List of usage examples for org.apache.commons.math3.linear RealMatrixChangingVisitor RealMatrixChangingVisitor
RealMatrixChangingVisitor
From source file:com.github.tteofili.looseen.yay.RectifierFunction.java
@Override public RealMatrix applyMatrix(RealMatrix weights) { RealMatrix matrix = weights.copy();//from www . j a va 2 s . co m matrix.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return Math.max(0, value); } @Override public double end() { return 0; } }); return matrix; }
From source file:com.github.tteofili.looseen.yay.SoftmaxActivationFunction.java
public RealMatrix applyMatrix(RealMatrix weights) { RealMatrix matrix = weights.copy();/* ww w.ja v a 2s . c om*/ final double finalD = expDen(matrix); matrix.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return Math.exp(value) / finalD; } @Override public double end() { return 0; } }); return matrix; }
From source file:com.github.tteofili.looseen.yay.SGM.java
/** * perform weights learning from the training examples using (configurable) mini batch gradient descent algorithm * * @param samples the training examples//from ww w . jav a 2s. c o m * @return the final cost with the updated weights * @throws Exception if BGD fails to converge or any numerical error happens */ private double learnWeights(Sample... samples) throws Exception { int iterations = 0; double cost = Double.MAX_VALUE; int j = 0; // momentum RealMatrix vb = MatrixUtils.createRealMatrix(biases[0].getRowDimension(), biases[0].getColumnDimension()); RealMatrix vb2 = MatrixUtils.createRealMatrix(biases[1].getRowDimension(), biases[1].getColumnDimension()); RealMatrix vw = MatrixUtils.createRealMatrix(weights[0].getRowDimension(), weights[0].getColumnDimension()); RealMatrix vw2 = MatrixUtils.createRealMatrix(weights[1].getRowDimension(), weights[1].getColumnDimension()); long start = System.currentTimeMillis(); int c = 1; RealMatrix x = MatrixUtils.createRealMatrix(configuration.batchSize, samples[0].getInputs().length); RealMatrix y = MatrixUtils.createRealMatrix(configuration.batchSize, samples[0].getOutputs().length); while (true) { int i = 0; for (int k = j * configuration.batchSize; k < j * configuration.batchSize + configuration.batchSize; k++) { Sample sample = samples[k % samples.length]; x.setRow(i, sample.getInputs()); y.setRow(i, sample.getOutputs()); i++; } j++; long time = (System.currentTimeMillis() - start) / 1000; if (iterations % (1 + (configuration.maxIterations / 100)) == 0 && time > 60 * c) { c += 1; // System.out.println("cost: " + cost + ", accuracy: " + evaluate(this) + " after " + iterations + " iterations in " + (time / 60) + " minutes (" + ((double) iterations / time) + " ips)"); } RealMatrix w0t = weights[0].transpose(); RealMatrix w1t = weights[1].transpose(); RealMatrix hidden = rectifierFunction.applyMatrix(x.multiply(w0t)); hidden.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value + biases[0].getEntry(0, column); } @Override public double end() { return 0; } }); RealMatrix scores = hidden.multiply(w1t); scores.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value + biases[1].getEntry(0, column); } @Override public double end() { return 0; } }); RealMatrix probs = scores.copy(); int len = scores.getColumnDimension() - 1; for (int d = 0; d < configuration.window - 1; d++) { int startColumn = d * len / (configuration.window - 1); RealMatrix subMatrix = scores.getSubMatrix(0, scores.getRowDimension() - 1, startColumn, startColumn + x.getColumnDimension()); for (int sm = 0; sm < subMatrix.getRowDimension(); sm++) { probs.setSubMatrix(softmaxActivationFunction.applyMatrix(subMatrix.getRowMatrix(sm)).getData(), sm, startColumn); } } RealMatrix correctLogProbs = MatrixUtils.createRealMatrix(x.getRowDimension(), 1); correctLogProbs.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return -Math.log(probs.getEntry(row, getMaxIndex(y.getRow(row)))); } @Override public double end() { return 0; } }); double dataLoss = correctLogProbs.walkInOptimizedOrder(new RealMatrixPreservingVisitor() { private double d = 0; @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public void visit(int row, int column, double value) { d += value; } @Override public double end() { return d; } }) / samples.length; double reg = 0d; reg += weights[0].walkInOptimizedOrder(new RealMatrixPreservingVisitor() { private double d = 0d; @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public void visit(int row, int column, double value) { d += Math.pow(value, 2); } @Override public double end() { return d; } }); reg += weights[1].walkInOptimizedOrder(new RealMatrixPreservingVisitor() { private double d = 0d; @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public void visit(int row, int column, double value) { d += Math.pow(value, 2); } @Override public double end() { return d; } }); double regLoss = 0.5 * configuration.regularizationLambda * reg; double newCost = dataLoss + regLoss; if (iterations == 0) { // System.out.println("started with cost = " + dataLoss + " + " + regLoss + " = " + newCost); } if (Double.POSITIVE_INFINITY == newCost) { throw new Exception("failed to converge at iteration " + iterations + " with alpha " + configuration.alpha + " : cost going from " + cost + " to " + newCost); } else if (iterations > 1 && (newCost < configuration.threshold || iterations > configuration.maxIterations)) { cost = newCost; // System.out.println("successfully converged after " + (iterations - 1) + " iterations (alpha:" + configuration.alpha + ",threshold:" + configuration.threshold + ") with cost " + newCost); break; } else if (Double.isNaN(newCost)) { throw new Exception("failed to converge at iteration " + iterations + " with alpha " + configuration.alpha + " : cost calculation underflow"); } // update registered cost cost = newCost; // calculate the derivatives to update the parameters RealMatrix dscores = probs.copy(); dscores.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return (y.getEntry(row, column) == 1 ? (value - 1) : value) / samples.length; } @Override public double end() { return 0; } }); // get derivative on second layer RealMatrix dW2 = hidden.transpose().multiply(dscores); // regularize dw2 dW2.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value + configuration.regularizationLambda * w1t.getEntry(row, column); } @Override public double end() { return 0; } }); RealMatrix db2 = MatrixUtils.createRealMatrix(biases[1].getRowDimension(), biases[1].getColumnDimension()); dscores.walkInOptimizedOrder(new RealMatrixPreservingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public void visit(int row, int column, double value) { db2.setEntry(0, column, db2.getEntry(0, column) + value); } @Override public double end() { return 0; } }); RealMatrix dhidden = dscores.multiply(weights[1]); dhidden.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value < 0 ? 0 : value; } @Override public double end() { return 0; } }); RealMatrix db = MatrixUtils.createRealMatrix(biases[0].getRowDimension(), biases[0].getColumnDimension()); dhidden.walkInOptimizedOrder(new RealMatrixPreservingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public void visit(int row, int column, double value) { db.setEntry(0, column, db.getEntry(0, column) + value); } @Override public double end() { return 0; } }); // get derivative on first layer RealMatrix dW = x.transpose().multiply(dhidden); // regularize dW.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value + configuration.regularizationLambda * w0t.getEntry(row, column); } @Override public double end() { return 0; } }); RealMatrix dWt = dW.transpose(); RealMatrix dWt2 = dW2.transpose(); if (configuration.useNesterovMomentum) { // update nesterov momentum final RealMatrix vbPrev = vb.copy(); final RealMatrix vb2Prev = vb2.copy(); final RealMatrix vwPrev = vw.copy(); final RealMatrix vw2Prev = vw2.copy(); vb.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return configuration.mu * value - configuration.alpha * db.getEntry(row, column); } @Override public double end() { return 0; } }); vb2.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return configuration.mu * value - configuration.alpha * db2.getEntry(row, column); } @Override public double end() { return 0; } }); vw.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return configuration.mu * value - configuration.alpha * dWt.getEntry(row, column); } @Override public double end() { return 0; } }); vw2.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return configuration.mu * value - configuration.alpha * dWt2.getEntry(row, column); } @Override public double end() { return 0; } }); // update bias biases[0].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value - configuration.mu * vbPrev.getEntry(row, column) + (1 + configuration.mu) * vb.getEntry(row, column); } @Override public double end() { return 0; } }); biases[1].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value - configuration.mu * vb2Prev.getEntry(row, column) + (1 + configuration.mu) * vb2.getEntry(row, column); } @Override public double end() { return 0; } }); // update the weights weights[0].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value - configuration.mu * vwPrev.getEntry(row, column) + (1 + configuration.mu) * vw.getEntry(row, column); } @Override public double end() { return 0; } }); weights[1].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value - configuration.mu * vw2Prev.getEntry(row, column) + (1 + configuration.mu) * vw2.getEntry(row, column); } @Override public double end() { return 0; } }); } else if (configuration.useMomentum) { // update momentum vb.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return configuration.mu * value - configuration.alpha * db.getEntry(row, column); } @Override public double end() { return 0; } }); vb2.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return configuration.mu * value - configuration.alpha * db2.getEntry(row, column); } @Override public double end() { return 0; } }); vw.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return configuration.mu * value - configuration.alpha * dWt.getEntry(row, column); } @Override public double end() { return 0; } }); vw2.walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return configuration.mu * value - configuration.alpha * dWt2.getEntry(row, column); } @Override public double end() { return 0; } }); // update bias biases[0].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value + vb.getEntry(row, column); } @Override public double end() { return 0; } }); biases[1].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value + vb2.getEntry(row, column); } @Override public double end() { return 0; } }); // update the weights weights[0].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value + vw.getEntry(row, column); } @Override public double end() { return 0; } }); weights[1].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value + vw2.getEntry(row, column); } @Override public double end() { return 0; } }); } else { // standard parameter update // update bias biases[0].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value - configuration.alpha * db.getEntry(row, column); } @Override public double end() { return 0; } }); biases[1].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value - configuration.alpha * db2.getEntry(row, column); } @Override public double end() { return 0; } }); // update the weights weights[0].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value - configuration.alpha * dWt.getEntry(row, column); } @Override public double end() { return 0; } }); weights[1].walkInOptimizedOrder(new RealMatrixChangingVisitor() { @Override public void start(int rows, int columns, int startRow, int endRow, int startColumn, int endColumn) { } @Override public double visit(int row, int column, double value) { return value - configuration.alpha * dWt2.getEntry(row, column); } @Override public double end() { return 0; } }); } iterations++; } return cost; }