Examples with SDVariable org.nd4j.autodiff.samediff.SDVariable used on opensource projects

Example 51 with SDVariable

use of org.nd4j.autodiff.samediff.SDVariable in project nd4j by deeplearning4j.

the class ATan method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v) {
    // d(atan(x))/dx = 1/(x^2+1)
    SDVariable xSqPlus1 = f().square(arg()).add(1.0);
    SDVariable ret = xSqPlus1.rdiv(1.0).mul(i_v.get(0));
    return Arrays.asList(ret);
}

Example 52 with SDVariable

use of org.nd4j.autodiff.samediff.SDVariable in project nd4j by deeplearning4j.

the class ATanh method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v) {
    // d(atanh(x))/dx = 1 / (1-x^2)
    SDVariable oneMinusX2 = sameDiff.square(arg()).rsub(1.0);
    SDVariable ret = oneMinusX2.rdiv(1.0).mul(i_v.get(0));
    return Arrays.asList(ret);
}

Example 53 with SDVariable

use of org.nd4j.autodiff.samediff.SDVariable in project nd4j by deeplearning4j.

the class Log method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v) {
    f().validateDifferentialFunctionsameDiff(arg());
    SDVariable toInverse = sameDiff.setupFunction(f().div(i_v.get(0), arg()));
    return Arrays.asList(toInverse);
}

Example 54 with SDVariable

use of org.nd4j.autodiff.samediff.SDVariable in project nd4j by deeplearning4j.

the class Tan method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v) {
    // d(tan(x))/dx = (sec(x))^2 = 1 / (cos(x))^2
    SDVariable oneDivCos2 = sameDiff.square(sameDiff.cos(arg())).rdiv(1.0);
    SDVariable ret = oneDivCos2.mul(i_v.get(0));
    return Arrays.asList(ret);
}

Example 55 with SDVariable

use of org.nd4j.autodiff.samediff.SDVariable in project nd4j by deeplearning4j.

the class ClipByNorm method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> grad) {
    // dOut/dIn is ??? if clipped, 1 otherwise
    int origRank = Shape.rankFromShape(arg().getShape());
    SDVariable l2norm = f().norm2(arg(), dimensions);
    SDVariable broadcastableNorm = f().reductionBroadcastableWithOrigShape(origRank, dimensions, l2norm);
    SDVariable isClippedBC = f().gte(broadcastableNorm, clipValue);
    SDVariable notClippedBC = isClippedBC.rsub(1.0);
    // SDVariable dnormdx = arg().div(broadcastableNorm);
    // SDVariable sqNorm = f().square(broadcastableNorm);
    // SDVariable dOutdInClipped = sqNorm.rdiv(-1).mul(dnormdx).mul(arg()) //-1/(norm2(x))^2 * x/norm2(x)
    // .add(broadcastableNorm.rdiv(1.0))
    // .mul(clipValue);
    SDVariable dOutdInClipped = // -x^2/(norm2(x))^3
    f().neg(f().square(arg()).div(f().cube(broadcastableNorm))).add(// + 1/norm(x)
    broadcastableNorm.rdiv(1.0)).mul(clipValue).mul(isClippedBC);
    SDVariable ret = notClippedBC.add(dOutdInClipped).mul(grad.get(0));
    return Arrays.asList(ret);
}