Examples with BasicLayer org.encog.neural.networks.layers.BasicLayer used on opensource projects

Example 1 with BasicLayer

use of org.encog.neural.networks.layers.BasicLayer in project shifu by ShifuML.

the class FloatNeuralStructure method finalizeStruct.

/**
 * Build the synapse and layer structure. This method should be called afteryou are done adding layers to a network,
 * or change the network's logic property.
 */
public void finalizeStruct() {
    if (this.getLayers().size() < 2) {
        throw new NeuralNetworkError("There must be at least two layers before the structure is finalized.");
    }
    final FlatLayer[] flatLayers = new FlatLayer[this.getLayers().size()];
    for (int i = 0; i < this.getLayers().size(); i++) {
        final BasicLayer layer = (BasicLayer) this.getLayers().get(i);
        if (layer.getActivation() == null) {
            layer.setActivation(new ActivationLinear());
        }
        flatLayers[i] = layer;
    }
    this.setFlat(new FloatFlatNetwork(flatLayers, true));
    finalizeLimit();
    this.getLayers().clear();
    enforceLimit();
}

Example 2 with BasicLayer

use of org.encog.neural.networks.layers.BasicLayer in project shifu by ShifuML.

the class NNTrainer method buildNetwork.

@SuppressWarnings("unchecked")
public void buildNetwork() {
    network = new BasicNetwork();
    network.addLayer(new BasicLayer(new ActivationLinear(), true, trainSet.getInputSize()));
    int numLayers = (Integer) modelConfig.getParams().get(CommonConstants.NUM_HIDDEN_LAYERS);
    List<String> actFunc = (List<String>) modelConfig.getParams().get(CommonConstants.ACTIVATION_FUNC);
    List<Integer> hiddenNodeList = (List<Integer>) modelConfig.getParams().get(CommonConstants.NUM_HIDDEN_NODES);
    if (numLayers != 0 && (numLayers != actFunc.size() || numLayers != hiddenNodeList.size())) {
        throw new RuntimeException("the number of layer do not equal to the number of activation function or the function list and node list empty");
    }
    if (toLoggingProcess)
        LOG.info("    - total " + numLayers + " layers, each layers are: " + Arrays.toString(hiddenNodeList.toArray()) + " the activation function are: " + Arrays.toString(actFunc.toArray()));
    for (int i = 0; i < numLayers; i++) {
        String func = actFunc.get(i);
        Integer numHiddenNode = hiddenNodeList.get(i);
        // java 6
        if ("linear".equalsIgnoreCase(func)) {
            network.addLayer(new BasicLayer(new ActivationLinear(), true, numHiddenNode));
        } else if (func.equalsIgnoreCase("sigmoid")) {
            network.addLayer(new BasicLayer(new ActivationSigmoid(), true, numHiddenNode));
        } else if (func.equalsIgnoreCase("tanh")) {
            network.addLayer(new BasicLayer(new ActivationTANH(), true, numHiddenNode));
        } else if (func.equalsIgnoreCase("log")) {
            network.addLayer(new BasicLayer(new ActivationLOG(), true, numHiddenNode));
        } else if (func.equalsIgnoreCase("sin")) {
            network.addLayer(new BasicLayer(new ActivationSIN(), true, numHiddenNode));
        } else {
            LOG.info("Unsupported activation function: " + func + " !! Set this layer activation function to be Sigmoid ");
            network.addLayer(new BasicLayer(new ActivationSigmoid(), true, numHiddenNode));
        }
    }
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false, trainSet.getIdealSize()));
    network.getStructure().finalizeStructure();
    if (!modelConfig.isFixInitialInput()) {
        network.reset();
    } else {
        int numWeight = 0;
        for (int i = 0; i < network.getLayerCount() - 1; i++) {
            numWeight = numWeight + network.getLayerTotalNeuronCount(i) * network.getLayerNeuronCount(i + 1);
        }
        LOG.info("    - You have " + numWeight + " weights to be initialize");
        loadWeightsInput(numWeight);
    }
}

Example 3 with BasicLayer

use of org.encog.neural.networks.layers.BasicLayer in project shifu by ShifuML.

the class LogisticRegressionTrainer method train.

/**
 * {@inheritDoc}
 * <p>
 * no <code>regularization</code>
 * <p>
 * Regular will be provide later
 * <p>
 *
 * @throws IOException
 *             e
 */
@Override
public double train() throws IOException {
    classifier = new BasicNetwork();
    classifier.addLayer(new BasicLayer(new ActivationLinear(), true, trainSet.getInputSize()));
    classifier.addLayer(new BasicLayer(new ActivationSigmoid(), false, trainSet.getIdealSize()));
    classifier.getStructure().finalizeStructure();
    // resetParams(classifier);
    classifier.reset();
    // Propagation mlTrain = getMLTrain();
    Propagation propagation = new QuickPropagation(classifier, trainSet, (Double) modelConfig.getParams().get("LearningRate"));
    int epochs = modelConfig.getNumTrainEpochs();
    // Get convergence threshold from modelConfig.
    double threshold = modelConfig.getTrain().getConvergenceThreshold() == null ? 0.0 : modelConfig.getTrain().getConvergenceThreshold().doubleValue();
    String formatedThreshold = df.format(threshold);
    LOG.info("Using " + (Double) modelConfig.getParams().get("LearningRate") + " training rate");
    for (int i = 0; i < epochs; i++) {
        propagation.iteration();
        double trainError = propagation.getError();
        double validError = classifier.calculateError(this.validSet);
        LOG.info("Epoch #" + (i + 1) + " Train Error:" + df.format(trainError) + " Validation Error:" + df.format(validError));
        // Convergence judging.
        double avgErr = (trainError + validError) / 2;
        if (judger.judge(avgErr, threshold)) {
            LOG.info("Trainer-{}> Epoch #{} converged! Average Error: {}, Threshold: {}", trainerID, (i + 1), df.format(avgErr), formatedThreshold);
            break;
        }
    }
    propagation.finishTraining();
    LOG.info("#" + this.trainerID + " finish training");
    saveLR();
    return 0.0d;
}

Example 4 with BasicLayer

use of org.encog.neural.networks.layers.BasicLayer in project shifu by ShifuML.

the class NNTrainerTest method setUp.

@BeforeClass
public void setUp() {
    trainSet = new BasicMLDataSet();
    network = new BasicNetwork();
    network.addLayer(new BasicLayer(new ActivationLinear(), true, 2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 4));
    network.addLayer(new BasicLayer(new ActivationLOG(), true, 3));
    network.addLayer(new BasicLayer(new ActivationSIN(), true, 3));
    network.addLayer(new BasicLayer(new ActivationTANH(), false, 1));
    network.getStructure().finalizeStructure();
    network.reset();
}

Example 5 with BasicLayer

use of org.encog.neural.networks.layers.BasicLayer 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;
}