private void modifyWeights(
      DoubleMatrix trainingExample,
      DoubleMatrix result,
      DoubleMatrix output,
      double learningFactor,
      double momentum,
      List<DoubleMatrix> previousModifications,
      EvaluationContext evalCtx) {

    List<DoubleMatrix> errors = countErrors(result, output);
    List<Layer> layers = getLayers();

    evalCtx.resetContext();
    Iterator<ActivationFunction> activationFunctionIter = evalCtx.getActivationFunction();

    DoubleMatrix temporalResult = trainingExample;

    for (int i = 0; i < errors.size(); i++) {
      DoubleMatrix error = errors.get(i);
      int layerIndex = i + 1;
      Layer layer = layers.get(layerIndex);
      DoubleMatrix previousModification = previousModifications.get(i);

      ActivationFunction activationFunc = activationFunctionIter.next();
      ActivationFunctionDerivative derivative =
          DerivativeFactory.getInstance().getDerivative(activationFunc);

      if (layer.includeBias()) {
        temporalResult = addBiasInput(temporalResult);
      }

      DoubleMatrix oldVal = temporalResult.dup();
      temporalResult = layer.getWeights().mmul(temporalResult);
      temporalResult = activationFunc.eval(temporalResult);

      // dla kazdego neuronu w warstwie
      for (int j = 0; j < layer.getWeights().rows; j++) {

        double derVal = derivative.evaluate(temporalResult.get(j));
        DoubleMatrix oldDelta = previousModification.getRow(j);
        DoubleMatrix delta = oldVal.mul(derVal).mul(learningFactor).mul(error.get(j));
        delta = delta.transpose();
        delta = delta.add(oldDelta.mul(momentum));
        previousModification.putRow(j, delta);
        DoubleMatrix oldWeights = layer.getWeights().getRow(j);
        DoubleMatrix newWeights = oldWeights.add(delta);
        layer.getWeights().putRow(j, newWeights);
      }
    }
  }
  @Test
  public void testWithMnist() throws Exception {
    MnistDataFetcher fetcher = new MnistDataFetcher(true);
    fetcher.fetch(200);
    DataSet data = fetcher.next();
    data.filterAndStrip(new int[] {0, 1});
    log.info("Training on " + data.numExamples());

    DBN dbn =
        new DBN.Builder()
            .hiddenLayerSizes(new int[] {1000, 500, 250, 10})
            .numberOfInputs(784)
            .numberOfOutPuts(2)
            .build();

    dbn.pretrain(data.getFirst(), new Object[] {1, 1e-1, 10000});

    DeepAutoEncoder encoder = new DeepAutoEncoder(dbn);
    encoder.finetune(data.getFirst(), 1e-3, 1000);

    DoubleMatrix reconstruct = encoder.reconstruct(data.getFirst());
    for (int j = 0; j < data.numExamples(); j++) {

      DoubleMatrix draw1 = data.get(j).getFirst().mul(255);
      DoubleMatrix reconstructed2 = reconstruct.getRow(j);
      DoubleMatrix draw2 = reconstructed2.mul(255);

      DrawMnistGreyScale d = new DrawMnistGreyScale(draw1);
      d.title = "REAL";
      d.draw();
      DrawMnistGreyScale d2 = new DrawMnistGreyScale(draw2);
      d2.title = "TEST";
      d2.draw();
      Thread.sleep(10000);
      d.frame.dispose();
      d2.frame.dispose();
    }
  }
Exemple #3
0
  private void costantiniUnwrap() throws LPException {

    final int ny = wrappedPhase.rows - 1; // start from Zero!
    final int nx = wrappedPhase.columns - 1; // start from Zero!

    if (wrappedPhase.isVector()) throw new IllegalArgumentException("Input must be 2D array");
    if (wrappedPhase.rows < 2 || wrappedPhase.columns < 2)
      throw new IllegalArgumentException("Size of input must be larger than 2");

    // Default weight
    DoubleMatrix w1 = DoubleMatrix.ones(ny + 1, 1);
    w1.put(0, 0.5);
    w1.put(w1.length - 1, 0.5);
    DoubleMatrix w2 = DoubleMatrix.ones(1, nx + 1);
    w2.put(0, 0.5);
    w2.put(w2.length - 1, 0.5);
    DoubleMatrix weight = w1.mmul(w2);

    DoubleMatrix i, j, I_J, IP1_J, I_JP1;
    DoubleMatrix Psi1, Psi2;
    DoubleMatrix[] ROWS;

    // Compute partial derivative Psi1, eqt (1,3)
    i = intRangeDoubleMatrix(0, ny - 1);
    j = intRangeDoubleMatrix(0, nx);
    ROWS = grid2D(i, j);
    I_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1]);
    IP1_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0].add(1), ROWS[1]);
    Psi1 =
        JblasUtils.getMatrixFromIdx(wrappedPhase, IP1_J)
            .sub(JblasUtils.getMatrixFromIdx(wrappedPhase, I_J));
    Psi1 = UnwrapUtils.wrapDoubleMatrix(Psi1);

    // Compute partial derivative Psi2, eqt (2,4)
    i = intRangeDoubleMatrix(0, ny);
    j = intRangeDoubleMatrix(0, nx - 1);
    ROWS = grid2D(i, j);
    I_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1]);
    I_JP1 = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1].add(1));
    Psi2 =
        JblasUtils.getMatrixFromIdx(wrappedPhase, I_JP1)
            .sub(JblasUtils.getMatrixFromIdx(wrappedPhase, I_J));
    Psi2 = UnwrapUtils.wrapDoubleMatrix(Psi2);

    // Compute beq
    DoubleMatrix beq = DoubleMatrix.zeros(ny, nx);
    i = intRangeDoubleMatrix(0, ny - 1);
    j = intRangeDoubleMatrix(0, nx - 1);
    ROWS = grid2D(i, j);
    I_J = JblasUtils.sub2ind(Psi1.rows, ROWS[0], ROWS[1]);
    I_JP1 = JblasUtils.sub2ind(Psi1.rows, ROWS[0], ROWS[1].add(1));
    beq.addi(JblasUtils.getMatrixFromIdx(Psi1, I_JP1).sub(JblasUtils.getMatrixFromIdx(Psi1, I_J)));
    I_J = JblasUtils.sub2ind(Psi2.rows, ROWS[0], ROWS[1]);
    I_JP1 = JblasUtils.sub2ind(Psi2.rows, ROWS[0].add(1), ROWS[1]);
    beq.subi(JblasUtils.getMatrixFromIdx(Psi2, I_JP1).sub(JblasUtils.getMatrixFromIdx(Psi2, I_J)));
    beq.muli(-1 / (2 * Constants._PI));
    for (int k = 0; k < beq.length; k++) {
      beq.put(k, Math.round(beq.get(k)));
    }
    beq.reshape(beq.length, 1);

    logger.debug("Constraint matrix");
    i = intRangeDoubleMatrix(0, ny - 1);
    j = intRangeDoubleMatrix(0, nx - 1);
    ROWS = grid2D(i, j);
    DoubleMatrix ROW_I_J = JblasUtils.sub2ind(i.length, ROWS[0], ROWS[1]);
    int nS0 = nx * ny;

    // Use by S1p, S1m
    DoubleMatrix[] COLS;
    COLS = grid2D(i, j);
    DoubleMatrix COL_IJ_1 = JblasUtils.sub2ind(i.length, COLS[0], COLS[1]);
    COLS = grid2D(i, j.add(1));
    DoubleMatrix COL_I_JP1 = JblasUtils.sub2ind(i.length, COLS[0], COLS[1]);
    int nS1 = (nx + 1) * (ny);

    // SOAPBinding.Use by S2p, S2m
    COLS = grid2D(i, j);
    DoubleMatrix COL_IJ_2 = JblasUtils.sub2ind(i.length + 1, COLS[0], COLS[1]);
    COLS = grid2D(i.add(1), j);
    DoubleMatrix COL_IP1_J = JblasUtils.sub2ind(i.length + 1, COLS[0], COLS[1]);
    int nS2 = nx * (ny + 1);

    // Equality constraint matrix (Aeq)
    /*
        S1p = + sparse(ROW_I_J, COL_I_JP1,1,nS0,nS1) ...
              - sparse(ROW_I_J, COL_IJ_1,1,nS0,nS1);
        S1m = -S1p;

        S2p = - sparse(ROW_I_J, COL_IP1_J,1,nS0,nS2) ...
              + sparse(ROW_I_J, COL_IJ_2,1,nS0,nS2);
        S2m = -S2p;
    */

    // ToDo: Aeq matrix should be sparse from it's initialization, look into JblasMatrix factory for
    // howto
    // ...otherwise even a data set of eg 40x40 pixels will exhaust heap:
    // ...    dimension of Aeq (equality constraints) matrix for 30x30 input is 1521x6240 matrix
    // ...    dimension of Aeq (                    ) matrix for 50x50 input is 2401x9800
    // ...    dimension of Aeq (                    ) matrix for 512x512 input is 261121x1046528
    DoubleMatrix S1p =
        JblasUtils.setUpMatrixFromIdx(nS0, nS1, ROW_I_J, COL_I_JP1)
            .sub(JblasUtils.setUpMatrixFromIdx(nS0, nS1, ROW_I_J, COL_IJ_1));
    DoubleMatrix S1m = S1p.neg();

    DoubleMatrix S2p =
        JblasUtils.setUpMatrixFromIdx(nS0, nS2, ROW_I_J, COL_IP1_J)
            .neg()
            .add(JblasUtils.setUpMatrixFromIdx(nS0, nS2, ROW_I_J, COL_IJ_2));
    DoubleMatrix S2m = S2p.neg();

    DoubleMatrix Aeq =
        concatHorizontally(concatHorizontally(S1p, S1m), concatHorizontally(S2p, S2m));

    final int nObs = Aeq.columns;
    final int nUnkn = Aeq.rows;

    DoubleMatrix c1 = JblasUtils.getMatrixFromRange(0, ny, 0, weight.columns, weight);
    DoubleMatrix c2 = JblasUtils.getMatrixFromRange(0, weight.rows, 0, nx, weight);

    c1.reshape(c1.length, 1);
    c2.reshape(c2.length, 1);

    DoubleMatrix cost =
        DoubleMatrix.concatVertically(
            DoubleMatrix.concatVertically(c1, c1), DoubleMatrix.concatVertically(c2, c2));

    logger.debug("Minimum network flow resolution");

    StopWatch clockLP = new StopWatch();
    LinearProgram lp = new LinearProgram(cost.data);
    lp.setMinProblem(true);

    boolean[] integerBool = new boolean[nObs];
    double[] lowerBound = new double[nObs];
    double[] upperBound = new double[nObs];

    for (int k = 0; k < nUnkn; k++) {
      lp.addConstraint(new LinearEqualsConstraint(Aeq.getRow(k).toArray(), beq.get(k), "cost"));
    }

    for (int k = 0; k < nObs; k++) {
      integerBool[k] = true;
      lowerBound[k] = 0;
      upperBound[k] = 99999;
    }

    // setup bounds and integer nature
    lp.setIsinteger(integerBool);
    lp.setUpperbound(upperBound);
    lp.setLowerbound(lowerBound);
    LinearProgramSolver solver = SolverFactory.newDefault();

    //        double[] solution;
    //        solution = solver.solve(lp);
    DoubleMatrix solution = new DoubleMatrix(solver.solve(lp));

    clockLP.stop();
    logger.debug("Total GLPK time: {} [sec]", (double) (clockLP.getElapsedTime()) / 1000);

    // Displatch the LP solution
    int offset;

    int[] idx1p = JblasUtils.intRangeIntArray(0, nS1 - 1);
    DoubleMatrix x1p = solution.get(idx1p);
    x1p.reshape(ny, nx + 1);
    offset = idx1p[nS1 - 1] + 1;

    int[] idx1m = JblasUtils.intRangeIntArray(offset, offset + nS1 - 1);
    DoubleMatrix x1m = solution.get(idx1m);
    x1m.reshape(ny, nx + 1);
    offset = idx1m[idx1m.length - 1] + 1;

    int[] idx2p = JblasUtils.intRangeIntArray(offset, offset + nS2 - 1);
    DoubleMatrix x2p = solution.get(idx2p);
    x2p.reshape(ny + 1, nx);
    offset = idx2p[idx2p.length - 1] + 1;

    int[] idx2m = JblasUtils.intRangeIntArray(offset, offset + nS2 - 1);
    DoubleMatrix x2m = solution.get(idx2m);
    x2m.reshape(ny + 1, nx);

    // Compute the derivative jumps, eqt (20,21)
    DoubleMatrix k1 = x1p.sub(x1m);
    DoubleMatrix k2 = x2p.sub(x2m);

    // (?) Round to integer solution
    if (roundK == true) {
      for (int idx = 0; idx < k1.length; idx++) {
        k1.put(idx, FastMath.floor(k1.get(idx)));
      }
      for (int idx = 0; idx < k2.length; idx++) {
        k2.put(idx, FastMath.floor(k2.get(idx)));
      }
    }

    // Sum the jumps with the wrapped partial derivatives, eqt (10,11)
    k1.reshape(ny, nx + 1);
    k2.reshape(ny + 1, nx);
    k1.addi(Psi1.div(Constants._TWO_PI));
    k2.addi(Psi2.div(Constants._TWO_PI));

    // Integrate the partial derivatives, eqt (6)
    // cumsum() method in JblasTester -> see cumsum_demo() in JblasTester.cumsum_demo()
    DoubleMatrix k2_temp = DoubleMatrix.concatHorizontally(DoubleMatrix.zeros(1), k2.getRow(0));
    k2_temp = JblasUtils.cumsum(k2_temp, 1);
    DoubleMatrix k = DoubleMatrix.concatVertically(k2_temp, k1);
    k = JblasUtils.cumsum(k, 1);

    // Unwrap - final solution
    unwrappedPhase = k.mul(Constants._TWO_PI);
  }