Examples with Layer org.knime.base.data.neural.Layer used on opensource projects

Example 1 with Layer

use of org.knime.base.data.neural.Layer in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addInputLayer.

/**
 * Writes the PMML input layer of the MLP.
 *
 * @param nnModel
 *            the Neural Network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 */
protected void addInputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp) {
    Layer inputlayer = mlp.getLayer(0);
    Perceptron[] inputperceptrons = inputlayer.getPerceptrons();
    HashMap<String, Integer> inputmap = mlp.getInputMapping();
    NeuralInputs neuralInputs = nnModel.addNewNeuralInputs();
    neuralInputs.setNumberOfInputs(BigInteger.valueOf(inputperceptrons.length));
    for (int i = 0; i < inputperceptrons.length; i++) {
        NeuralInput neuralInput = neuralInputs.addNewNeuralInput();
        neuralInput.setId(0 + "," + i);
        // search corresponding input column
        String colname = "";
        for (Entry<String, Integer> e : inputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = e.getKey();
            }
        }
        DerivedField df = neuralInput.addNewDerivedField();
        df.setOptype(OPTYPE.CONTINUOUS);
        df.setDataType(DATATYPE.DOUBLE);
        FieldRef fieldRef = df.addNewFieldRef();
        fieldRef.setField(m_nameMapper.getDerivedFieldName(colname));
    }
}

Example 2 with Layer

use of org.knime.base.data.neural.Layer in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method initializeFrom.

/**
 * {@inheritDoc}
 */
@Override
public void initializeFrom(final PMMLDocument pmmlDoc) {
    m_nameMapper = new DerivedFieldMapper(pmmlDoc);
    NeuralNetwork[] models = pmmlDoc.getPMML().getNeuralNetworkArray();
    if (models.length == 0) {
        throw new IllegalArgumentException("No neural network model" + " provided.");
    } else if (models.length > 1) {
        LOGGER.warn("Multiple neural network models found. " + "Only the first model is considered.");
    }
    NeuralNetwork nnModel = models[0];
    // ------------------------------
    // initiate Neural Input
    initInputLayer(nnModel);
    // -------------------------------
    // initiate Hidden Layer
    initiateHiddenLayers(nnModel);
    // -------------------------------
    // initiate Final Layer
    initiateFinalLayer(nnModel);
    // --------------------------------
    // initiate Neural Outputs
    initiateNeuralOutputs(nnModel);
    // --------------------------------
    // initiate Neural Network properties
    ACTIVATIONFUNCTION.Enum actFunc = nnModel.getActivationFunction();
    NNNORMALIZATIONMETHOD.Enum normMethod = nnModel.getNormalizationMethod();
    if (ACTIVATIONFUNCTION.LOGISTIC != actFunc) {
        LOGGER.error("Only logistic activation function is " + "supported in KNIME MLP.");
    }
    if (NNNORMALIZATIONMETHOD.NONE != normMethod) {
        LOGGER.error("No normalization method is " + "supported in KNIME MLP.");
    }
    MININGFUNCTION.Enum functionName = nnModel.getFunctionName();
    if (MININGFUNCTION.CLASSIFICATION == functionName) {
        m_mlpMethod = MultiLayerPerceptron.CLASSIFICATION_MODE;
    } else if (MININGFUNCTION.REGRESSION == functionName) {
        m_mlpMethod = MultiLayerPerceptron.REGRESSION_MODE;
    }
    if (m_allLayers.size() < 3) {
        throw new IllegalArgumentException("Only neural networks with 3 Layers supported in KNIME MLP.");
    }
    Layer[] allLayers = new Layer[m_allLayers.size()];
    allLayers = m_allLayers.toArray(allLayers);
    m_mlp = new MultiLayerPerceptron(allLayers);
    Architecture myarch = new Architecture(allLayers[0].getPerceptrons().length, allLayers.length - 2, allLayers[1].getPerceptrons().length, allLayers[allLayers.length - 1].getPerceptrons().length);
    m_mlp.setArchitecture(myarch);
    m_mlp.setClassMapping(m_classmap);
    m_mlp.setInputMapping(m_inputmap);
    m_mlp.setMode(m_mlpMethod);
}

Example 3 with Layer

use of org.knime.base.data.neural.Layer in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addLayer.

/**
 * Writes a layer of the MLP.
 *
 * @param nnModel
 *            the NeuralNetwork model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 * @param layer
 *            the number of the current layer.
 */
protected void addLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp, final int layer) {
    Layer curLayer = mlp.getLayer(layer);
    Perceptron[] perceptrons = curLayer.getPerceptrons();
    AttributesImpl atts = new AttributesImpl();
    atts.addAttribute(null, null, "numberOfNeurons", CDATA, "" + perceptrons.length);
    NeuralLayer neuralLayer = nnModel.addNewNeuralLayer();
    for (int i = 0; i < perceptrons.length; i++) {
        Neuron neuron = neuralLayer.addNewNeuron();
        neuron.setId(layer + "," + i);
        neuron.setBias(-1 * perceptrons[i].getThreshold());
        double[] weights = perceptrons[i].getWeights();
        int predLayerLength = weights.length;
        for (int j = 0; j < predLayerLength; j++) {
            Con con = neuron.addNewCon();
            con.setFrom((layer - 1) + "," + j);
            con.setWeight(weights[j]);
        }
    }
}

Example 4 with Layer

use of org.knime.base.data.neural.Layer in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addInputLayer.

/**
 * Writes the PMML input layer of the MLP.
 *
 * @param nnModel
 *            the Neural Network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 */
protected void addInputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp) {
    Layer inputlayer = mlp.getLayer(0);
    Perceptron[] inputperceptrons = inputlayer.getPerceptrons();
    HashMap<String, Integer> inputmap = mlp.getInputMapping();
    NeuralInputs neuralInputs = nnModel.addNewNeuralInputs();
    neuralInputs.setNumberOfInputs(BigInteger.valueOf(inputperceptrons.length));
    for (int i = 0; i < inputperceptrons.length; i++) {
        NeuralInput neuralInput = neuralInputs.addNewNeuralInput();
        neuralInput.setId(0 + "," + i);
        // search corresponding input column
        String colname = "";
        for (Entry<String, Integer> e : inputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = e.getKey();
            }
        }
        DerivedField df = neuralInput.addNewDerivedField();
        df.setOptype(OPTYPE.CONTINUOUS);
        df.setDataType(DATATYPE.DOUBLE);
        FieldRef fieldRef = df.addNewFieldRef();
        fieldRef.setField(m_nameMapper.getDerivedFieldName(colname));
    }
}

Example 5 with Layer

use of org.knime.base.data.neural.Layer in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addOutputLayer.

/**
 * Writes the PMML output layer of the MLP.
 *
 * @param nnModel
 *            the neural network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 * @param spec
 *            the port object spec
 */
protected void addOutputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp, final PMMLPortObjectSpec spec) {
    int lastlayer = mlp.getNrLayers() - 1;
    String targetCol = spec.getTargetFields().iterator().next();
    Layer outputlayer = mlp.getLayer(lastlayer);
    Perceptron[] outputperceptrons = outputlayer.getPerceptrons();
    HashMap<DataCell, Integer> outputmap = mlp.getClassMapping();
    NeuralOutputs neuralOuts = nnModel.addNewNeuralOutputs();
    neuralOuts.setNumberOfOutputs(BigInteger.valueOf(outputperceptrons.length));
    for (int i = 0; i < outputperceptrons.length; i++) {
        NeuralOutput neuralOutput = neuralOuts.addNewNeuralOutput();
        neuralOutput.setOutputNeuron(lastlayer + "," + i);
        // search corresponding output value
        String colname = "";
        for (Entry<DataCell, Integer> e : outputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = ((StringValue) e.getKey()).getStringValue();
            }
        }
        DerivedField df = neuralOutput.addNewDerivedField();
        df.setOptype(OPTYPE.CATEGORICAL);
        df.setDataType(DATATYPE.STRING);
        if (mlp.getMode() == MultiLayerPerceptron.CLASSIFICATION_MODE) {
            df.setOptype(OPTYPE.CATEGORICAL);
            df.setDataType(DATATYPE.STRING);
        } else if (mlp.getMode() == MultiLayerPerceptron.REGRESSION_MODE) {
            df.setOptype(OPTYPE.CONTINUOUS);
            df.setDataType(DATATYPE.DOUBLE);
        }
        if (mlp.getMode() == MultiLayerPerceptron.CLASSIFICATION_MODE) {
            NormDiscrete normDiscrete = df.addNewNormDiscrete();
            normDiscrete.setField(targetCol);
            normDiscrete.setValue(colname);
        } else if (mlp.getMode() == MultiLayerPerceptron.REGRESSION_MODE) {
            FieldRef fieldRef = df.addNewFieldRef();
            fieldRef.setField(targetCol);
        }
    }
}