Examples of org.apache.hama.ml.ann.SmallLayeredNeuralNetwork

org.apache.hama.ml.ann.SmallLayeredNeuralNetwork
SmallLayeredNeuralNetwork defines the general operations for derivative layered models, include Linear Regression, Logistic Regression, Multilayer Perceptron, Autoencoder, and Restricted Boltzmann Machine, etc. For SmallLayeredNeuralNetwork, the training can be conducted in parallel, but the parameters of the models are assumes to be stored in a single machine. In general, these models consist of neurons which are aligned in layers. Between layers, for any two adjacent layers, the neurons are connected to form a bipartite weighted graph.


  /* Internal model */
  private final SmallLayeredNeuralNetwork ann;


  public LinearRegression(int dimension) {
    ann = new SmallLayeredNeuralNetwork();
    ann.addLayer(dimension, false,
        FunctionFactory.createDoubleFunction("IdentityFunction"));
    ann.addLayer(1, true,
        FunctionFactory.createDoubleFunction("IdentityFunction"));
    ann.setCostFunction(FunctionFactory

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    ann.setCostFunction(FunctionFactory
        .createDoubleDoubleFunction("SquaredError"));
  }


  public LinearRegression(String modelPath) {
    ann = new SmallLayeredNeuralNetwork(modelPath);
  }

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public class LogisticRegression {
  
  private final SmallLayeredNeuralNetwork ann;
  
  public LogisticRegression(int dimension) {
    this.ann = new SmallLayeredNeuralNetwork();
    this.ann.addLayer(dimension, false, FunctionFactory.createDoubleFunction("Sigmoid"));
    this.ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
    this.ann.setCostFunction(FunctionFactory.createDoubleDoubleFunction("CrossEntropy"));
  }

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    this.ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
    this.ann.setCostFunction(FunctionFactory.createDoubleDoubleFunction("CrossEntropy"));
  }
  
  public LogisticRegression(String modelPath) {
    this.ann = new SmallLayeredNeuralNetwork(modelPath);
  }

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