Java Code Examples for org.nd4j.autodiff.samediff.SameDiff#setLogExecution()
The following examples show how to use
org.nd4j.autodiff.samediff.SameDiff#setLogExecution() .
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Example 1
| Source File: GradCheckReductions.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testReduce3() {
Nd4j.getRandom().setSeed(12345);
int d0 = 3;
int d1 = 4;
int d2 = 5;
List<String> allFailed = new ArrayList<>();
for (int[] reduceDims : new int[][]{{Integer.MAX_VALUE}, {0, 1, 2}, {0}, {1}, {2}, {0, 1}, {0, 2}, {1, 2}}) {
for (int i = 0; i < 6; i++) {
SameDiff sd = SameDiff.create();
sd.setLogExecution(false);
SDVariable in = sd.var("in", new int[]{-1, d1, d2});
SDVariable in2 = sd.var("in2", new int[]{-1, d1, d2});
INDArray inArr = Nd4j.randn(new int[]{d0, d1, d2}).muli(100);
INDArray in2Arr = Nd4j.randn(inArr.shape()).muli(100);
SDVariable reduced;
String name;
switch (i) {
case 0:
reduced = sd.manhattanDistance(in, in2, reduceDims);
name = "manhattan";
break;
case 1:
reduced = sd.euclideanDistance(in, in2, reduceDims);
name = "euclidean";
break;
case 2:
reduced = sd.cosineSimilarity(in, in2, reduceDims);
name = "cosine";
break;
case 3:
reduced = sd.cosineDistance(in, in2, reduceDims);
name = "cosinedistance";
break;
case 4:
reduced = sd.hammingDistance(in, in2, reduceDims);
name = "hamming";
break;
case 5:
name = "jaccard";
reduced = sd.jaccardDistance(name, in, in2, reduceDims);
inArr.divi(100).addi(0.1);
in2Arr.divi(100).addi(0.1);
break;
default:
throw new RuntimeException();
}
//Sum: note that this should be a no-op for the full array cases
SDVariable sum = sd.sum(reduced, Integer.MAX_VALUE);
String msg = "(test " + i + " - " + name + ", dimensions=" + Arrays.toString(reduceDims) + ")";
log.info("*** Starting test: " + msg);
sd.associateArrayWithVariable(inArr, in);
sd.associateArrayWithVariable(in2Arr, in2);
sd.execAndEndResult();
// FIXME: we can't swallow exceptions here now, but once release out and stuff stabilized - we can
//try {
boolean ok = GradCheckUtil.checkGradients(sd, 1e-5, 1e-5, 1e-4, true, false);
if (!ok) {
allFailed.add(msg);
}
/*
} catch (Exception e) {
e.printStackTrace();
allFailed.add(msg + " - EXCEPTION");
}
*/
}
}
assertEquals("Failed: " + allFailed, 0, allFailed.size());
}
Example 2
| Source File: ReductionOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testReduce3_2() {
Nd4j.getRandom().setSeed(12345);
int d0 = 3;
int d1 = 4;
int d2 = 5;
for (int[] reduceDims : new int[][]{{Integer.MAX_VALUE}, {0, 1, 2}, {0}, {1}, {2}, {0, 1}, {0, 2}, {1, 2}}) {
for (int i = 0; i < 6; i++) {
SameDiff sd = SameDiff.create();
sd.setLogExecution(false);
INDArray a = Nd4j.rand(DataType.DOUBLE, d0, d1, d2);
INDArray b = Nd4j.rand(DataType.DOUBLE, d0, d1, d2);
SDVariable in = sd.var("in", a);
SDVariable in2 = sd.var("in2", b);
INDArray expOut;
SDVariable reduced;
String name;
// System.out.println(i);
switch (i) {
case 0:
reduced = sd.math().manhattanDistance(in, in2, reduceDims);
name = "manhattan";
expOut = Nd4j.getExecutioner().exec(new ManhattanDistance(a, b, null, false, reduceDims));
break;
case 1:
reduced = sd.math().euclideanDistance(in, in2, reduceDims);
name = "euclidean";
expOut = Nd4j.getExecutioner().exec(new EuclideanDistance(a, b, null, false, reduceDims));
break;
case 2:
reduced = sd.math().cosineSimilarity(in, in2, reduceDims);
name = "cosine";
expOut = Nd4j.getExecutioner().exec(new CosineSimilarity(a, b, null, false, reduceDims));
break;
case 3:
reduced = sd.math().jaccardDistance(in, in2, reduceDims);
name = "jaccard";
expOut = Nd4j.getExecutioner().exec(new JaccardDistance(a, b, null, false, reduceDims));
break;
case 4:
reduced = sd.math().hammingDistance(in, in2, reduceDims);
name = "hamming";
expOut = Nd4j.getExecutioner().exec(new HammingDistance(a, b, null, false, reduceDims));
break;
case 5:
reduced = sd.math().cosineDistance(in, in2, reduceDims);
name = "reduced";
expOut = Nd4j.getExecutioner().exec(new CosineDistance(a, b, null, false, reduceDims));
break;
default:
throw new RuntimeException();
}
// System.out.println(i + " - end");
long[] expShape;
if (Arrays.equals(new int[]{0}, reduceDims)) {
expShape = new long[]{4, 5};
} else if (Arrays.equals(new int[]{1}, reduceDims)) {
expShape = new long[]{3, 5};
} else if (Arrays.equals(new int[]{2}, reduceDims)) {
expShape = new long[]{3, 4};
} else if (Arrays.equals(new int[]{Integer.MAX_VALUE}, reduceDims)) {
expShape = new long[]{};
} else if (Arrays.equals(new int[]{0, 1}, reduceDims)) {
expShape = new long[]{5};
} else if (Arrays.equals(new int[]{0, 2}, reduceDims)) {
expShape = new long[]{4};
} else if (Arrays.equals(new int[]{1, 2}, reduceDims)) {
expShape = new long[]{3};
} else if (Arrays.equals(new int[]{0, 1, 2}, reduceDims)) {
expShape = new long[]{};
} else {
throw new RuntimeException();
}
String msg = name + " - dims=" + Arrays.toString(reduceDims);
INDArray out = reduced.eval();
log.info(msg + " - expected shape: " + Arrays.toString(expShape) + ", out=" + Arrays.toString(out.shape())
+ ", outExp=" + Arrays.toString(expOut.shape()));
assertArrayEquals(msg, expShape, out.shape());
assertArrayEquals(msg, expShape, expOut.shape());
assertEquals(msg, expOut, out);
}
}
}
