Java Code Examples for org.nd4j.linalg.ops.transforms.Transforms#or()
The following examples show how to use
org.nd4j.linalg.ops.transforms.Transforms#or() .
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Example 1
| Source File: TransformsTest.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testOr1() {
INDArray x = Nd4j.create(new double[] {0, 0, 1, 0, 0});
INDArray y = Nd4j.create(new double[] {0, 0, 1, 1, 0});
INDArray z = Transforms.or(x, y);
assertEquals(y, z);
}
Example 2
| Source File: TransformsTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testOr1() {
INDArray x = Nd4j.create(new double[] {0, 0, 1, 0, 0});
INDArray y = Nd4j.create(new double[] {0, 0, 1, 1, 0});
val e = Nd4j.create(new boolean[] {false, false, true, true, false});
INDArray z = Transforms.or(x, y);
assertEquals(e, z);
}
Example 3
| Source File: SpecialTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testYoloStyle(){
WorkspaceConfiguration WS_ALL_LAYERS_ACT_CONFIG = WorkspaceConfiguration.builder()
.initialSize(0)
.overallocationLimit(0.05)
.policyLearning(LearningPolicy.FIRST_LOOP)
.policyReset(ResetPolicy.BLOCK_LEFT)
.policySpill(SpillPolicy.REALLOCATE)
.policyAllocation(AllocationPolicy.OVERALLOCATE)
.build();
for( int i=0; i<10; i++ ){
try(val ws = Nd4j.getWorkspaceManager().getAndActivateWorkspace(WS_ALL_LAYERS_ACT_CONFIG, "ws")){
// System.out.println("STARTING: " + i);
INDArray objectPresentMask = Nd4j.create(DataType.BOOL, 1,4,4);
long[] shape = {1,3,2,4,4};
INDArray noIntMask1 = Nd4j.createUninitialized(DataType.BOOL, shape, 'c');
INDArray noIntMask2 = Nd4j.createUninitialized(DataType.BOOL, shape, 'c');
noIntMask1 = Transforms.or(noIntMask1.get(all(), all(), point(0), all(), all()), noIntMask1.get(all(), all(), point(1), all(), all()) ); //Shape: [mb, b, H, W]. Values 1 if no intersection
noIntMask2 = Transforms.or(noIntMask2.get(all(), all(), point(0), all(), all()), noIntMask2.get(all(), all(), point(1), all(), all()) );
INDArray noIntMask = Transforms.or(noIntMask1, noIntMask2 );
Nd4j.getExecutioner().commit();
INDArray intMask = Transforms.not(noIntMask); //Values 0 if no intersection
Nd4j.getExecutioner().commit();
Broadcast.mul(intMask, objectPresentMask, intMask, 0, 2, 3);
Nd4j.getExecutioner().commit();
// System.out.println("DONE: " + i);
}
}
}
Example 4
| 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 5
| 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);
}
}
