Java Code Examples for org.nd4j.autodiff.samediff.SDVariable#mul()
The following examples show how to use
org.nd4j.autodiff.samediff.SDVariable#mul() .
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Example 1
| Source File: CosineSimilarity.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
public static List<SDVariable> doDiff(SameDiff sameDiff, SDVariable x, SDVariable y,
SDVariable gradOut, boolean keepDims, int... dimensions){
SDVariable a = sameDiff.sum(x.mul(y),true, dimensions);
SDVariable l2x = sameDiff.norm2(x, true, dimensions);
SDVariable l2y = sameDiff.norm2(y, true, dimensions);
SDVariable b = l2x.mul(l2y);
SDVariable l2xSq = sameDiff.math().square(l2x);
SDVariable l2ySq = sameDiff.math().square(l2y);
SDVariable broadcastableGrad;
if(keepDims || dimensions == null || dimensions.length == 0 || (dimensions.length == 1 && dimensions[0] == Integer.MAX_VALUE)){
//keepDims or full array reduction
broadcastableGrad = gradOut;
} else {
broadcastableGrad = SameDiffUtils.reductionBroadcastableWithOrigShape(x, sameDiff.constant(Nd4j.createFromArray(dimensions)), gradOut);
}
SDVariable dcdx = y.sub(x.mul(a).div(l2xSq)).div(b);
SDVariable dcdy = x.sub(y.mul(a).div(l2ySq)).div(b);
return Arrays.asList(dcdx.mul(broadcastableGrad), dcdy.mul(broadcastableGrad));
}
Example 2
| Source File: Cross.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> gradients) {
/**
* dL / dx = dL / dCross * dCross / dx
* dCross(a,b) / da = Cross(1, b)
* dCross(a,b) / db = Cross(a, 1)
*
* return (grad * Cross(1, b), grad * Cross(a, 1)
*/
SDVariable grad = gradients.get(0);
SDVariable a = larg();
SDVariable b = rarg();
SDVariable ones = sameDiff.onesLike(a);
SDVariable gradLeft = grad.mul(sameDiff.math().cross(b, ones));
SDVariable gradRight = grad.mul(sameDiff.math().cross(ones, a));
return Arrays.asList(gradLeft, gradRight);
}
Example 3
| Source File: ScatterMin.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> gradOut) {
//3 args: ref, indices, updates
//For non-modified indices, input gradient (reference) is same as output gradient
//For modified indices, dL/dref = dL/dOut if(ref[index[i],j] == min) or 0 otherwise
//And for updates, dL/du = dL/dOut if(update[i,j]==min) or 0 otherwise
List<SDVariable> ret = new ArrayList<>(3);
SDVariable notModified = arg(0).eq(outputVariable()).castTo(arg(0).dataType()); //0 if modified, 1 otherwise
SDVariable refGrad = gradOut.get(0).mul(notModified);
SDVariable gatherOut = sameDiff.gather(outputVariable(), arg(1), 0);
SDVariable gatherGrad = sameDiff.gather(gradOut.get(0), arg(1), 0);
SDVariable outIsUpdate = gatherOut.eq(arg(2)).castTo(arg(2).dataType());
SDVariable updateGrad = gatherGrad.mul(outIsUpdate);
return Arrays.asList(refGrad, sameDiff.zerosLike(arg(1)), updateGrad);
}
Example 4
| Source File: ScatterMul.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> gradOut){
//3 args: ref, indices, updates
//For non-modified indices, input gradient (reference) is same as output gradient
//For modified indices, dL/dref = dL/dOut * dOut/dRef = dL/dOut * d(ref * update)/dRef = dL/dOut * update
//And for updates, dL/du = dL/dOut * dOut/du = dL/dOut * d(ref * update)/du = dL/dOut * ref
SDVariable ref = arg(0);
SDVariable indices = arg(1);
SDVariable updates = arg(2);
List<SDVariable> ret = new ArrayList<>(3);
SDVariable gradRef = sameDiff.scatterMul(gradOut.get(0), indices, updates);
ret.add(gradRef); //Reference array
ret.add(sameDiff.zerosLike(arg(1))); //Indices
SDVariable gatherOutGrad = sameDiff.gather(gradOut.get(0), indices, 0); //Updates
SDVariable gatherRef = sameDiff.gather(ref, indices, 0);
SDVariable updateGrad = gatherOutGrad.mul(gatherRef);
ret.add(updateGrad);
return ret;
}
Example 5
| Source File: Cross.java From nd4j with Apache License 2.0 | 6 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> gradients) {
/**
* dL / dx = dL / dCross * dCross / dx
* dCross(a,b) / da = Cross(1, b)
* dCross(a,b) / db = Cross(a, 1)
*
* return (grad * Cross(1, b), grad * Cross(a, 1)
*/
SDVariable grad = gradients.get(0);
SDVariable a = larg();
SDVariable b = rarg();
SDVariable ones = sameDiff.onesLike(a);
SDVariable gradLeft = grad.mul(sameDiff.cross(ones, b));
SDVariable gradRight = grad.mul(sameDiff.cross(a, ones));
return Arrays.asList(gradLeft, gradRight);
}
Example 6
| Source File: AttentionVertex.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Override
public SDVariable defineVertex(SameDiff sameDiff, Map<String, SDVariable> layerInput, Map<String, SDVariable> paramTable, Map<String, SDVariable> maskVars) {
final SDVariable queries = layerInput.get("queries");
final SDVariable keys = layerInput.get("keys");
final SDVariable values = layerInput.get("values");
final SDVariable mask = maskVars != null ? sameDiff.min(maskVars.get("keys"), maskVars.get("values")): null;
SDVariable attention;
if(projectInput){
val Wq = paramTable.get(WEIGHT_KEY_QUERY_PROJECTION);
val Wk = paramTable.get(WEIGHT_KEY_KEY_PROJECTION);
val Wv = paramTable.get(WEIGHT_KEY_VALUE_PROJECTION);
val Wo = paramTable.get(WEIGHT_KEY_OUT_PROJECTION);
attention = sameDiff.nn.multiHeadDotProductAttention(getLayerName(), queries, keys, values, Wq, Wk, Wv, Wo, mask, true);
}else{
attention = sameDiff.nn.dotProductAttention(getLayerName(), queries, keys, values, mask, true);
}
if(maskVars != null){
return attention.mul(sameDiff.expandDims(maskVars.get("queries"), 1));
}else{
return attention;
}
}
Example 7
| Source File: NormMax.java From nd4j with Apache License 2.0 | 6 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> i_v1) {
//maxnorm(in) = max_i |x_i|
//d maxnorm(in)/dx = 0 if x_i is not the max, or d|x|/dx otherwise
SDVariable absIn = sameDiff.abs(arg());
SDVariable maxnorm = outputVariables()[0];
int origRank = Shape.rankFromShape(arg().getShape()); //TODO shape may not always be defined?
SDVariable maxnormBc = f().reductionBroadcastableWithOrigShape(origRank, dimensions, maxnorm);
maxnormBc = sameDiff.onesLike(arg()).mul(maxnormBc);
SDVariable eq = sameDiff.eq(absIn, maxnormBc);
SDVariable dAbsXdX = sameDiff.sign(arg());
SDVariable dNormmaxDx = eq.mul(dAbsXdX);
SDVariable broadcastableGrad = f().reductionBroadcastableWithOrigShape(origRank, dimensions, i_v1.get(0));
SDVariable ret = dNormmaxDx.mul(broadcastableGrad);
return Arrays.asList(ret);
}
Example 8
| Source File: Tan.java From nd4j with Apache License 2.0 | 5 votes |
|
@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 9
| Source File: EuclideanDistance.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> i_v1) {
//ddist(x,y)/dxi = (xi-yi)/dist(x,y)
SDVariable euc = outputVariables()[0];
SDVariable difference = larg().sub(rarg());
SDVariable divBroadcastable = i_v1.get(0).div(euc);
if(!keepDims && !(dimensions == null || dimensions.length == 0 || (dimensions.length == 1 && dimensions[0] == Integer.MAX_VALUE))){
//Not keep dims, and not full array reduction -> need to make broadcastable
divBroadcastable = SameDiffUtils.reductionBroadcastableWithOrigShape(arg(), sameDiff.constant(Nd4j.createFromArray(dimensions)), divBroadcastable);
}
SDVariable gradX = difference.mul(divBroadcastable);
SDVariable gradY = sameDiff.math.neg(gradX);
return Arrays.asList(gradX, gradY);
}
Example 10
| Source File: Variance.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> i_v1) {
//If out = var(in) then:
//dL/dIn = dL/dOut * dOut/dIn
// with dOut/dIn = (in-mean) * 2/(n-1)
val n = f().getReductionLength(this);
int origRank = Shape.rankFromShape(arg().getShape());
SDVariable broadcastableMean = f().reductionBroadcastableWithOrigShape(origRank, dimensions, f().mean(arg(), dimensions));
SDVariable broadcastableGrad = f().reductionBroadcastableWithOrigShape(origRank, dimensions, i_v1.get(0));
SDVariable dOutdIn = arg().sub(broadcastableMean).mul(2.0 / (biasCorrected ? (n - 1) : n));
SDVariable dLdIn = dOutdIn.mul(broadcastableGrad);
return Arrays.asList(dLdIn);
}
Example 11
| Source File: SameDiffSimpleLambdaVertex.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public SDVariable defineVertex(SameDiff sameDiff, VertexInputs inputs) {
SDVariable in1 = inputs.getInput(0);
SDVariable in2 = inputs.getInput(1);
SDVariable ret = in1.mul(in2);
return ret;
}
Example 12
| Source File: Max.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
public List<SDVariable> doDiff(List<SDVariable> i_v1) {
//TODO do we need to handle the "multiple equal maximums" case?
//TODO code duplication (min/max)
SDVariable out = outputVariables()[0];
int origRank = Shape.rankFromShape(arg().getShape());
SDVariable expandedOut = sameDiff.f().reductionBroadcastableWithOrigShape(origRank, dimensions, out);
expandedOut = sameDiff.onesLike(arg()).mul(expandedOut);
SDVariable expandedGrad = sameDiff.f().reductionBroadcastableWithOrigShape(origRank, dimensions, i_v1.get(0));
SDVariable eq = sameDiff.eq(arg(), expandedOut);
SDVariable ret = eq.mul(expandedGrad);
return Arrays.asList(ret);
}
Example 13
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testScalarOps() {
int d0 = 2;
int d1 = 3;
int d2 = 4;
int n = d0 * d1 * d2;
List<String> failed = new ArrayList<>();
for (int i = 0; i < 11; i++) {
for (char inOrder : new char[]{'c', 'f'}) {
SameDiff sd = SameDiff.create();
INDArray inArr = Nd4j.linspace(1, n, n, DataType.DOUBLE).reshape('c', d0, d1, d2).dup(inOrder);
SDVariable in = sd.var("in", inArr);
TestCase tc = new TestCase(sd).gradientCheck(true);
SDVariable out;
String msg;
switch (i) {
case 0:
out = in.mul(2);
tc.expectedOutput(out.name(), inArr.mul(2));
msg = "mul - " + inOrder;
break;
case 1:
out = in.div(2);
tc.expectedOutput(out.name(), inArr.div(2));
msg = "div - " + inOrder;
break;
case 2:
out = in.add(2);
tc.expectedOutput(out.name(), inArr.add(2));
msg = "add - " + inOrder;
break;
case 3:
out = in.sub(2);
tc.expectedOutput(out.name(), inArr.sub(2));
msg = "sub - " + inOrder;
break;
case 4:
out = in.rdiv(2);
tc.expectedOutput(out.name(), inArr.rdiv(2));
msg = "rdiv - " + inOrder;
break;
case 5:
out = in.rsub(2);
tc.expectedOutput(out.name(), inArr.rsub(2));
msg = "rsub - " + inOrder;
break;
case 6:
out = sd.math().pow(in, 2);
tc.expectedOutput(out.name(), Transforms.pow(inArr, 2));
msg = "pow - " + inOrder;
break;
case 7:
inArr.assign(Nd4j.rand(inArr.dataType(), inArr.shape()).muli(5).subi(2.5));
out = sd.math().floorMod(in, 2.0);
tc.expected(out, Nd4j.getExecutioner().exec(new ScalarFMod(inArr.dup(), 2.0)));
msg = "scalarFloorMod - " + inOrder;
break;
case 8:
inArr.assign(Nd4j.rand(inArr.shape()));
out = sd.scalarMax(in, 0.5);
tc.expected(out, Transforms.max(inArr.dup(), 0.5));
msg = "scalarMax - " + inOrder;
break;
case 9:
inArr.assign(Nd4j.rand(inArr.shape()));
out = sd.scalarMin(in, 0.5);
tc.expected(out, Transforms.min(inArr.dup(), 0.5));
msg = "scalarMin - " + inOrder;
break;
case 10:
out = in.assign(0.5);
tc.expected(out, Nd4j.valueArrayOf(inArr.shape(), 0.5));
msg = "scalarSet - " + inOrder;
break;
default:
throw new RuntimeException();
}
tc.testName(msg);
SDVariable loss = sd.standardDeviation(out, true);
log.info("Starting test: " + msg);
String err = OpValidation.validate(tc, true);
if (err != null) {
failed.add(err);
}
}
}
assertEquals(failed.toString(), 0, failed.size());
}
Example 14
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast2() {
Nd4j.getRandom().setSeed(12345);
List<String> failed = new ArrayList<>();
for (int[] dim_sz1s : new int[][]{{0, 1}, {0, 2}, {1, 2}, {0, 1, 2}}) {
long[] otherShape = {3, 4, 5};
otherShape[dim_sz1s[0]] = 1;
otherShape[dim_sz1s[1]] = 1;
if (dim_sz1s.length == 3) {
otherShape[dim_sz1s[2]] = 1;
}
for (int i = 0; i < 8; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in3", DataType.DOUBLE, 3, 4, 5);
SDVariable in2 = sd.var("inToBc", DataType.DOUBLE, otherShape);
String name;
SDVariable bcOp;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
//bcOp = sd.scalarFloorDiv(in3, in2);
bcOp = new FloorDivOp(sd, in3, in2).outputVariable();
name = "floordiv";
break;
case 7:
//bcOp = sd.scalarFloorMod(in3, in2);
bcOp = new FloorModOp(sd, in3, in2).outputVariable();
name = "floormod";
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/5976
continue;
}
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", dimensions=" + Arrays.toString(dim_sz1s) + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.randn(DataType.DOUBLE, 3, 4, 5).muli(100);
INDArray in2Arr = Nd4j.randn(DataType.DOUBLE, otherShape).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
TestCase tc = new TestCase(sd);
String error = OpValidation.validate(tc);
if(error != null){
failed.add(name);
}
}
}
assertEquals("Failed: " + failed, 0, failed.size());
}
Example 15
| Source File: ReductionOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testReductionsBackwards() {
// for (int i = 0; i < 7; i++) {
int i=5;
{
SameDiff sd = SameDiff.create();
int nOut = 4;
int minibatch = 3;
SDVariable input = sd.var("in", DataType.DOUBLE, new long[]{minibatch, nOut});
SDVariable label = sd.var("label", DataType.DOUBLE, new long[]{minibatch, nOut});
SDVariable diff = input.sub(label);
SDVariable sqDiff = diff.mul(diff);
SDVariable msePerEx = sd.mean("msePerEx", sqDiff, 1);
SDVariable loss; //Scalar value
String name;
switch (i) {
case 0:
loss = sd.mean("loss", msePerEx, 0);
name = "mean";
break;
case 1:
loss = sd.sum("loss", msePerEx, 0);
name = "sum";
break;
case 2:
loss = sd.standardDeviation("loss", msePerEx, true, 0);
name = "stdev";
break;
case 3:
loss = sd.min("loss", msePerEx, 0);
name = "min";
break;
case 4:
loss = sd.max("loss", msePerEx, 0);
name = "max";
break;
case 5:
loss = sd.variance("loss", msePerEx, true, 0);
name = "variance";
break;
case 6:
loss = sd.prod("loss", msePerEx, 0);
name = "prod";
break;
default:
throw new RuntimeException();
}
String msg = "test: " + i + " - " + name;
log.info("*** Starting test: " + msg);
INDArray inputArr = Nd4j.rand(DataType.DOUBLE, minibatch, nOut);
INDArray labelArr = Nd4j.rand(DataType.DOUBLE, minibatch, nOut);
sd.associateArrayWithVariable(inputArr, input);
sd.associateArrayWithVariable(labelArr, label);
INDArray result = loss.eval();
assertEquals(1, result.length());
sd.calculateGradients(Collections.emptyMap(), sd.getVariables().keySet());
}
}
Example 16
| Source File: KerasLambdaTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
public SDVariable defineLayer(SameDiff sd, SDVariable x) { return x.mul(3); }
Example 17
| Source File: GradCheckMisc.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast1() {
Nd4j.getRandom().setSeed(12345);
List<String> allFailed = new ArrayList<>();
for (int dim_sz1 : new int[]{0, 1, 2}) {
int[] in2Shape = {3, 4, 5};
in2Shape[dim_sz1] = 1;
for (int i = 2; i < 3; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in3", Nd4j.rand(new int[]{3, 4, 5}));
SDVariable in2 = sd.var("in2", in2Shape);
SDVariable bcOp;
String name;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
bcOp = sd.f().floorDiv(in3, in2);
name = "floordiv";
break;
case 7:
bcOp = sd.f().floorMod(in3, in2);
name = "floormod";
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", dimension=" + dim_sz1 + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.randn(new int[]{3, 4, 5}).muli(100);
INDArray in2Arr = Nd4j.randn(in2Shape).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
try {
INDArray out = sd.execAndEndResult();
assertNotNull(out);
assertArrayEquals(new long[]{1, 1}, out.shape());
// System.out.println(sd.asFlatPrint());
boolean ok = GradCheckUtil.checkGradients(sd);
if (!ok) {
allFailed.add(msg);
}
} catch (Exception e) {
e.printStackTrace();
allFailed.add(msg + " - EXCEPTION");
}
}
}
assertEquals("Failed: " + allFailed, 0, allFailed.size());
}
Example 18
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast3() {
//These tests: output size > input sizes
Nd4j.getRandom().setSeed(12345);
List<String> failed = new ArrayList<>();
//Test cases: in1Shape, in2Shape, shapeOf(op(in1,in2))
List<Triple<long[], long[], long[]>> testCases = new ArrayList<>();
testCases.add(new Triple<>(new long[]{3, 1}, new long[]{1, 4}, new long[]{3, 4}));
testCases.add(new Triple<>(new long[]{3, 1}, new long[]{3, 4}, new long[]{3, 4}));
testCases.add(new Triple<>(new long[]{3, 4}, new long[]{1, 4}, new long[]{3, 4}));
testCases.add(new Triple<>(new long[]{3, 4, 1}, new long[]{1, 1, 5}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 4, 1}, new long[]{3, 1, 5}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 1, 5}, new long[]{1, 4, 1}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 1, 5}, new long[]{1, 4, 5}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 1, 5}, new long[]{3, 4, 5}, new long[]{3, 4, 5}));
testCases.add(new Triple<>(new long[]{3, 1, 1, 1}, new long[]{1, 4, 5, 6}, new long[]{3, 4, 5, 6}));
testCases.add(new Triple<>(new long[]{1, 1, 1, 6}, new long[]{3, 4, 5, 6}, new long[]{3, 4, 5, 6}));
testCases.add(new Triple<>(new long[]{1, 4, 5, 1}, new long[]{3, 1, 1, 6}, new long[]{3, 4, 5, 6}));
if(!OpValidationSuite.IGNORE_FAILING) {
testCases.add(new Triple<>(new long[]{1, 6}, new long[]{3, 4, 5, 1}, new long[]{3, 4, 5, 6}));
}
for (val p : testCases) {
for (int i = 0; i < 8; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in1", DataType.DOUBLE, p.getFirst());
SDVariable in2 = sd.var("in2", DataType.DOUBLE, p.getSecond());
String name;
SDVariable bcOp;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
//bcOp = sd.scalarFloorDiv(in3, in2);
bcOp = new FloorDivOp(sd, in3, in2).outputVariable();
name = "floordiv";
break;
case 7:
//bcOp = sd.scalarFloorMod(in3, in2);
bcOp = new FloorModOp(sd, in3, in2).outputVariable();
name = "floormod";
if(OpValidationSuite.IGNORE_FAILING){
//https://github.com/deeplearning4j/deeplearning4j/issues/5976
continue;
}
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", array 1 size =" + Arrays.toString(p.getFirst())
+ ", array 2 size = " + Arrays.toString(p.getSecond()) + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.rand(DataType.DOUBLE, p.getFirst()).muli(100);
INDArray in2Arr = Nd4j.rand(DataType.DOUBLE, p.getSecond()).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
TestCase tc = new TestCase(sd);
String error = OpValidation.validate(tc);
if(error != null){
failed.add(name + " " + i + " - " + error);
}
}
}
assertEquals("Failed: " + failed, 0, failed.size());
}
Example 19
| Source File: GradCheckReductions.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testReductionGradients1() {
//Test reductions: final, but *not* the only function
Nd4j.getRandom().setSeed(12345);
List<String> allFailed = new ArrayList<>();
for (int dim : new int[]{0, Integer.MAX_VALUE}) { //These two cases are equivalent here
for (int i = 0; i < 10; i++) {
SameDiff sd = SameDiff.create();
int nOut = 4;
int minibatch = 10;
SDVariable input = sd.var("in", new int[]{-1, nOut});
SDVariable label = sd.var("label", new int[]{-1, nOut});
SDVariable diff = input.sub(label);
SDVariable sqDiff = diff.mul(diff);
SDVariable msePerEx = sd.mean("msePerEx", sqDiff, 1);
SDVariable loss;
String name;
switch (i) {
case 0:
loss = sd.mean("loss", msePerEx, dim);
name = "mean";
break;
case 1:
loss = sd.sum("loss", msePerEx, dim);
name = "sum";
break;
case 2:
loss = sd.standardDeviation("loss", msePerEx, true, dim);
name = "stdev";
break;
case 3:
loss = sd.min("loss", msePerEx, dim);
name = "min";
break;
case 4:
loss = sd.max("loss", msePerEx, dim);
name = "max";
break;
case 5:
loss = sd.variance("loss", msePerEx, true, dim);
name = "variance";
break;
case 6:
loss = sd.prod("loss", msePerEx, dim);
name = "prod";
break;
case 7:
loss = sd.norm1("loss", msePerEx, dim);
name = "norm1";
break;
case 8:
loss = sd.norm2("loss", msePerEx, dim);
name = "norm2";
break;
case 9:
loss = sd.normmax("loss", msePerEx, dim);
name = "normmax";
break;
default:
throw new RuntimeException();
}
String msg = "(test " + i + " - " + name + ", dimension=" + dim + ")";
log.info("*** Starting test: " + msg);
INDArray inputArr = Nd4j.randn(minibatch, nOut).muli(100);
INDArray labelArr = Nd4j.randn(minibatch, nOut).muli(100);
sd.associateArrayWithVariable(inputArr, input);
sd.associateArrayWithVariable(labelArr, label);
try {
INDArray out = sd.execAndEndResult();
assertNotNull(out);
assertArrayEquals(new int[]{1, 1}, out.shape());
// System.out.println(sd.asFlatPrint());
boolean ok = GradCheckUtil.checkGradients(sd);
if (!ok) {
allFailed.add(msg);
}
} catch (Exception e) {
e.printStackTrace();
allFailed.add(msg + " - EXCEPTION");
}
}
}
assertEquals("Failed: " + allFailed, 0, allFailed.size());
}
Example 20
| Source File: GradCheckMisc.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast2() {
Nd4j.getRandom().setSeed(12345);
List<String> allFailed = new ArrayList<>();
for (int[] dim_sz1s : new int[][]{{0, 1}, {0, 2}, {1, 2}, {0,1,2}}) {
int[] otherShape = {3, 4, 5};
otherShape[dim_sz1s[0]] = 1;
otherShape[dim_sz1s[1]] = 1;
if(dim_sz1s.length == 3){
otherShape[dim_sz1s[2]] = 1;
}
for (int i = 0; i < 6; i++) {
SameDiff sd = SameDiff.create();
SDVariable in3 = sd.var("in3", new int[]{3, 4, 5});
SDVariable in2 = sd.var("inToBc", otherShape);
String name;
SDVariable bcOp;
switch (i) {
case 0:
bcOp = in3.add(in2);
name = "add";
break;
case 1:
bcOp = in3.sub(in2);
name = "sub";
break;
case 2:
bcOp = in3.mul(in2);
name = "mul";
break;
case 3:
bcOp = in3.div(in2);
name = "div";
break;
case 4:
bcOp = in3.rsub(in2);
name = "rsub";
break;
case 5:
bcOp = in3.rdiv(in2);
name = "rdiv";
break;
case 6:
bcOp = sd.f().floorDiv(in3, in2);
name = "floordiv";
break;
case 7:
bcOp = sd.f().floorMod(in3, in2);
name = "floormod";
break;
default:
throw new RuntimeException();
}
SDVariable outVar = sd.sum(bcOp);
String msg = "(test " + i + ": " + name + ", dimensions=" + Arrays.toString(dim_sz1s) + ")";
log.info("*** Starting test: " + msg);
INDArray in3Arr = Nd4j.randn(new int[]{3,4,5}).muli(100);
INDArray in2Arr = Nd4j.randn(otherShape).muli(100);
sd.associateArrayWithVariable(in3Arr, in3);
sd.associateArrayWithVariable(in2Arr, in2);
try {
INDArray out = sd.execAndEndResult();
assertNotNull(out);
assertArrayEquals(new long[]{1, 1}, out.shape());
// System.out.println(sd.asFlatPrint());
boolean ok = GradCheckUtil.checkGradients(sd);
if (!ok) {
allFailed.add(msg);
}
} catch (Exception e) {
e.printStackTrace();
allFailed.add(msg + " - EXCEPTION");
}
}
}
assertEquals("Failed: " + allFailed, 0, allFailed.size());
}
