Java Code Examples for org.nd4j.linalg.api.ndarray.INDArray#rsubi()
The following examples show how to use
org.nd4j.linalg.api.ndarray.INDArray#rsubi() .
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Example 1
| Source File: LossL2.java From nd4j with Apache License 2.0 | 6 votes |
|
protected INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if (labels.size(1) != preOutput.size(1)) {
throw new IllegalArgumentException(
"Labels array numColumns (size(1) = " + labels.size(1) + ") does not match output layer"
+ " number of outputs (nOut = " + preOutput.size(1) + ") ");
}
INDArray output = activationFn.getActivation(preOutput.dup(), true);
INDArray scoreArr = output.rsubi(labels);
scoreArr = scoreArr.muli(scoreArr);
//Weighted loss function
if (weights != null) {
if (weights.length() != output.size(1)) {
throw new IllegalStateException("Weights vector (length " + weights.length()
+ ") does not match output.size(1)=" + output.size(1));
}
scoreArr.muliRowVector(weights);
}
//Loss function with masking
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr;
}
Example 2
| Source File: LossL2.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
protected INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
INDArray output = activationFn.getActivation(preOutput.dup(), true);
INDArray scoreArr = output.rsubi(labels);
scoreArr = scoreArr.muli(scoreArr);
//Weighted loss function
if (weights != null) {
if (weights.length() != output.size(1)) {
throw new IllegalStateException("Weights vector (length " + weights.length()
+ ") does not match output.size(1)=" + output.size(1));
}
scoreArr.muliRowVector(weights.castTo(scoreArr.dataType()));
}
//Loss function with masking
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr;
}
Example 3
| Source File: LossHinge.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
public INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
/* y_hat is -1 or 1
hinge loss is max(0,1-y_hat*y)
*/
INDArray output = activationFn.getActivation(preOutput.dup(), true);
INDArray scoreArr = output.muli(labels); //y*yhat
scoreArr.rsubi(1.0); //1 - y*yhat
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr; // 1 - y*yhat
}
Example 4
| Source File: LossSquaredHinge.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
public INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
/* y_hat is -1 or 1
hinge loss is max(0,1-y_hat*y)
*/
INDArray output = activationFn.getActivation(preOutput.dup(), true);
INDArray scoreArr = output.muli(labels); //y*yhat
scoreArr.rsubi(1.0); //1 - y*yhat
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr; // 1 - y*yhat
}
Example 5
| Source File: LossPoisson.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Override
public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
INDArray yHat = activationFn.getActivation(preOutput.dup(), true);
INDArray yDivyhat = labels.div(yHat);
INDArray dLda = yDivyhat.rsubi(1);
if (mask != null && LossUtil.isPerOutputMasking(dLda, mask)) {
//For *most* activation functions: we don't actually need to mask dL/da in addition to masking dL/dz later
//but: some, like softmax, require both (due to dL/dz_i being a function of dL/da_j, for i != j)
//We could add a special case for softmax (activationFn instanceof ActivationSoftmax) but that would be
// error prone - though buy us a tiny bit of performance
LossUtil.applyMask(dLda, mask);
}
INDArray gradients = activationFn.backprop(preOutput, dLda).getFirst(); //TODO activation functions with params
if (mask != null) {
LossUtil.applyMask(gradients, mask);
}
return gradients;
}
Example 6
| Source File: LossHinge.java From nd4j with Apache License 2.0 | 6 votes |
|
public INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if (labels.size(1) != preOutput.size(1)) {
throw new IllegalArgumentException(
"Labels array numColumns (size(1) = " + labels.size(1) + ") does not match output layer"
+ " number of outputs (nOut = " + preOutput.size(1) + ") ");
}
/* y_hat is -1 or 1
hinge loss is max(0,1-y_hat*y)
*/
//INDArray output = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(activationFn, preOutput.dup()));
INDArray output = activationFn.getActivation(preOutput.dup(), true);
INDArray scoreArr = output.muli(labels); //y*yhat
scoreArr.rsubi(1.0); //1 - y*yhat
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr; // 1 - y*yhat
}
Example 7
| Source File: LossSquaredHinge.java From nd4j with Apache License 2.0 | 6 votes |
|
public INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if (labels.size(1) != preOutput.size(1)) {
throw new IllegalArgumentException(
"Labels array numColumns (size(1) = " + labels.size(1) + ") does not match output layer"
+ " number of outputs (nOut = " + preOutput.size(1) + ") ");
}
/* y_hat is -1 or 1
hinge loss is max(0,1-y_hat*y)
*/
INDArray output = activationFn.getActivation(preOutput.dup(), true);
INDArray scoreArr = output.muli(labels); //y*yhat
scoreArr.rsubi(1.0); //1 - y*yhat
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr; // 1 - y*yhat
}
Example 8
| Source File: NDArrayTestsFortran.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testRSubi() {
INDArray n2 = Nd4j.ones(2);
INDArray n2Assertion = Nd4j.zeros(2);
INDArray nRsubi = n2.rsubi(1);
assertEquals(n2Assertion, nRsubi);
}
Example 9
| Source File: LossPoisson.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if (labels.size(1) != preOutput.size(1)) {
throw new IllegalArgumentException(
"Labels array numColumns (size(1) = " + labels.size(1) + ") does not match output layer"
+ " number of outputs (nOut = " + preOutput.size(1) + ") ");
}
INDArray yHat = activationFn.getActivation(preOutput.dup(), true);
INDArray yDivyhat = labels.div(yHat);
INDArray dLda = yDivyhat.rsubi(1);
if (mask != null && LossUtil.isPerOutputMasking(dLda, mask)) {
//For *most* activation functions: we don't actually need to mask dL/da in addition to masking dL/dz later
//but: some, like softmax, require both (due to dL/dz_i being a function of dL/da_j, for i != j)
//We could add a special case for softmax (activationFn instanceof ActivationSoftmax) but that would be
// error prone - though buy us a tiny bit of performance
LossUtil.applyMask(dLda, mask);
}
INDArray gradients = activationFn.backprop(preOutput, dLda).getFirst(); //TODO activation functions with params
if (mask != null) {
LossUtil.applyMask(gradients, mask);
}
return gradients;
}
Example 10
| Source File: NDArrayTestsFortran.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testRSubi() {
INDArray n2 = Nd4j.ones(2);
INDArray n2Assertion = Nd4j.zeros(2);
INDArray nRsubi = n2.rsubi(1);
assertEquals(n2Assertion, nRsubi);
}
Example 11
| Source File: CudaScalarsTests.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testPinnedScalarRSub() throws Exception {
// simple way to stop test if we're not on CUDA backend here
assertEquals("JcublasLevel1", Nd4j.getBlasWrapper().level1().getClass().getSimpleName());
INDArray array1 = Nd4j.create(new float[]{1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f, 1.01f});
INDArray array2 = Nd4j.create(new float[]{2.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f});
array2.rsubi(0.5f);
System.out.println("RSub result: " + array2.getFloat(0));
assertEquals(-1.5f, array2.getFloat(0), 0.01f);
}
Example 12
| Source File: LossBinaryXENT.java From nd4j with Apache License 2.0 | 4 votes |
|
private INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if (labels.size(1) != preOutput.size(1)) {
throw new IllegalArgumentException(
"Labels array numColumns (size(1) = " + labels.size(1) + ") does not match output layer"
+ " number of outputs (nOut = " + preOutput.size(1) + ") ");
}
INDArray scoreArr;
if (activationFn instanceof ActivationSoftmax) {
//Use LogSoftMax op to avoid numerical issues when calculating score
INDArray logsoftmax = Nd4j.getExecutioner().execAndReturn(new LogSoftMax(preOutput.dup()));
scoreArr = logsoftmax.muli(labels);
} else {
//INDArray output = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(activationFn, preOutput.dup()));
INDArray output = activationFn.getActivation(preOutput.dup(), true);
if (clipEps > 0.0) {
CustomOp op = DynamicCustomOp.builder("clipbyvalue")
.addInputs(output)
.callInplace(true)
.addFloatingPointArguments(clipEps, 1.0-clipEps)
.build();
Nd4j.getExecutioner().exec(op);
}
scoreArr = Transforms.log(output, true).muli(labels);
INDArray secondTerm = output.rsubi(1);
Transforms.log(secondTerm, false);
secondTerm.muli(labels.rsub(1));
scoreArr.addi(secondTerm);
}
//Weighted loss function
if (weights != null) {
if (weights.length() != preOutput.size(1)) {
throw new IllegalStateException("Weights vector (length " + weights.length()
+ ") does not match output.size(1)=" + preOutput.size(1));
}
scoreArr.muliRowVector(weights);
}
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr;
}
Example 13
| Source File: NDArrayColumnsMathOpTransform.java From DataVec with Apache License 2.0 | 4 votes |
|
@Override
protected Writable doOp(Writable... input) {
INDArray out = ((NDArrayWritable) input[0]).get().dup();
switch (mathOp) {
case Add:
for (int i = 1; i < input.length; i++) {
out.addi(((NDArrayWritable) input[i]).get());
}
break;
case Subtract:
out.subi(((NDArrayWritable) input[1]).get());
break;
case Multiply:
for (int i = 1; i < input.length; i++) {
out.muli(((NDArrayWritable) input[i]).get());
}
break;
case Divide:
out.divi(((NDArrayWritable) input[1]).get());
break;
case ReverseSubtract:
out.rsubi(((NDArrayWritable) input[1]).get());
break;
case ReverseDivide:
out.rdivi(((NDArrayWritable) input[1]).get());
break;
case Modulus:
case ScalarMin:
case ScalarMax:
throw new IllegalArgumentException(
"Invalid MathOp: cannot use " + mathOp + " with NDArrayColumnsMathOpTransform");
default:
throw new RuntimeException("Unknown MathOp: " + mathOp);
}
//To avoid threading issues...
Nd4j.getExecutioner().commit();
return new NDArrayWritable(out);
}
Example 14
| Source File: NDArrayColumnsMathOpTransform.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
protected Writable doOp(Writable... input) {
INDArray out = ((NDArrayWritable) input[0]).get().dup();
switch (mathOp) {
case Add:
for (int i = 1; i < input.length; i++) {
out.addi(((NDArrayWritable) input[i]).get());
}
break;
case Subtract:
out.subi(((NDArrayWritable) input[1]).get());
break;
case Multiply:
for (int i = 1; i < input.length; i++) {
out.muli(((NDArrayWritable) input[i]).get());
}
break;
case Divide:
out.divi(((NDArrayWritable) input[1]).get());
break;
case ReverseSubtract:
out.rsubi(((NDArrayWritable) input[1]).get());
break;
case ReverseDivide:
out.rdivi(((NDArrayWritable) input[1]).get());
break;
case Modulus:
case ScalarMin:
case ScalarMax:
throw new IllegalArgumentException(
"Invalid MathOp: cannot use " + mathOp + " with NDArrayColumnsMathOpTransform");
default:
throw new RuntimeException("Unknown MathOp: " + mathOp);
}
//To avoid threading issues...
Nd4j.getExecutioner().commit();
return new NDArrayWritable(out);
}
Example 15
| Source File: NDArrayScalarOpTransform.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
public NDArrayWritable map(Writable w) {
if (!(w instanceof NDArrayWritable)) {
throw new IllegalArgumentException("Input writable is not an NDArrayWritable: is " + w.getClass());
}
//Make a copy - can't always assume that the original INDArray won't be used again in the future
NDArrayWritable n = ((NDArrayWritable) w);
INDArray a = n.get().dup();
switch (mathOp) {
case Add:
a.addi(scalar);
break;
case Subtract:
a.subi(scalar);
break;
case Multiply:
a.muli(scalar);
break;
case Divide:
a.divi(scalar);
break;
case Modulus:
a.fmodi(scalar);
break;
case ReverseSubtract:
a.rsubi(scalar);
break;
case ReverseDivide:
a.rdivi(scalar);
break;
case ScalarMin:
Transforms.min(a, scalar, false);
break;
case ScalarMax:
Transforms.max(a, scalar, false);
break;
default:
throw new UnsupportedOperationException("Unknown or not supported op: " + mathOp);
}
//To avoid threading issues...
Nd4j.getExecutioner().commit();
return new NDArrayWritable(a);
}
Example 16
| Source File: LossBinaryXENT.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
private INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
INDArray scoreArr;
if (activationFn instanceof ActivationSoftmax) {
//TODO Post GPU support for custom ops: Use LogSoftMax op to avoid numerical issues when calculating score
INDArray logsoftmax = Nd4j.exec((CustomOp) new SoftMax(preOutput, preOutput.ulike(), -1))[0];
Transforms.log(logsoftmax, false);
scoreArr = logsoftmax.muli(labels);
} else {
INDArray output = activationFn.getActivation(preOutput.dup(), true);
if (clipEps > 0.0) {
CustomOp op = DynamicCustomOp.builder("clipbyvalue")
.addInputs(output)
.callInplace(true)
.addFloatingPointArguments(clipEps, 1.0-clipEps)
.build();
Nd4j.getExecutioner().execAndReturn(op);
}
scoreArr = Transforms.log(output, true).muli(labels);
INDArray secondTerm = output.rsubi(1);
Transforms.log(secondTerm, false);
secondTerm.muli(labels.rsub(1));
scoreArr.addi(secondTerm);
}
//Weighted loss function
if (weights != null) {
if (weights.length() != preOutput.size(1)) {
throw new IllegalStateException("Weights vector (length " + weights.length()
+ ") does not match output.size(1)=" + preOutput.size(1));
}
scoreArr.muliRowVector(weights.castTo(scoreArr.dataType()));
}
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr;
}
Example 17
| Source File: NDArrayScalarOpTransform.java From DataVec with Apache License 2.0 | 4 votes |
|
@Override
public NDArrayWritable map(Writable w) {
if (!(w instanceof NDArrayWritable)) {
throw new IllegalArgumentException("Input writable is not an NDArrayWritable: is " + w.getClass());
}
//Make a copy - can't always assume that the original INDArray won't be used again in the future
NDArrayWritable n = ((NDArrayWritable) w);
INDArray a = n.get().dup();
switch (mathOp) {
case Add:
a.addi(scalar);
break;
case Subtract:
a.subi(scalar);
break;
case Multiply:
a.muli(scalar);
break;
case Divide:
a.divi(scalar);
break;
case Modulus:
throw new UnsupportedOperationException(mathOp + " is not supported for NDArrayWritable");
case ReverseSubtract:
a.rsubi(scalar);
break;
case ReverseDivide:
a.rdivi(scalar);
break;
case ScalarMin:
Transforms.min(a, scalar, false);
break;
case ScalarMax:
Transforms.max(a, scalar, false);
break;
default:
throw new UnsupportedOperationException("Unknown or not supported op: " + mathOp);
}
//To avoid threading issues...
Nd4j.getExecutioner().commit();
return new NDArrayWritable(a);
}
