Java Code Examples for org.nd4j.linalg.checkutil.NDArrayCreationUtil#getAll3dTestArraysWithShape()
The following examples show how to use
org.nd4j.linalg.checkutil.NDArrayCreationUtil#getAll3dTestArraysWithShape() .
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Example 1
| Source File: SameDiffTests.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
public void testSqueezeExpandChain() {
val origShape = new long[]{3, 4, 5};
for (int i = 0; i < 3; i++) {
val shape = origShape.clone();
shape[i] = 1;
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, shape)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable squeeze = sd.squeeze(in, i);
SDVariable expand = sd.expandDims(squeeze, i);
INDArray out = sd.execAndEndResult();
String msg = "expand/Squeeze=" + i + ", source=" + p.getSecond();
assertEquals(msg, out, inArr); //squeeze -> expand: should be opposite ops
}
}
}
Example 2
| Source File: GradCheckMisc.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
public void testReshapeGradient() {
int[] origShape = new int[]{3, 4, 5};
for (int[] toShape : new int[][]{{3, 4 * 5}, {3 * 4, 5}, {1, 3 * 4 * 5}, {3 * 4 * 5, 1}}) {
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, origShape)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable reshape = sd.reshape(in, toShape);
//Using stdev here: mean/sum would backprop the same gradient for each input...
SDVariable stdev = sd.standardDeviation("out", reshape, true);
INDArray out = sd.execAndEndResult();
INDArray expOut = in.getArr().std(true, Integer.MAX_VALUE);
assertEquals(expOut, out);
String msg = "toShape=" + Arrays.toString(toShape) + ", source=" + p.getSecond();
boolean ok = GradCheckUtil.checkGradients(sd);
assertTrue(msg, ok);
}
}
}
Example 3
| Source File: GradCheckMisc.java From nd4j with Apache License 2.0 | 6 votes |
|
@Test
public void testPermuteGradient() {
int[] origShape = new int[]{3, 4, 5};
for (int[] perm : new int[][]{{0, 1, 2}, {0, 2, 1}, {1, 0, 2}, {1, 2, 0}, {2, 0, 1}, {2, 1, 0}}) {
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, origShape)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable permute = sd.f().permute(in, perm);
//Using stdev here: mean/sum would backprop the same gradient for each input...
SDVariable stdev = sd.standardDeviation("out", permute, true);
INDArray out = sd.execAndEndResult();
INDArray expOut = in.getArr().std(true, Integer.MAX_VALUE);
assertEquals(expOut, out);
String msg = "permute=" + Arrays.toString(perm) + ", source=" + p.getSecond();
boolean ok = GradCheckUtil.checkGradients(sd);
assertTrue(msg, ok);
}
}
}
Example 4
| Source File: SameDiffTests.java From nd4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSqueezeDims() {
val origShape = new long[]{3, 4, 5};
for (int i = 0; i < 3; i++) {
val shape = origShape.clone();
shape[i] = 1;
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, shape)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable squeeze = sd.f().squeeze(in, i);
INDArray out = sd.execAndEndResult();
INDArray expOut;
switch (i) {
case 0:
expOut = inArr.dup('c').reshape('c', origShape[1], origShape[2]);
break;
case 1:
expOut = inArr.dup('c').reshape('c', origShape[0], origShape[2]);
break;
case 2:
expOut = inArr.dup('c').reshape('c', origShape[0], origShape[1]);
break;
default:
throw new RuntimeException();
}
String msg = "squeezeDim=" + i + ", source=" + p.getSecond();
assertEquals(msg, out, expOut);
}
}
}
Example 5
| Source File: SameDiffTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSqueezeDims() {
val origShape = new long[]{3, 4, 5};
for (int i = 0; i < 3; i++) {
val shape = origShape.clone();
shape[i] = 1;
for (Pair<INDArray, String> p : NDArrayCreationUtil
.getAll3dTestArraysWithShape(12345, shape, DataType.FLOAT)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable squeeze = sd.squeeze(in, i);
INDArray out = squeeze.eval();
INDArray expOut;
switch (i) {
case 0:
expOut = inArr.dup('c').reshape('c', origShape[1], origShape[2]);
break;
case 1:
expOut = inArr.dup('c').reshape('c', origShape[0], origShape[2]);
break;
case 2:
expOut = inArr.dup('c').reshape('c', origShape[0], origShape[1]);
break;
default:
throw new RuntimeException();
}
String msg = "squeezeDim=" + i + ", source=" + p.getSecond();
assertEquals(msg, out, expOut);
}
}
}
Example 6
| Source File: SameDiffTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSqueezeExpandChain() {
val origShape = new long[]{3, 4, 5};
for (int i = 0; i < 3; i++) {
val shape = origShape.clone();
shape[i] = 1;
for (Pair<INDArray, String> p : NDArrayCreationUtil
.getAll3dTestArraysWithShape(12345, shape, DataType.FLOAT)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable squeeze = sd.squeeze(in, i);
SDVariable expand = sd.expandDims(squeeze, i);
INDArray out = expand.eval();
String msg = "expand/Squeeze=" + i + ", source=" + p.getSecond();
assertEquals(msg, out, inArr); //squeeze -> expand: should be opposite ops
}
}
}
Example 7
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testPermuteGradient() {
int[] origShape = new int[]{3, 4, 5};
List<String> failed = new ArrayList<>();
for (int[] perm : new int[][]{{0, 1, 2}, {0, 2, 1}, {1, 0, 2}, {1, 2, 0}, {2, 0, 1}, {2, 1, 0}}) {
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, origShape, DataType.DOUBLE)) {
String msg = "permute=" + Arrays.toString(perm) + ", source=" + p.getSecond();
System.out.println(msg);
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable permute = sd.permute(in, perm);
//Using stdev here: mean/sum would backprop the same gradient for each input...
SDVariable stdev = sd.standardDeviation("out", permute, true);
INDArray exp = inArr.permute(perm);
INDArray expOut = in.getArr().std(true, Integer.MAX_VALUE);
TestCase tc = new TestCase(sd);
tc.testName(msg)
.expected("out", expOut)
.expected(permute, exp);
String error = OpValidation.validate(tc, true);
if(error != null){
failed.add(msg);
}
}
}
assertEquals(failed.toString(), 0, failed.size());
}
Example 8
| Source File: TestNdArrReadWriteTxt.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testNd4jReadWriteText() throws Exception {
File dir = testDir.newFolder();
int count = 0;
for(val testShape : new long[][]{{1,1}, {3,1}, {4,5}, {1,2,3}, {2,1,3}, {2,3,1}, {2,3,4}, {1,2,3,4}, {2,3,4,2}}){
List<Pair<INDArray, String>> l = null;
switch (testShape.length){
case 2:
l = NDArrayCreationUtil.getAllTestMatricesWithShape(testShape[0], testShape[1], 12345, Nd4j.defaultFloatingPointType());
break;
case 3:
l = NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, testShape, Nd4j.defaultFloatingPointType());
break;
case 4:
l = NDArrayCreationUtil.getAll4dTestArraysWithShape(12345, testShape, Nd4j.defaultFloatingPointType());
break;
default:
throw new RuntimeException();
}
for (Pair<INDArray, String> p : l) {
File f = new File(dir, (count++) + ".txt");
Nd4j.writeTxt(p.getFirst(), f.getAbsolutePath());
INDArray read = Nd4j.readTxt(f.getAbsolutePath());
String s = FileUtils.readFileToString(f, StandardCharsets.UTF_8);
// System.out.println(s);
assertEquals(p.getFirst(), read);
}
}
}
Example 9
| Source File: GradCheckMisc.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
public void testSqueezeGradient() {
val origShape = new long[]{3, 4, 5};
for (int i = 0; i < 3; i++) {
val shape = origShape.clone();
shape[i] = 1;
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, shape)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable squeeze = sd.f().squeeze(in, i);
//Using stdev here: mean/sum would backprop the same gradient for each input...
SDVariable stdev = sd.standardDeviation("out", squeeze, true);
long[] expShapePostSqueeze;
switch (i) {
case 0:
expShapePostSqueeze = new long[]{4, 5};
break;
case 1:
expShapePostSqueeze = new long[]{3, 5};
break;
case 2:
expShapePostSqueeze = new long[]{3, 4};
break;
default:
throw new RuntimeException();
}
sd.execAndEndResult();
INDArray squeezed = squeeze.getArr();
assertArrayEquals(expShapePostSqueeze, squeezed.shape());
INDArray out = sd.execAndEndResult();
INDArray expOut = in.getArr().std(true, Integer.MAX_VALUE);
assertEquals(expOut, out);
String msg = "squeezeDim=" + i + ", source=" + p.getSecond();
boolean ok = GradCheckUtil.checkGradients(sd);
assertTrue(msg, ok);
}
}
}
Example 10
| Source File: ShapeOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testSqueezeGradient() {
val origShape = new long[]{3, 4, 5};
List<String> failed = new ArrayList<>();
for (int i = 0; i < 3; i++) {
val shape = origShape.clone();
shape[i] = 1;
for (Pair<INDArray, String> p : NDArrayCreationUtil.getAll3dTestArraysWithShape(12345, shape, DataType.DOUBLE)) {
INDArray inArr = p.getFirst().muli(100);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", inArr);
SDVariable squeeze = sd.squeeze(in, i);
//Using stdev here: mean/sum would backprop the same gradient for each input...
SDVariable stdev = sd.standardDeviation("out", squeeze, true);
long[] expShapePostSqueeze;
switch (i) {
case 0:
expShapePostSqueeze = new long[]{4, 5};
break;
case 1:
expShapePostSqueeze = new long[]{3, 5};
break;
case 2:
expShapePostSqueeze = new long[]{3, 4};
break;
default:
throw new RuntimeException();
}
INDArray exp = inArr.dup('c').reshape('c', expShapePostSqueeze);
Map<String,INDArray> m = sd.outputAll(null);
INDArray squeezed = m.get(squeeze.name());
// assertArrayEquals(expShapePostSqueeze, squeezed.shape());
INDArray out = m.get(stdev.name());
INDArray expOut = in.getArr().std(true, Integer.MAX_VALUE);
assertEquals(expOut, out);
String msg = "squeezeDim=" + i + ", source=" + p.getSecond();
TestCase tc = new TestCase(sd)
.testName(msg)
.expected(squeeze.name(), exp)
.expectedOutput("out", expOut);
String error = OpValidation.validate(tc, true);
if(error != null){
failed.add(name);
}
}
}
assertEquals(failed.toString(), 0, failed.size());
}
