Examples with Backpropagation org.gitia.froog.trainingalgorithm.Backpropagation used on opensource projects

Example 1 with Backpropagation

use of org.gitia.froog.trainingalgorithm.Backpropagation in project froog by mroodschild.

the class BackpropagationBachTest method testEntrenar.

/**
 * Test of entrenar method, of class BackpropagationBach.
 */
@Ignore
@Test
public void testEntrenar() {
    System.out.println("entrenar");
    Feedforward net = null;
    double[][] input = null;
    double[][] output = null;
    int iteraciones = 0;
    Backpropagation instance = new Backpropagation();
    instance.setEpoch(iteraciones);
    instance.entrenar(net, input, output);
    // TODO review the generated test code and remove the default call to fail.
    fail("The test case is a prototype.");
}

Example 2 with Backpropagation

use of org.gitia.froog.trainingalgorithm.Backpropagation in project froog by mroodschild.

the class BackpropagationBachTest method testComputeCost_doubleArr_doubleArr.

/**
 * Test of computeCost method, of class BackpropagationBach.
 */
@Ignore
@Test
public void testComputeCost_doubleArr_doubleArr() {
    System.out.println("computeCost");
    double[] input = null;
    double[] output = null;
    Backpropagation instance = new Backpropagation();
    double expResult = 0.0;
    double result = instance.cost(input, output);
    assertEquals(expResult, result, 0.0);
    // TODO review the generated test code and remove the default call to fail.
    fail("The test case is a prototype.");
}

Example 3 with Backpropagation

use of org.gitia.froog.trainingalgorithm.Backpropagation in project froog by mroodschild.

the class BackpropagationBachTest method testComputeCost_SimpleMatrix_SimpleMatrix.

// /**
// * Test of calcularGradientes method, of class BackpropagationBach.
// */
// @Ignore
// @Test
// public void testCalcularGradientes() {
// System.out.println("calcularGradientes");
// Backpropagation instance = new Backpropagation();
// double expResult = 0.0;
// double result = instance.calcularGradientes();
// assertEquals(expResult, result, 0.0);
// // TODO review the generated test code and remove the default call to fail.
// fail("The test case is a prototype.");
// }
/**
 * Test of computeCost method, of class BackpropagationBach.
 */
@Ignore
@Test
public void testComputeCost_SimpleMatrix_SimpleMatrix() {
    System.out.println("computeCost");
    SimpleMatrix input = null;
    SimpleMatrix Yobs = null;
    Backpropagation instance = new Backpropagation();
    double expResult = 0.0;
    double result = instance.cost(input, Yobs);
    assertEquals(expResult, result, 0.0);
    // TODO review the generated test code and remove the default call to fail.
    fail("The test case is a prototype.");
}

Example 4 with Backpropagation

use of org.gitia.froog.trainingalgorithm.Backpropagation in project froog by mroodschild.

the class Test method main.

public static void main(String[] args) {
    SimpleMatrix input = CSV.open("src/main/resources/function/train_in.csv");
    SimpleMatrix output = CSV.open("src/main/resources/function/train_out.csv");
    SimpleMatrix in_test = CSV.open("src/main/resources/function/test_in.csv");
    SimpleMatrix out_test = CSV.open("src/main/resources/function/test_out.csv");
    SimpleMatrix all_in = CSV.open("src/main/resources/function/all_in.csv");
    STD std = new STD();
    std.fit(input);
    input = std.eval(input);
    in_test = std.eval(in_test);
    all_in = std.eval(all_in);
    int inputSize = input.numCols();
    int outputSize = output.numCols();
    // ==================== Preparamos la RNA =======================
    Random rand = new Random(4);
    // int nn = 30;
    Feedforward net = new Feedforward();
    net.addLayer(new Layer(inputSize, 10, TransferFunction.TANSIG, rand));
    net.addLayer(new Layer(10, 5, TransferFunction.PRERELU, rand));
    net.addLayer(new Layer(5, outputSize, TransferFunction.PURELIM, rand));
    // =================  configuraciones del ensayo ========================
    // Preparamos el algoritmo de entrenamiento
    Backpropagation bp = new Backpropagation();
    bp.setEpoch(5000);
    // bp.setMomentum(0.9);
    bp.setLearningRate(0.01);
    bp.setInputTest(in_test);
    bp.setOutputTest(out_test);
    bp.setTestFrecuency(1);
    bp.setLossFunction(LossFunction.RMSE);
    input.printDimensions();
    output.printDimensions();
    bp.entrenar(net, input, output);
    try {
        net.outputAll(all_in).saveToFileCSV("src/main/resources/function/res_train.csv");
    } catch (IOException ex) {
        Logger.getLogger(Test.class.getName()).log(Level.SEVERE, null, ex);
    }
}