Examples with Sqrt org.nd4j.linalg.api.ops.impl.transforms.Sqrt used on opensource projects

Example 1 with Sqrt

use of org.nd4j.linalg.api.ops.impl.transforms.Sqrt in project nd4j by deeplearning4j.

the class CrashTest method op.

protected void op(INDArray x, INDArray y, int i) {
    // broadcast along row & column
    INDArray row = Nd4j.ones(64);
    INDArray column = Nd4j.ones(1024, 1);
    x.addiRowVector(row);
    x.addiColumnVector(column);
    // casual scalar
    x.addi(i * 2);
    // reduction along all dimensions
    float sum = x.sumNumber().floatValue();
    // index reduction
    Nd4j.getExecutioner().exec(new IMax(x), Integer.MAX_VALUE);
    // casual transform
    Nd4j.getExecutioner().exec(new Sqrt(x, x));
    // dup
    INDArray x1 = x.dup(x.ordering());
    INDArray x2 = x.dup(x.ordering());
    INDArray x3 = x.dup('c');
    INDArray x4 = x.dup('f');
    // vstack && hstack
    INDArray vstack = Nd4j.vstack(x, x1, x2, x3, x4);
    INDArray hstack = Nd4j.hstack(x, x1, x2, x3, x4);
    // reduce3 call
    Nd4j.getExecutioner().exec(new ManhattanDistance(x, x2));
    // flatten call
    INDArray flat = Nd4j.toFlattened(x, x1, x2, x3, x4);
    // reduction along dimension: row & column
    INDArray max_0 = x.max(0);
    INDArray max_1 = x.max(1);
    // index reduction along dimension: row & column
    INDArray imax_0 = Nd4j.argMax(x, 0);
    INDArray imax_1 = Nd4j.argMax(x, 1);
    // logisoftmax, softmax & softmax derivative
    Nd4j.getExecutioner().exec(new OldSoftMax(x));
    Nd4j.getExecutioner().exec(new SoftMaxDerivative(x));
    Nd4j.getExecutioner().exec(new LogSoftMax(x));
    // BooleanIndexing
    BooleanIndexing.replaceWhere(x, 5f, Conditions.lessThan(8f));
    // assing on view
    BooleanIndexing.assignIf(x, x1, Conditions.greaterThan(-1000000000f));
    // std var along all dimensions
    float std = x.stdNumber().floatValue();
    // std var along row & col
    INDArray xStd_0 = x.std(0);
    INDArray xStd_1 = x.std(1);
    // blas call
    float dot = (float) Nd4j.getBlasWrapper().dot(x, x1);
    // mmul
    for (boolean tA : paramsA) {
        for (boolean tB : paramsB) {
            INDArray xT = tA ? x.dup() : x.dup().transpose();
            INDArray yT = tB ? y.dup() : y.dup().transpose();
            Nd4j.gemm(xT, yT, tA, tB);
        }
    }
    // specially for views, checking here without dup and rollover
    Nd4j.gemm(x, y, false, false);
    System.out.println("Iteration passed: " + i);
}