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

Example 6 with SDVariable

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

the class Sqrt method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v) {
    SDVariable out = arg();
    SDVariable g = sameDiff.pow(out, -0.5).mul(0.5).mul(i_v.get(0));
    return Arrays.asList(g);
}

Example 7 with SDVariable

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

the class OldRDivOp method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v) {
    SDVariable gradWrtX = f().div(i_v.get(0), larg());
    SDVariable gradWrtY = f().mul(f().neg(gradWrtX), f().div(rarg(), larg()));
    List<SDVariable> ret = new ArrayList<>(2);
    ret.add(gradWrtX);
    ret.add(gradWrtY);
    return ret;
}

Example 8 with SDVariable

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

the class TruncateDivOp method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v) {
    SDVariable gradWrtX = f().div(i_v.get(0), rarg());
    SDVariable gradWrtY = f().mul(f().neg(gradWrtX), f().div(larg(), rarg()));
    List<SDVariable> ret = new ArrayList<>(2);
    ret.add(gradWrtX);
    ret.add(gradWrtY);
    return ret;
}

Example 9 with SDVariable

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

the class DepthToSpace method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v) {
    // Gradient to DepthToSpace is just SpaceToDepth of same block size and data format.
    SDVariable gradient = i_v.get(0);
    SDVariable ret = sameDiff.spaceToDepth(gradient, blockSize, dataFormat);
    return Arrays.asList(ret);
}

Example 10 with SDVariable

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

the class TensorMmul method doTensorMmul.

private SDVariable doTensorMmul(SDVariable a, SDVariable b, int[][] axes) {
    int validationLength = Math.min(axes[0].length, axes[1].length);
    for (int i = 0; i < validationLength; i++) {
        if (a.getShape()[axes[0][i]] != b.getShape()[axes[1][i]])
            throw new IllegalArgumentException("Size of the given axes at each dimension must be the same size.");
        if (axes[0][i] < 0)
            axes[0][i] += a.getShape().length;
        if (axes[1][i] < 0)
            axes[1][i] += b.getShape().length;
    }
    List<Integer> listA = new ArrayList<>();
    for (int i = 0; i < a.getShape().length; i++) {
        if (!Ints.contains(axes[0], i))
            listA.add(i);
    }
    int[] newAxesA = Ints.concat(Ints.toArray(listA), axes[0]);
    List<Integer> listB = new ArrayList<>();
    for (int i = 0; i < b.getShape().length; i++) {
        if (!Ints.contains(axes[1], i))
            listB.add(i);
    }
    int[] newAxesB = Ints.concat(axes[1], Ints.toArray(listB));
    int n2 = 1;
    int aLength = Math.min(a.getShape().length, axes[0].length);
    for (int i = 0; i < aLength; i++) {
        n2 *= a.getShape()[axes[0][i]];
    }
    // if listA and listB are empty these do not initialize.
    // so initializing with {1} which will then get overridden if not empty
    int[] newShapeA = { -1, n2 };
    int[] oldShapeA;
    if (listA.size() == 0) {
        oldShapeA = new int[] { 1 };
    } else {
        oldShapeA = Ints.toArray(listA);
        for (int i = 0; i < oldShapeA.length; i++) oldShapeA[i] = a.getShape()[oldShapeA[i]];
    }
    int n3 = 1;
    int bNax = Math.min(b.getShape().length, axes[1].length);
    for (int i = 0; i < bNax; i++) {
        n3 *= b.getShape()[axes[1][i]];
    }
    int[] newShapeB = { n3, -1 };
    int[] oldShapeB;
    if (listB.size() == 0) {
        oldShapeB = new int[] { 1 };
    } else {
        oldShapeB = Ints.toArray(listB);
        for (int i = 0; i < oldShapeB.length; i++) oldShapeB[i] = b.getShape()[oldShapeB[i]];
    }
    SDVariable at = f().reshape(f().permute(a, newAxesA), newShapeA);
    SDVariable bt = f().reshape(f().permute(b, newAxesB), newShapeB);
    SDVariable ret = f().mmul(at, bt);
    int[] aPlusB = Ints.concat(oldShapeA, oldShapeB);
    return f().reshape(ret, aPlusB);
}