Examples with BasicFloatNetwork ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork used on opensource projects

Example 11 with BasicFloatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork in project shifu by ShifuML.

the class TrainModelProcessor method inputOutputModelCheckSuccess.

@SuppressWarnings("unchecked")
private boolean inputOutputModelCheckSuccess(FileSystem fileSystem, Path modelPath, Map<String, Object> modelParams) throws IOException {
    BasicML basicML = ModelSpecLoaderUtils.loadModel(this.modelConfig, modelPath, fileSystem);
    BasicFloatNetwork model = (BasicFloatNetwork) ModelSpecLoaderUtils.getBasicNetwork(basicML);
    int[] outputCandidateCounts = DTrainUtils.getInputOutputCandidateCounts(modelConfig.getNormalizeType(), getColumnConfigList());
    int inputs = outputCandidateCounts[0] == 0 ? outputCandidateCounts[2] : outputCandidateCounts[0];
    boolean isInputOutConsistent = model.getInputCount() <= inputs && model.getOutputCount() == outputCandidateCounts[1];
    if (!isInputOutConsistent) {
        return false;
    }
    // same hidden layer ?
    boolean isHasSameHiddenLayer = (model.getLayerCount() - 2) <= (Integer) modelParams.get(CommonConstants.NUM_HIDDEN_LAYERS);
    if (!isHasSameHiddenLayer) {
        return false;
    }
    // same hidden nodes ?
    boolean isHasSameHiddenNodes = true;
    // same activations ?
    boolean isHasSameHiddenActivation = true;
    List<Integer> hiddenNodeList = (List<Integer>) modelParams.get(CommonConstants.NUM_HIDDEN_NODES);
    List<String> actFuncList = (List<String>) modelParams.get(CommonConstants.ACTIVATION_FUNC);
    for (int i = 1; i < model.getLayerCount() - 1; i++) {
        if (model.getLayerNeuronCount(i) > hiddenNodeList.get(i - 1)) {
            isHasSameHiddenNodes = false;
            break;
        }
        ActivationFunction activation = model.getActivation(i);
        String actFunc = actFuncList.get(i - 1);
        if (actFunc.equalsIgnoreCase(NNConstants.NN_LINEAR)) {
            isHasSameHiddenActivation = ActivationLinear.class == activation.getClass();
        } else if (actFunc.equalsIgnoreCase(NNConstants.NN_SIGMOID)) {
            isHasSameHiddenActivation = ActivationSigmoid.class == activation.getClass();
        } else if (actFunc.equalsIgnoreCase(NNConstants.NN_TANH)) {
            isHasSameHiddenActivation = ActivationTANH.class == activation.getClass();
        } else if (actFunc.equalsIgnoreCase(NNConstants.NN_LOG)) {
            isHasSameHiddenActivation = ActivationLOG.class == activation.getClass();
        } else if (actFunc.equalsIgnoreCase(NNConstants.NN_SIN)) {
            isHasSameHiddenActivation = ActivationSIN.class == activation.getClass();
        } else if (actFunc.equalsIgnoreCase(NNConstants.NN_RELU)) {
            isHasSameHiddenActivation = ActivationReLU.class == activation.getClass();
        } else if (actFunc.equalsIgnoreCase(NNConstants.NN_LEAKY_RELU)) {
            isHasSameHiddenActivation = ActivationLeakyReLU.class == activation.getClass();
        } else if (actFunc.equalsIgnoreCase(NNConstants.NN_SWISH)) {
            isHasSameHiddenActivation = ActivationSwish.class == activation.getClass();
        } else if (actFunc.equalsIgnoreCase(NNConstants.NN_PTANH)) {
            isHasSameHiddenActivation = ActivationPTANH.class == activation.getClass();
        } else {
            isHasSameHiddenActivation = ActivationSigmoid.class == activation.getClass();
        }
        if (!isHasSameHiddenActivation) {
            break;
        }
    }
    if (!isHasSameHiddenNodes || !isHasSameHiddenActivation) {
        return false;
    }
    return true;
}

Example 12 with BasicFloatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork in project shifu by ShifuML.

the class NNOutput method initNetwork.

@SuppressWarnings("unchecked")
private void initNetwork(MasterContext<NNParams, NNParams> context) {
    int[] inputOutputIndex = DTrainUtils.getInputOutputCandidateCounts(modelConfig.getNormalizeType(), this.columnConfigList);
    boolean isLinearTarget = CommonUtils.isLinearTarget(modelConfig, columnConfigList);
    @SuppressWarnings("unused") int inputNodeCount = inputOutputIndex[0] == 0 ? inputOutputIndex[2] : inputOutputIndex[0];
    // if is one vs all classification, outputNodeCount is set to 1, if classes=2, outputNodeCount is also 1
    int classes = modelConfig.getTags().size();
    int outputNodeCount = (isLinearTarget || modelConfig.isRegression()) ? inputOutputIndex[1] : (modelConfig.getTrain().isOneVsAll() ? inputOutputIndex[1] : (classes == 2 ? 1 : classes));
    int numLayers = (Integer) validParams.get(CommonConstants.NUM_HIDDEN_LAYERS);
    List<String> actFunc = (List<String>) validParams.get(CommonConstants.ACTIVATION_FUNC);
    List<Integer> hiddenNodeList = (List<Integer>) validParams.get(CommonConstants.NUM_HIDDEN_NODES);
    boolean isAfterVarSelect = inputOutputIndex[0] != 0;
    // cache all feature list for sampling features
    List<Integer> allFeatures = NormalUtils.getAllFeatureList(columnConfigList, isAfterVarSelect);
    String subsetStr = context.getProps().getProperty(CommonConstants.SHIFU_NN_FEATURE_SUBSET);
    if (StringUtils.isBlank(subsetStr)) {
        this.subFeatures = new HashSet<Integer>(allFeatures);
    } else {
        String[] splits = subsetStr.split(",");
        this.subFeatures = new HashSet<Integer>();
        for (String split : splits) {
            this.subFeatures.add(Integer.parseInt(split));
        }
    }
    int featureInputsCnt = DTrainUtils.getFeatureInputsCnt(modelConfig, columnConfigList, this.subFeatures);
    String outputActivationFunc = (String) validParams.get(CommonConstants.OUTPUT_ACTIVATION_FUNC);
    this.network = DTrainUtils.generateNetwork(featureInputsCnt, outputNodeCount, numLayers, actFunc, hiddenNodeList, false, this.dropoutRate, this.wgtInit, CommonUtils.isLinearTarget(modelConfig, columnConfigList), outputActivationFunc);
    ((BasicFloatNetwork) this.network).setFeatureSet(this.subFeatures);
    // register here to save models
    PersistorRegistry.getInstance().add(new PersistBasicFloatNetwork());
}

Example 13 with BasicFloatNetwork

use of ml.shifu.shifu.core.dtrain.dataset.BasicFloatNetwork in project shifu by ShifuML.

the class IndependentNNModel method compute.

/**
 * Given double array data, compute score values of neural network
 *
 * @param data
 *            data array includes only effective column data, numeric value is real value after normalization,
 *            categorical feature value is pos rates or woe .
 * @return neural network model output, if multiple models, do averaging on all models outputs
 */
public double[] compute(double[] data) {
    if (this.basicNetworks == null || this.basicNetworks.size() == 0) {
        throw new IllegalStateException("no models inside");
    }
    if (this.basicNetworks.size() == 1) {
        return this.basicNetworks.get(0).compute(new BasicMLData(data)).getData();
    } else {
        int outputSize = this.basicNetworks.get(0).getOutputCount();
        int modelSize = this.basicNetworks.size();
        double[] results = new double[outputSize];
        for (BasicFloatNetwork network : this.basicNetworks) {
            double[] currResults = network.compute(new BasicMLData(data)).getData();
            assert currResults.length == results.length;
            for (int i = 0; i < currResults.length; i++) {
                // directly do averaging on each model output element
                results[i] += currResults[i] / modelSize;
            }
        }
        return results;
    }
}