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

Example 1 with FloatNeuralStructure

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

the class DTrainUtils method generateNetwork.

// public static BasicNetwork generateNetwork(int in, int out, int numLayers, List<String> actFunc,
// List<Integer> hiddenNodeList, boolean isRandomizeWeights, double dropoutRate) {
// return generateNetwork(in, out, numLayers, actFunc, hiddenNodeList, isRandomizeWeights, dropoutRate,
// WGT_INIT_DEFAULT);
// }
public static BasicNetwork generateNetwork(int in, int out, int numLayers, List<String> actFunc, List<Integer> hiddenNodeList, boolean isRandomizeWeights, double dropoutRate, String wgtInit, boolean isLinearTarget, String outputActivationFunc) {
    final BasicFloatNetwork network = new BasicFloatNetwork();
    // in shifuconfig, we have a switch to control enable input layer dropout
    if (Boolean.valueOf(Environment.getProperty(CommonConstants.SHIFU_TRAIN_NN_INPUTLAYERDROPOUT_ENABLE, "true"))) {
        // we need to guarantee that input layer dropout rate is 40% of hiddenlayer dropout rate
        network.addLayer(new BasicDropoutLayer(new ActivationLinear(), true, in, dropoutRate * 0.4d));
    } else {
        network.addLayer(new BasicDropoutLayer(new ActivationLinear(), true, in, 0d));
    }
    // int hiddenNodes = 0;
    for (int i = 0; i < numLayers; i++) {
        String func = actFunc.get(i);
        Integer numHiddenNode = hiddenNodeList.get(i);
        // hiddenNodes += numHiddenNode;
        if (func.equalsIgnoreCase(NNConstants.NN_LINEAR)) {
            network.addLayer(new BasicDropoutLayer(new ActivationLinear(), true, numHiddenNode, dropoutRate));
        } else if (func.equalsIgnoreCase(NNConstants.NN_SIGMOID)) {
            network.addLayer(new BasicDropoutLayer(new ActivationSigmoid(), true, numHiddenNode, dropoutRate));
        } else if (func.equalsIgnoreCase(NNConstants.NN_TANH)) {
            network.addLayer(new BasicDropoutLayer(new ActivationTANH(), true, numHiddenNode, dropoutRate));
        } else if (func.equalsIgnoreCase(NNConstants.NN_LOG)) {
            network.addLayer(new BasicDropoutLayer(new ActivationLOG(), true, numHiddenNode, dropoutRate));
        } else if (func.equalsIgnoreCase(NNConstants.NN_SIN)) {
            network.addLayer(new BasicDropoutLayer(new ActivationSIN(), true, numHiddenNode, dropoutRate));
        } else if (func.equalsIgnoreCase(NNConstants.NN_RELU)) {
            network.addLayer(new BasicDropoutLayer(new ActivationReLU(), true, numHiddenNode, dropoutRate));
        } else if (func.equalsIgnoreCase(NNConstants.NN_LEAKY_RELU)) {
            network.addLayer(new BasicDropoutLayer(new ActivationLeakyReLU(), true, numHiddenNode, dropoutRate));
        } else if (func.equalsIgnoreCase(NNConstants.NN_SWISH)) {
            network.addLayer(new BasicDropoutLayer(new ActivationSwish(), true, numHiddenNode, dropoutRate));
        } else if (func.equalsIgnoreCase(NNConstants.NN_PTANH)) {
            network.addLayer(new BasicDropoutLayer(new ActivationPTANH(), true, numHiddenNode, dropoutRate));
        } else {
            network.addLayer(new BasicDropoutLayer(new ActivationSigmoid(), true, numHiddenNode, dropoutRate));
        }
    }
    if (isLinearTarget) {
        if (NNConstants.NN_RELU.equalsIgnoreCase(outputActivationFunc)) {
            network.addLayer(new BasicLayer(new ActivationReLU(), true, out));
        } else if (NNConstants.NN_LEAKY_RELU.equalsIgnoreCase(outputActivationFunc)) {
            network.addLayer(new BasicLayer(new ActivationLeakyReLU(), true, out));
        } else if (NNConstants.NN_SWISH.equalsIgnoreCase(outputActivationFunc)) {
            network.addLayer(new BasicLayer(new ActivationSwish(), true, out));
        } else {
            network.addLayer(new BasicLayer(new ActivationLinear(), true, out));
        }
    } else {
        network.addLayer(new BasicLayer(new ActivationSigmoid(), false, out));
    }
    NeuralStructure structure = network.getStructure();
    if (network.getStructure() instanceof FloatNeuralStructure) {
        ((FloatNeuralStructure) structure).finalizeStruct();
    } else {
        structure.finalizeStructure();
    }
    if (isRandomizeWeights) {
        if (wgtInit == null || wgtInit.length() == 0) {
            // default randomization
            network.reset();
        } else if (wgtInit.equalsIgnoreCase(WGT_INIT_GAUSSIAN)) {
            new GaussianRandomizer(0, 1).randomize(network);
        } else if (wgtInit.equalsIgnoreCase(WGT_INIT_XAVIER)) {
            new XavierWeightRandomizer().randomize(network);
        } else if (wgtInit.equalsIgnoreCase(WGT_INIT_HE)) {
            new HeWeightRandomizer().randomize(network);
        } else if (wgtInit.equalsIgnoreCase(WGT_INIT_LECUN)) {
            new LecunWeightRandomizer().randomize(network);
        } else if (wgtInit.equalsIgnoreCase(WGT_INIT_DEFAULT)) {
            // default randomization
            network.reset();
        } else {
            // default randomization
            network.reset();
        }
    }
    return network;
}