork.ActivationFunction.IDENTITY, ModelUtil.createMiningSchema(continuousLabel), neuralInputs, neuralLayers).setNeuralOutputs(NeuralNetworkUtil.createRegressionNeuralOutputs(entities, continuousLabel));
    return neuralNetwork;
}