Example usage for org.deeplearning4j.nn.conf Updater ADADELTA

Introduction

In this page you can find the example usage for org.deeplearning4j.nn.conf Updater ADADELTA.

Prototype

Updater ADADELTA

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Usage

From source file:gr.aueb.cs.nlp.computationgraphs.GraphConfigurations.java

License:Open Source License

/**
 * an example LSTM Graph//from   ww w.  j a  v  a2s.  co  m
 * @param totalCategories
 * @param trainSet
 * @param testSet
 * @return
 */
public static ComputationGraphConfiguration LSTMGraph(List<Word> trainSet) {
    int inputFeatures = trainSet.get(0).getFeatureVec().getValues().length;
    int outputLabels = trainSet.get(0).getFeatureVec().getLabels().length;

    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(0.01)
            .regularization(true).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .graphBuilder()

            .addInputs("input") //can use any label for this, it is just an identifier for the graph.
            .addLayer("L1", new GravesLSTM.Builder().nIn(inputFeatures) // always current nIn = Sum(nOut) of layer inputs
                    .nOut(150) //you have as many neurons as your outputs...
                    .biasLearningRate(0.2) //faster changing bias int he first layer then slower
                    .activation(Activation.TANH) // the enumerations are made by me, 
                    //to help me know which activations are available,
                    //for me relu work so often but they need more neurons per layer than others
                    .l1(0.3) //l1 regularization
                    .l2(0.02) //l2 regularization
                    .dropOut(0.3) //regularization via freezing whole neurons on a feedforward phase, 
                    //this works usual/y better than l1,l2 for me
                    .updater(Updater.ADADELTA) //how the weights are updated. nesterovs uses the momentum
                    .momentum(0.3) //the higher the easier to escape a saddle point or miss an optimum
                    .build(), "input")//input here, means which layer is the input to this layes. 
            //so the layer with identifier "input" is the input for "L1" 
            .addLayer("L2", new GravesLSTM.Builder().nIn(150) // an autoencoder for some feature extraction
                    .nOut(200).biasLearningRate(0.02).l1(0.3).l2(0.02).activation(Activation.RELU).dropOut(0.3)
                    .updater(Updater.ADAM)//how to use ADAM, read the ADAM paper 
                    //to understand better what they do https://arxiv.org/pdf/1412.6980.pdf
                    //usually cheaper training and better bias correction...
                    .adamMeanDecay(0.2).adamMeanDecay(0.2).build(), "L1")
            .addLayer("L3", new RnnOutputLayer.Builder().nIn(200).nOut(outputLabels).build(), "L2")
            .setOutputs("L3") //We need to specify the network outputs and their order
            .build();
    return conf;
}

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From source file:org.wso2.carbon.ml.rest.api.neuralNetworks.FeedForwardNetwork.java

License:Open Source License

/**
 * method to map user selected Updater Algorithm to Updater object.
 * @param updater//from   w w w  .j  a  v  a2 s.  co  m
 * @return an Updater object .
 */
Updater mapUpdater(String updater) {

    Updater updaterAlgo = null;

    switch (updater) {

    case "sgd":
        updaterAlgo = Updater.SGD;
        break;

    case "adam":
        updaterAlgo = Updater.ADAM;
        break;

    case "adadelta":
        updaterAlgo = Updater.ADADELTA;
        break;

    case "nesterovs":
        updaterAlgo = Updater.NESTEROVS;
        break;

    case "adagrad":
        updaterAlgo = Updater.ADAGRAD;
        break;

    case "rmsprop":
        updaterAlgo = Updater.RMSPROP;
        break;

    case "none":
        updaterAlgo = Updater.NONE;
        break;

    case "custom":
        updaterAlgo = Updater.CUSTOM;
        break;

    default:
        updaterAlgo = null;
        break;
    }
    return updaterAlgo;
}

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