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

Example 16 with SDVariable

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

the class ManhattanDistance method doDiff.

@Override
public List<SDVariable> doDiff(List<SDVariable> i_v1) {
    // ddist(x,y)/dxi = sign(xi-yi)
    SDVariable difference = larg().sub(rarg());
    SDVariable gradBroadcastable;
    // TODO shape may not always be defined?
    int origRank = Shape.rankFromShape(arg().getShape());
    if (!(dimensions.length == 1 && dimensions[0] == Integer.MAX_VALUE)) {
        // 1x1 output case
        gradBroadcastable = i_v1.get(0);
    } else {
        gradBroadcastable = f().reductionBroadcastableWithOrigShape(origRank, dimensions, i_v1.get(0));
    }
    SDVariable gradX = sameDiff.sign(difference).mul(gradBroadcastable);
    SDVariable gradY = f().neg(gradX);
    return Arrays.asList(gradX, gradY);
}

Example 17 with SDVariable

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

the class BaseOp method outputVariables.

@Override
public SDVariable[] outputVariables(String baseName) {
    if (zVertexId == null) {
        val outputNames = sameDiff.getOutputsForFunction(this);
        // no need to dynamically create if already exists
        if (outputNames != null) {
            zVertexId = sameDiff.getVariable(outputNames[0]).getVarName();
            return new SDVariable[] { sameDiff.getVariable(outputNames[0]) };
        }
        if (isInPlace()) {
            val newVars = sameDiff.generateOutputVariableForOp(this, null);
            val inputArr = x();
            // in place op
            if (inputArr == null) {
                return newVars;
            }
            sameDiff.putArrayForVarName(newVars[0].getVarName(), inputArr);
            z = inputArr;
            if (sameDiff.getOutputsForFunction(this) == null)
                sameDiff.addOutgoingFor(newVars, this);
            return newVars;
        }
        val newVars = sameDiff.generateOutputVariableForOp(this, null);
        INDArray arr = null;
        if (newVars == null || newVars.length < 1 || newVars[0].getShape() == null) {
            arr = null;
        } else if (newVars[0].getArr() == null) {
            arr = newVars[0].storeAndAllocateNewArray();
        } else
            arr = newVars[0].getArr();
        if (arr == null) {
            val shapes = calculateOutputShape();
            if (shapes != null && !shapes.isEmpty() && shapes.get(0) != null) {
                sameDiff.putShapeForVarName(newVars[0].getVarName(), shapes.get(0));
                arr = newVars[0].storeAndAllocateNewArray();
            }
        }
        z = arr;
        if (sameDiff.getOutputsForFunction(this) == null)
            sameDiff.addOutgoingFor(newVars, this);
        return newVars;
    }
    return new SDVariable[] { sameDiff.getVariable(zVertexId) };
}

Example 18 with SDVariable

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

the class OnnxGraphMapper method mapNodeType.

@Override
public void mapNodeType(OnnxProto3.NodeProto tfNode, ImportState<OnnxProto3.GraphProto, onnx.OnnxProto3.TypeProto.Tensor> importState) {
    val differentialFunction = DifferentialFunctionClassHolder.getInstance().getOpWithOnnxName(tfNode.getOpType());
    if (differentialFunction == null) {
        throw new NoOpNameFoundException("No op name found " + tfNode.getOpType());
    }
    val diff = importState.getSameDiff();
    val idx = importState.getGraph().getNodeList().indexOf(tfNode);
    val name = !tfNode.getName().isEmpty() ? tfNode.getName() : String.valueOf(idx);
    try {
        val newInstance = differentialFunction.getClass().newInstance();
        val args = new SDVariable[tfNode.getInputCount()];
        newInstance.setSameDiff(importState.getSameDiff());
        newInstance.initFromOnnx(tfNode, diff, getAttrMap(tfNode), importState.getGraph());
        importState.getSameDiff().putFunctionForId(newInstance.getOwnName(), newInstance);
        // ensure we can track node name to function instance later.
        diff.setBaseNameForFunctionInstanceId(tfNode.getName(), newInstance);
        diff.addVarNameForImport(tfNode.getName());
    } catch (Exception e) {
        e.printStackTrace();
    }
}

Example 19 with SDVariable

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

the class DifferentialFunction method diff.

/**
 * Perform automatic differentiation
 * wrt the input variables
 * @param i_v1 the input variables
 * @return the differentiated output
 * wrt each input variable
 */
public List<SDVariable> diff(List<SDVariable> i_v1) {
    List<SDVariable> vals = doDiff(i_v1);
    if (vals == null) {
        throw new IllegalStateException("Error executing diff operation: doDiff returned null for op: " + this.opName());
    }
    val outputVars = args();
    for (int i = 0; i < vals.size(); i++) {
        SDVariable var = outputVars[i];
        SDVariable grad = var.getGradient();
        if (grad != null) {
            SDVariable gradVar = f().add(grad, vals.get(i));
            try {
                vals.set(i, gradVar);
            } catch (UnsupportedOperationException e) {
                throw new UnsupportedOperationException("Use a mutable list when returning values from " + this.getClass().getSimpleName() + ".doDiff (e.g. Arrays.asList instead of Collections.singletonList)", e);
            }
            sameDiff.setGradientForVariableName(var.getVarName(), gradVar);
        } else {
            SDVariable gradVar = vals.get(i);
            sameDiff.updateVariableNameAndReference(gradVar, var.getVarName() + "-grad");
            sameDiff.setGradientForVariableName(var.getVarName(), gradVar);
            sameDiff.setForwardVariableForVarName(gradVar.getVarName(), var);
        }
    }
    return vals;
}

Example 20 with SDVariable

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

the class DifferentialFunction method getZ.

@JsonIgnore
private INDArray getZ() {
    if (isInPlace())
        return getX();
    SDVariable opId = outputVariables()[0];
    INDArray ret = opId.getArr();
    return ret;
}