Java Code Examples for org.nd4j.linalg.api.ndarray.INDArray#gt()
The following examples show how to use
org.nd4j.linalg.api.ndarray.INDArray#gt() .
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Example 1
| Source File: EvaluationMultiLabel.java From scava with Eclipse Public License 2.0 | 5 votes |
|
@Override
public void eval(INDArray labels, INDArray networkPredictions)
{
// Length of real labels must be same as length of predicted labels
if (!Arrays.equals(labels.shape(),networkPredictions.shape())) {
throw new IllegalArgumentException("Unable to evaluate. Predictions and labels arrays are not same shape." +
" Predictions shape: " + Arrays.toString(networkPredictions.shape()) + ", Labels shape: " + Arrays.toString(labels.shape()));
}
INDArray guess;
INDArray realOutcome;
//The nExamples are given by mini batch, so we need to keep the total length
nExamples += networkPredictions.rows();
List<Integer> actual;
List<Integer> predicted;
for (int i = 0; i < networkPredictions.rows(); i++)
{
//get the first row to analyze
guess = networkPredictions.getRow(i);
realOutcome=labels.getRow(i);
guess=guess.gt(activationThreshold);
actual=new ArrayList<Integer>();
predicted=new ArrayList<Integer>();
for(int j = 0; j < nLabels; j++)
{
actual.add((int) realOutcome.getDouble(j));
predicted.add((int) guess.getDouble(j));
}
actualList.add(actual);
predictedList.add(predicted);
}
}
Example 2
| Source File: Vasttext.java From scava with Eclipse Public License 2.0 | 5 votes |
|
private List<Object> predictLabels(DataIteratorConstructor vasttextMemoryDataContrustor)
{
INDArray predictions = predict(vasttextMemoryDataContrustor);
List<Object> predictionsLabels = new ArrayList<Object>();
if(multiLabel)
{
predictions=predictions.gt(multiLabelActivation);
List<String> activatedLabels;
for(int i=0; i<predictions.rows(); i++)
{
//This is the worst case scenario in which all the labels are present
activatedLabels = new ArrayList<String>(labelsSize);
for(int j=0; j<labelsSize; j++)
{
if(predictions.getDouble(i, j)==1.0)
activatedLabels.add(labels.get(j));
}
predictionsLabels.add(activatedLabels);
}
}
else
{
INDArray predictionIndexes = Nd4j.argMax(predictions, 1);
for(int i=0; i<predictionIndexes.length(); i++)
{
predictionsLabels.add(labels.get(predictionIndexes.getInt(i)));
}
}
return predictionsLabels;
}
Example 3
| Source File: TransformsTest.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testGT1() {
INDArray x = Nd4j.create(new double[] {0, 1, 2, 4});
INDArray exp = Nd4j.create(new double[] {0, 0, 1, 1});
INDArray z = x.gt(1);
assertEquals(exp, z);
}
Example 4
| Source File: TransformsTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testGT1() {
INDArray x = Nd4j.create(new double[] {0, 1, 2, 4});
INDArray exp = Nd4j.create(new boolean[] {false, false, true, true});
INDArray z = x.gt(1);
assertEquals(exp, z);
}
Example 5
| Source File: SameDiffTests.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testPairwiseBooleanTransforms() {
/*
eq, neq, gt, lt, gte, lte, or, and, xor
*/
//Test transforms (pairwise)
Nd4j.getRandom().setSeed(12345);
for (int i = 0; i < 11; i++) {
SameDiff sd = SameDiff.create();
int nOut = 4;
int minibatch = 5;
INDArray ia = Nd4j.randn(minibatch, nOut);
INDArray ib = Nd4j.randn(minibatch, nOut);
SDVariable in1 = sd.var("in1", ia);
SDVariable in2 = sd.var("in2", ib);
SDVariable t;
INDArray expOut;
switch (i) {
case 0:
t = sd.eq(in1, in2);
expOut = ia.eq(ib);
break;
case 1:
t = sd.neq(in1, in2);
expOut = ia.neq(ib);
break;
case 2:
t = sd.gt(in1, in2);
expOut = ia.gt(ib);
break;
case 3:
t = sd.lt(in1, in2);
expOut = ia.lt(ib);
break;
case 4:
t = sd.gte(in1, in2);
expOut = ia.dup();
Nd4j.getExecutioner().exec(new GreaterThanOrEqual(new INDArray[]{ia, ib}, new INDArray[]{expOut}));
break;
case 5:
t = sd.lte(in1, in2);
expOut = ia.dup();
Nd4j.getExecutioner().exec(new LessThanOrEqual(new INDArray[]{ia, ib}, new INDArray[]{expOut}));
break;
case 6:
ia = Nd4j.getExecutioner().exec(new BernoulliDistribution(ia, 0.5));
ib = Nd4j.getExecutioner().exec(new BernoulliDistribution(ib, 0.5));
t = sd.or(in1, in2);
expOut = Transforms.or(ia, ib);
break;
case 7:
t = sd.max(in1, in2);
expOut = Nd4j.getExecutioner().execAndReturn(new OldMax(ia, ib, ia.dup(), ia.length()));
break;
case 8:
t = sd.min(in1, in2);
expOut = Nd4j.getExecutioner().execAndReturn(new OldMin(ia, ib, ia.dup(), ia.length()));
break;
case 9:
ia = Nd4j.getExecutioner().exec(new BernoulliDistribution(ia, 0.5));
ib = Nd4j.getExecutioner().exec(new BernoulliDistribution(ib, 0.5));
t = sd.and(in1, in2);
expOut = Transforms.and(ia, ib);
break;
case 10:
ia = Nd4j.getExecutioner().exec(new BernoulliDistribution(ia, 0.5));
ib = Nd4j.getExecutioner().exec(new BernoulliDistribution(ib, 0.5));
t = sd.xor(in1, in2);
expOut = Transforms.xor(ia, ib);
break;
default:
throw new RuntimeException();
}
log.info("Executing: " + i);
INDArray out = sd.execAndEndResult();
assertEquals(expOut, out);
}
}
Example 6
| Source File: EvaluationBinaryTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testEvaluationBinary() {
//Compare EvaluationBinary to Evaluation class
DataType dtypeBefore = Nd4j.defaultFloatingPointType();
EvaluationBinary first = null;
String sFirst = null;
try {
for (DataType globalDtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF, DataType.INT}) {
Nd4j.setDefaultDataTypes(globalDtype, globalDtype.isFPType() ? globalDtype : DataType.DOUBLE);
for (DataType lpDtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF}) {
Nd4j.getRandom().setSeed(12345);
int nExamples = 50;
int nOut = 4;
long[] shape = {nExamples, nOut};
INDArray labels = Nd4j.getExecutioner().exec(new BernoulliDistribution(Nd4j.createUninitialized(lpDtype, shape), 0.5));
INDArray predicted = Nd4j.rand(lpDtype, shape);
INDArray binaryPredicted = predicted.gt(0.5);
EvaluationBinary eb = new EvaluationBinary();
eb.eval(labels, predicted);
//System.out.println(eb.stats());
double eps = 1e-6;
for (int i = 0; i < nOut; i++) {
INDArray lCol = labels.getColumn(i,true);
INDArray pCol = predicted.getColumn(i,true);
INDArray bpCol = binaryPredicted.getColumn(i,true);
int countCorrect = 0;
int tpCount = 0;
int tnCount = 0;
for (int j = 0; j < lCol.length(); j++) {
if (lCol.getDouble(j) == bpCol.getDouble(j)) {
countCorrect++;
if (lCol.getDouble(j) == 1) {
tpCount++;
} else {
tnCount++;
}
}
}
double acc = countCorrect / (double) lCol.length();
Evaluation e = new Evaluation();
e.eval(lCol, pCol);
assertEquals(acc, eb.accuracy(i), eps);
assertEquals(e.accuracy(), eb.scoreForMetric(ACCURACY, i), eps);
assertEquals(e.precision(1), eb.scoreForMetric(PRECISION, i), eps);
assertEquals(e.recall(1), eb.scoreForMetric(RECALL, i), eps);
assertEquals(e.f1(1), eb.scoreForMetric(F1, i), eps);
assertEquals(e.falseAlarmRate(), eb.scoreForMetric(FAR, i), eps);
assertEquals(e.falsePositiveRate(1), eb.falsePositiveRate(i), eps);
assertEquals(tpCount, eb.truePositives(i));
assertEquals(tnCount, eb.trueNegatives(i));
assertEquals((int) e.truePositives().get(1), eb.truePositives(i));
assertEquals((int) e.trueNegatives().get(1), eb.trueNegatives(i));
assertEquals((int) e.falsePositives().get(1), eb.falsePositives(i));
assertEquals((int) e.falseNegatives().get(1), eb.falseNegatives(i));
assertEquals(nExamples, eb.totalCount(i));
String s = eb.stats();
if(first == null) {
first = eb;
sFirst = s;
} else {
assertEquals(first, eb);
assertEquals(sFirst, s);
}
}
}
}
} finally {
Nd4j.setDefaultDataTypes(dtypeBefore, dtypeBefore);
}
}
Example 7
| Source File: SameDiffTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testPairwiseBooleanTransforms() {
/*
eq, neq, gt, lt, gte, lte, or, and, xor
*/
//Test transforms (pairwise)
Nd4j.getRandom().setSeed(12345);
for (int i = 0; i < 11; i++) {
SameDiff sd = SameDiff.create();
int nOut = 4;
int minibatch = 5;
INDArray ia = Nd4j.randn(minibatch, nOut);
INDArray ib = Nd4j.randn(minibatch, nOut);
SDVariable in1 = sd.var("in1", ia);
SDVariable in2 = sd.var("in2", ib);
SDVariable t;
INDArray expOut;
switch (i) {
case 0:
t = sd.eq(in1, in2);
expOut = ia.eq(ib);
break;
case 1:
t = sd.neq(in1, in2);
expOut = ia.neq(ib);
break;
case 2:
t = sd.gt(in1, in2);
expOut = ia.gt(ib);
break;
case 3:
t = sd.lt(in1, in2);
expOut = ia.lt(ib);
break;
case 4:
t = sd.gte(in1, in2);
expOut = Nd4j.create(DataType.BOOL, ia.shape());
Nd4j.exec(new GreaterThanOrEqual(new INDArray[]{ia, ib}, new INDArray[]{expOut}));
break;
case 5:
t = sd.lte(in1, in2);
expOut = Nd4j.create(DataType.BOOL, ia.shape());
Nd4j.exec(new LessThanOrEqual(new INDArray[]{ia, ib}, new INDArray[]{expOut}));
break;
case 6:
ia = Nd4j.exec(new BernoulliDistribution(ia, 0.5));
ib = Nd4j.exec(new BernoulliDistribution(ib, 0.5));
t = sd.math().or(in1.castTo(DataType.BOOL), in2.castTo(DataType.BOOL));
expOut = Transforms.or(ia, ib);
break;
case 7:
t = sd.max(in1, in2);
expOut = Nd4j.exec(new Max(ia, ib, ia.dup()))[0];
break;
case 8:
t = sd.min(in1, in2);
expOut = Nd4j.exec(new Min(ia, ib, ia.dup()))[0];
break;
case 9:
ia = Nd4j.exec(new BernoulliDistribution(ia, 0.5));
ib = Nd4j.exec(new BernoulliDistribution(ib, 0.5));
t = sd.math().and(in1.castTo(DataType.BOOL), in2.castTo(DataType.BOOL));
expOut = Transforms.and(ia, ib);
break;
case 10:
ia = Nd4j.exec(new BernoulliDistribution(ia, 0.5));
ib = Nd4j.exec(new BernoulliDistribution(ib, 0.5));
t = sd.math().xor(in1.castTo(DataType.BOOL), in2.castTo(DataType.BOOL));
expOut = Transforms.xor(ia, ib);
break;
default:
throw new RuntimeException();
}
log.info("Executing: " + i);
INDArray out = t.eval();
assertEquals(expOut, out);
}
}
Example 8
| Source File: EvaluationBinary.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
public void eval(INDArray labelsArr, INDArray predictionsArr, INDArray maskArr) {
//Check for NaNs in predictions - without this, evaulation could silently be intepreted as class 0 prediction due to argmax
long count = Nd4j.getExecutioner().execAndReturn(new MatchCondition(predictionsArr, Conditions.isNan())).getFinalResult().longValue();
Preconditions.checkState(count == 0, "Cannot perform evaluation with NaNs present in predictions:" +
" %s NaNs present in predictions INDArray", count);
if (countTruePositive != null && countTruePositive.length != labelsArr.size(axis)) {
throw new IllegalStateException("Labels array does not match stored state size. Expected labels array with "
+ "size " + countTruePositive.length + ", got labels array with size " + labelsArr.size(axis) + " for axis " + axis);
}
Triple<INDArray,INDArray, INDArray> p = BaseEvaluation.reshapeAndExtractNotMasked(labelsArr, predictionsArr, maskArr, axis);
INDArray labels = p.getFirst();
INDArray predictions = p.getSecond();
INDArray maskArray = p.getThird();
if(labels.dataType() != predictions.dataType())
labels = labels.castTo(predictions.dataType());
if(decisionThreshold != null && decisionThreshold.dataType() != predictions.dataType())
decisionThreshold = decisionThreshold.castTo(predictions.dataType());
//First: binarize the network prediction probabilities, threshold 0.5 unless otherwise specified
//This gives us 3 binary arrays: labels, predictions, masks
INDArray classPredictions;
if (decisionThreshold != null) {
classPredictions = Nd4j.createUninitialized(DataType.BOOL, predictions.shape());
Nd4j.getExecutioner()
.exec(new BroadcastGreaterThan(predictions, decisionThreshold, classPredictions, 1));
} else {
classPredictions = predictions.gt(0.5);
}
classPredictions = classPredictions.castTo(predictions.dataType());
INDArray notLabels = labels.rsub(1.0); //If labels are 0 or 1, then rsub(1) swaps
INDArray notClassPredictions = classPredictions.rsub(1.0);
INDArray truePositives = classPredictions.mul(labels); //1s where predictions are 1, and labels are 1. 0s elsewhere
INDArray trueNegatives = notClassPredictions.mul(notLabels); //1s where predictions are 0, and labels are 0. 0s elsewhere
INDArray falsePositives = classPredictions.mul(notLabels); //1s where predictions are 1, labels are 0
INDArray falseNegatives = notClassPredictions.mul(labels); //1s where predictions are 0, labels are 1
if (maskArray != null) {
//By multiplying by mask, we keep only those 1s that are actually present
maskArray = maskArray.castTo(truePositives.dataType());
truePositives.muli(maskArray);
trueNegatives.muli(maskArray);
falsePositives.muli(maskArray);
falseNegatives.muli(maskArray);
}
int[] tpCount = truePositives.sum(0).data().asInt();
int[] tnCount = trueNegatives.sum(0).data().asInt();
int[] fpCount = falsePositives.sum(0).data().asInt();
int[] fnCount = falseNegatives.sum(0).data().asInt();
if (countTruePositive == null) {
int l = tpCount.length;
countTruePositive = new int[l];
countFalsePositive = new int[l];
countTrueNegative = new int[l];
countFalseNegative = new int[l];
}
addInPlace(countTruePositive, tpCount);
addInPlace(countFalsePositive, fpCount);
addInPlace(countTrueNegative, tnCount);
addInPlace(countFalseNegative, fnCount);
if (rocBinary != null) {
rocBinary.eval(labels, predictions, maskArray);
}
}
