Java Code Examples for org.nd4j.autodiff.samediff.SDVariable#sub()
The following examples show how to use
org.nd4j.autodiff.samediff.SDVariable#sub() .
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Example 1
| Source File: LossFunctions.java From nd4j with Apache License 2.0 | 6 votes |
|
/**
* Mean squared error: L = mean( (predicted - label)^2)
*
* @param outputName Name of the output SDVariable
* @param predictions Predictions variable
* @param label Label variable
* @param weights Weights array. May be null, or any broadcastable shape (with predictions/label arrays).
* Note that this is also used for masking (weight of 0 = 'masked out')
* @param reduction Type of reduction to perform for the loss function
* @param dimensions Dimension(s) to apply the loss function on
* @return LossInfo - bean with variables etc for the loss function
*/
public static LossInfo mse(String outputName, SDVariable predictions, SDVariable label, SDVariable weights,
Reduction reduction, int... dimensions){
LossInfo.Builder b = validate("mse", predictions, label, reduction);
SameDiff sd = predictions.getSameDiff();
if(weights == null){
weights = sd.one("mse_loss_weights", SCALAR);
}
SDVariable diff = predictions.sub(label);
String name = (reduction == Reduction.NONE ? outputName : null);
SDVariable preReduceLoss = sd.square(diff).mul(name, weights);
return doReduce(sd, outputName, true, b, reduction, preReduceLoss, label, weights, dimensions);
}
Example 2
| Source File: LossFunctions.java From nd4j with Apache License 2.0 | 6 votes |
|
/**
* L2 loss function: i.e., sum of squared errors, L = sum_i (actual_i - predicted)^2
*
* @param outputName
* @param predictions
* @param label
* @param weights
* @param reduction
* @param dimensions
* @return
*/
public static LossInfo l2(String outputName, SDVariable predictions, SDVariable label, SDVariable weights,
Reduction reduction, int... dimensions){
LossInfo.Builder b = validate("l2", predictions, label, reduction);
SameDiff sd = predictions.getSameDiff();
if(weights == null){
weights = sd.one("l2_loss_weights", SCALAR);
}
SDVariable diff = predictions.sub(label);
String name = (reduction == Reduction.NONE ? outputName : null);
SDVariable preReduceLoss = sd.square(diff).mul(name, weights);
return doReduce(sd, outputName, false, b, reduction, preReduceLoss, label, weights, dimensions);
}
Example 3
| Source File: TestSessions.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testInferenceSessionBasic2(){
//So far: trivial test to check execution order
SameDiff sd = SameDiff.create();
SDVariable ph1 = sd.placeHolder("x", DataType.FLOAT, 3,3);
SDVariable ph2 = sd.placeHolder("y", DataType.FLOAT, 3,3);
SDVariable a = ph1.add("a", ph2);
SDVariable b = ph1.mmul("b", ph2);
SDVariable c = ph1.sub("c", ph2);
SDVariable d = a.add("d", b);
//To get array d - need to execute: a, b, d - NOT the sub op (c)
//NOTE: normally sessions are internal and completely hidden from users
InferenceSession is = new InferenceSession(sd);
INDArray x = Nd4j.linspace(1, 9, 9).castTo(DataType.FLOAT).reshape(3,3);
INDArray y = Nd4j.linspace(0.0, 0.9, 9, DataType.DOUBLE).castTo(DataType.FLOAT).reshape(3,3);
INDArray aExp = x.add(y);
INDArray bExp = x.mmul(y);
INDArray dExp = aExp.add(bExp);
Map<String,INDArray> m = new HashMap<>();
m.put("x", x);
m.put("y", y);
Map<String,INDArray> outMap = is.output(Collections.singletonList("d"), m, null,
Collections.<String>emptyList(), null, At.defaultAt(Operation.TRAINING));
assertEquals(1, outMap.size());
assertEquals(dExp, outMap.get("d"));
}
Example 4
| Source File: SameDiffMSEOutputLayer.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public SDVariable defineLayer(SameDiff sameDiff, SDVariable layerInput, SDVariable labels, Map<String, SDVariable> paramTable) {
SDVariable z = sameDiff.mmul(layerInput, paramTable.get("W")).add(paramTable.get("b"));
SDVariable out = activation.asSameDiff("out", sameDiff, z);
//MSE: 1/nOut * (input-labels)^2
SDVariable diff = out.sub(labels);
return diff.mul(diff).mean(1).sum();
}
Example 5
| 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 6
| 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());
}
Example 7
| 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 8
| 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 9
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testGradientAutoBroadcast1() {
Nd4j.getRandom().setSeed(12345);
List<String> failed = new ArrayList<>();
for (int dim_sz1 : new int[]{0, 1, 2}) {
int[] in2Shape = {3, 4, 5};
in2Shape[dim_sz1] = 1;
for (int i = 0; i < 8; 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.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 + ", 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);
TestCase tc = new TestCase(sd);
String error = OpValidation.validate(tc);
if(error != null){
failed.add(name);
}
}
}
assertEquals("Failed: " + failed, 0, failed.size());
}
Example 10
| 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 11
| 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 12
| 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 13
| Source File: SameDiffMSELossLayer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
public SDVariable defineLayer(SameDiff sameDiff, SDVariable layerInput, SDVariable labels, Map<String, SDVariable> paramTable) {
//MSE: 1/nOut * (input-labels)^2
SDVariable diff = layerInput.sub(labels);
return diff.mul(diff).mean(1).sum(0);
}
