Java Code Examples for org.nd4j.autodiff.samediff.SDVariable#setArray()
The following examples show how to use
org.nd4j.autodiff.samediff.SDVariable#setArray() .
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Example 1
| Source File: LayerOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testLrn2d() {
Nd4j.getRandom().setSeed(12345);
int[][] inputSizes = new int[][]{{1, 3, 8, 8}, {3, 6, 12, 12}};
List<String> failed = new ArrayList<>();
for (int[] inSizeNCHW : inputSizes) {
SameDiff sd = SameDiff.create();
SDVariable in = null;
int[] inSize;
//LRN
String msg = "LRN with NCHW - input" + Arrays.toString(inSizeNCHW);
inSize = inSizeNCHW;
in = sd.var("in", inSize);
SDVariable out = sd.cnn().localResponseNormalization(in, LocalResponseNormalizationConfig.builder()
.depth(3)
.bias(1)
.alpha(1)
.beta(0.5)
.build());
INDArray inArr = Nd4j.rand(inSize).muli(10);
in.setArray(inArr);
SDVariable loss = sd.mean("loss", out);
log.info("Starting test: " + msg);
TestCase tc = new TestCase(sd).gradientCheck(true);
String error = OpValidation.validate(tc);
if (error != null) {
failed.add(msg);
}
}
assertEquals(failed.toString(), 0, failed.size());
}
Example 2
| Source File: LayerOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testConv3d() {
//Pooling3d, Conv3D, batch norm
Nd4j.getRandom().setSeed(12345);
//NCDHW format
int[][] inputSizes = new int[][]{{2, 3, 4, 5, 5}};
List<String> failed = new ArrayList<>();
for (int[] inSizeNCDHW : inputSizes) {
for (boolean ncdhw : new boolean[]{true, false}) {
int nIn = inSizeNCDHW[1];
int[] shape = (ncdhw ? inSizeNCDHW : ncdhwToNdhwc(inSizeNCDHW));
for (int i = 0; i < 5; i++) {
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", shape);
SDVariable out;
String msg;
switch (i) {
case 0:
//Conv3d, with bias, same
msg = "0 - conv3d+bias+same, ncdhw=" + ncdhw + " - input " + Arrays.toString(shape);
SDVariable w0 = sd.var("w0", Nd4j.rand(new int[]{2, 2, 2, nIn, 3}).muli(10)); //[kD, kH, kW, iC, oC]
SDVariable b0 = sd.var("b0", Nd4j.rand(new long[]{3}).muli(10));
out = sd.cnn().conv3d(in, w0, b0, Conv3DConfig.builder()
.dataFormat(ncdhw ? Conv3DConfig.NCDHW : Conv3DConfig.NDHWC)
.isSameMode(true)
.kH(2).kW(2).kD(2)
.sD(1).sH(1).sW(1)
.build());
break;
case 1:
//Conv3d, no bias, no same
msg = "1 - conv3d+no bias+no same, ncdhw=" + ncdhw + " - input " + Arrays.toString(shape);
SDVariable w1 = sd.var("w1", Nd4j.rand(new int[]{2, 2, 2, nIn, 3}).muli(10)); //[kD, kH, kW, iC, oC]
out = sd.cnn().conv3d(in, w1, Conv3DConfig.builder()
.dataFormat(ncdhw ? Conv3DConfig.NCDHW : Conv3DConfig.NDHWC)
.isSameMode(false)
.kH(2).kW(2).kD(2)
.sD(1).sH(1).sW(1)
.build());
break;
case 2:
//pooling3d - average, no same
msg = "2 - pooling 3d, average, same";
out = sd.cnn().avgPooling3d(in, Pooling3DConfig.builder()
.kH(2).kW(2).kD(2)
.sH(1).sW(1).sD(1)
.isSameMode(false)
.isNCDHW(ncdhw)
.build());
break;
case 3:
//pooling 3d - max, no same
msg = "3 - pooling 3d, max, same";
out = sd.cnn().maxPooling3d(in, Pooling3DConfig.builder()
.kH(2).kW(2).kD(2)
.sH(1).sW(1).sD(1)
.isSameMode(true)
.isNCDHW(ncdhw)
.build());
break;
case 4:
//Deconv3d
msg = "4 - deconv3d, ncdhw=" + ncdhw;
SDVariable wDeconv = sd.var(Nd4j.rand(new int[]{2, 2, 2, 3, nIn})); //[kD, kH, kW, oC, iC]
SDVariable bDeconv = sd.var(Nd4j.rand(new int[]{3}));
out = sd.cnn().deconv3d("Deconv3d", in, wDeconv, bDeconv, DeConv3DConfig.builder()
.kD(2).kH(2).kW(2)
.isSameMode(true)
.dataFormat(ncdhw ? DeConv3DConfig.NCDHW : DeConv3DConfig.NDHWC)
.build());
break;
case 5:
//Batch norm - 3d input
throw new RuntimeException("Batch norm test not yet implemented");
default:
throw new RuntimeException();
}
INDArray inArr = Nd4j.rand(shape).muli(10);
in.setArray(inArr);
SDVariable loss = sd.standardDeviation("loss", out, true);
log.info("Starting test: " + msg);
TestCase tc = new TestCase(sd).gradientCheck(true);
tc.testName(msg);
String error = OpValidation.validate(tc);
if (error != null) {
failed.add(name);
}
}
}
}
assertEquals(failed.toString(), 0, failed.size());
}
Example 3
| Source File: MiscOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testScatterOpGradients() {
List<String> failed = new ArrayList<>();
for (int i = 0; i < 7; i++) {
Nd4j.getRandom().setSeed(12345);
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", DataType.DOUBLE, 20, 10);
SDVariable indices = sd.var("indices", DataType.INT, new long[]{5});
SDVariable updates = sd.var("updates", DataType.DOUBLE, 5, 10);
in.setArray(Nd4j.rand(DataType.DOUBLE, 20, 10));
indices.setArray(Nd4j.create(new double[]{3, 4, 5, 10, 18}).castTo(DataType.INT));
updates.setArray(Nd4j.rand(DataType.DOUBLE, 5, 10).muli(2).subi(1));
SDVariable scatter;
String name;
switch (i) {
case 0:
scatter = sd.scatterAdd("s", in, indices, updates);
name = "scatterAdd";
break;
case 1:
scatter = sd.scatterSub("s", in, indices, updates);
name = "scatterSub";
break;
case 2:
scatter = sd.scatterMul("s", in, indices, updates);
name = "scatterMul";
break;
case 3:
scatter = sd.scatterDiv("s", in, indices, updates);
name = "scatterDiv";
break;
case 4:
scatter = sd.scatterUpdate("s", in, indices, updates);
name = "scatterUpdate";
break;
case 5:
scatter = sd.scatterMax("s", in, indices, updates);
name = "scatterMax";
break;
case 6:
scatter = sd.scatterMin("s", in, indices, updates);
name = "scatterMin";
break;
default:
throw new RuntimeException();
}
INDArray exp = in.getArr().dup();
int[] indicesInt = indices.getArr().dup().data().asInt();
for( int j=0; j<indicesInt.length; j++ ){
INDArray updateRow = updates.getArr().getRow(j);
INDArray destinationRow = exp.getRow(indicesInt[j]);
switch (i){
case 0:
destinationRow.addi(updateRow);
break;
case 1:
destinationRow.subi(updateRow);
break;
case 2:
destinationRow.muli(updateRow);
break;
case 3:
destinationRow.divi(updateRow);
break;
case 4:
destinationRow.assign(updateRow);
break;
case 5:
destinationRow.assign(Transforms.max(destinationRow, updateRow, true));
break;
case 6:
destinationRow.assign(Transforms.min(destinationRow, updateRow, true));
break;
default:
throw new RuntimeException();
}
}
SDVariable loss = sd.sum(scatter); //.standardDeviation(scatter, true); //.sum(scatter); //TODO stdev might be better here as gradients are non-symmetrical...
TestCase tc = new TestCase(sd)
.expected(scatter, exp)
.gradCheckSkipVariables(indices.name());
String error = OpValidation.validate(tc);
if(error != null){
failed.add(name);
}
}
assertEquals(failed.toString(), 0, failed.size());
}
Example 4
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testIsX() {
List<String> failed = new ArrayList<>();
for (int i = 0; i < 4; i++) {
SameDiff sd = SameDiff.create();
SDVariable in = sd.var("in", 4);
SDVariable out;
INDArray exp;
INDArray inArr;
switch (i) {
case 0:
inArr = Nd4j.create(new double[]{10, Double.POSITIVE_INFINITY, 0, Double.NEGATIVE_INFINITY});
exp = Nd4j.create(new boolean[]{true, false, true, false});
out = sd.math().isFinite(in);
break;
case 1:
inArr = Nd4j.create(new double[]{10, Double.POSITIVE_INFINITY, 0, Double.NEGATIVE_INFINITY});
exp = Nd4j.create(new boolean[]{false, true, false, true});
out = sd.math().isInfinite(in);
break;
case 2:
//TODO: IsMax supports both bool and float out: https://github.com/deeplearning4j/deeplearning4j/issues/6872
inArr = Nd4j.create(new double[]{-3, 5, 0, 2});
exp = Nd4j.create(new boolean[]{false, true, false, false});
out = sd.math().isMax(in);
break;
case 3:
inArr = Nd4j.create(new double[]{0, Double.NaN, 10, Double.NaN});
exp = Nd4j.create(new boolean[]{false, true, false, true});
out = sd.math().isNaN(in);
break;
default:
throw new RuntimeException();
}
SDVariable other = sd.var("other", Nd4j.rand(DataType.DOUBLE, 4));
SDVariable loss = out.castTo(DataType.DOUBLE).add(other).mean();
TestCase tc = new TestCase(sd)
.gradientCheck(false) //Can't gradient check - in -> boolean -> cast(double)
.expected(out, exp);
in.setArray(inArr);
String err = OpValidation.validate(tc, true);
if (err != null) {
failed.add(err);
}
}
assertEquals(failed.toString(), 0, failed.size());
}
