Java Code Examples for org.nd4j.autodiff.samediff.SDVariable#norm1()
The following examples show how to use
org.nd4j.autodiff.samediff.SDVariable#norm1() .
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Example 1
| Source File: LayerOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testLayerNorm() {
final INDArray random = Nd4j.rand(DataType.DOUBLE, 10, 4);
final INDArray standardized = random.ulike();
Nd4j.getExecutioner().exec(new Standardize(random, standardized, 1));
final INDArray gain = Nd4j.rand(DataType.DOUBLE, 4);
final INDArray bias = Nd4j.rand(DataType.DOUBLE, 4);
final INDArray res = standardized.mulRowVector(gain).addRowVector(bias);
final INDArray expOut = res.norm1();
final int[] axis = new int[]{1};
SameDiff sd = SameDiff.create();
SDVariable sdInput = sd.var("input", standardized);
SDVariable sdGain = sd.var("gain", gain);
SDVariable sdBias = sd.var("bias", bias);
SDVariable out = sd.nn.layerNorm(sdInput, sdGain, sdBias, true, axis);
out.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", expOut)
.gradientCheck(true));
assertNull(err);
}
Example 2
| Source File: LayerOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testLayerNormNoBias() {
final INDArray random = Nd4j.rand(DataType.DOUBLE, 10, 4);
final INDArray standardized = random.ulike();
Nd4j.getExecutioner().exec(new Standardize(random, standardized, 1));
final INDArray gain = Nd4j.rand(DataType.DOUBLE, 4);
final INDArray res = standardized.mulRowVector(gain);
final INDArray expOut = res.norm1();
final int[] axis = new int[]{1};
SameDiff sd = SameDiff.create();
SDVariable sdInput = sd.var("input", standardized);
SDVariable sdGain = sd.var("gain", gain);
SDVariable out = sd.nn.layerNorm(sdInput, sdGain, true, axis);
out.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", expOut)
.gradientCheck(true));
assertNull(err, err);
}
Example 3
| Source File: ReductionOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testDotProductAttention(){
final INDArray keys = Nd4j.rand(new int[]{10, 4, 3});
final INDArray values = Nd4j.rand(new int[]{10, 4, 3});
final INDArray query = Nd4j.rand(new int[]{10, 4, 1});
final INDArray exec = Nd4j.matmul(keys, query, true, false, false)
.divi(Math.sqrt(keys.size(1)));
Nd4j.exec((CustomOp) new SoftMax(exec, exec, 1));
final INDArray finalOut = Nd4j.matmul(values, exec).norm1();
SameDiff sd = SameDiff.create();
SDVariable sdQ = sd.var("q", query);
SDVariable sdK = sd.var("k", keys);
SDVariable sdV = sd.var("v", values);
SDVariable t = sd.nn.dotProductAttention(sdQ, sdK, sdV, null, true);
t.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", finalOut)
.gradientCheck(true));
assertNull(err);
}
Example 4
| Source File: ReductionOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testDotProductAttentionMultiHeadInput(){
final INDArray keys = Nd4j.rand(new int[]{2, 5, 4, 3});
final INDArray values = Nd4j.rand(new int[]{2, 5, 4, 3});
final INDArray query = Nd4j.rand(new int[]{2, 5, 4, 1});
final INDArray exec = Nd4j.matmul(keys, query, true, false, false)
.divi(Math.sqrt(keys.size(-2)));
Nd4j.exec((CustomOp) new SoftMax(exec, exec, -2));
final INDArray finalOut = Nd4j.matmul(values, exec).norm1();
SameDiff sd = SameDiff.create();
SDVariable sdQ = sd.var("q", query);
SDVariable sdK = sd.var("k", keys);
SDVariable sdV = sd.var("v", values);
SDVariable t = sd.nn.dotProductAttention(sdQ, sdK, sdV, null, true);
t.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", finalOut)
.gradientCheck(true));
assertNull(err);
}
Example 5
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testStandardize() {
final INDArray random = Nd4j.rand(new int[]{10, 4});
final int[] axis = new int[]{1};
final INDArray means = random.mean(axis);
final INDArray std = random.std(false, axis);
final INDArray res = random.subColumnVector(means).divColumnVector(std);
final INDArray expOut = res.norm1();
SameDiff sd = SameDiff.create();
SDVariable sdA = sd.var("a", random);
SDVariable t = sd.math.standardize(sdA, axis);
t.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", expOut)
.gradientCheck(true));
assertNull(err, err);
}
Example 6
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testStandardizeNoDeviation() {
final INDArray random = Nd4j.rand(new int[]{10, 4});
for (int i = 0; i < 4; i++) {
random.putScalar(1, i, 7);
}
final int[] axis = new int[]{1};
final INDArray means = random.mean(axis);
final INDArray std = random.std(false, axis);
std.addi(std.eq(0).castTo(DataType.DOUBLE));
final INDArray res = random.subColumnVector(means).divColumnVector(std);
final INDArray expOut = res.norm1();
SameDiff sd = SameDiff.create();
SDVariable sdA = sd.var("a", random);
SDVariable t = sd.math.standardize(sdA, axis);
t.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", expOut)
.gradientCheck(true));
assertNull(err, err);
}
Example 7
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testMatMulTensor() {
final INDArray a = Nd4j.rand(new int[]{1, 2, 3, 4, 5});
final INDArray b = Nd4j.rand(new int[]{1, 2, 3, 5, 6});
final INDArray z = Nd4j.matmul(a, b);
assertArrayEquals(z.shape(), new long[]{1, 2, 3, 4, 6});
SameDiff sd = SameDiff.create();
SDVariable sdA = sd.var("a", a);
SDVariable sdB = sd.var("b", b);
SDVariable t = sd.mmul(sdA, sdB);
t.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.gradientCheck(true));
assertNull(err, err);
}
Example 8
| Source File: LayerOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testLayerNormNoDeviation() {
final INDArray random = Nd4j.rand(DataType.DOUBLE, 10, 4);
for (int i = 0; i < 4; i++) {
random.putScalar(1, i, 7);
}
final INDArray standardized = random.ulike();
Nd4j.getExecutioner().exec(new Standardize(random, standardized, 1));
final INDArray gain = Nd4j.rand(DataType.DOUBLE, 4);
final INDArray bias = Nd4j.rand(DataType.DOUBLE, 4);
final INDArray res = standardized.mulRowVector(gain).addRowVector(bias);
final INDArray expOut = res.norm1();
final int[] axis = new int[]{1};
SameDiff sd = SameDiff.create();
SDVariable sdInput = sd.var("input", standardized);
SDVariable sdGain = sd.var("gain", gain);
SDVariable sdBias = sd.var("bias", bias);
SDVariable out = sd.nn.layerNorm(sdInput, sdGain, sdBias, true, axis);
out.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", expOut)
.gradCheckMask(Collections.singletonMap("input", random.neq(7)))
.gradientCheck(true));
assertNull(err, err);
}
Example 9
| Source File: ReductionOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testDotProductAttentionWithMask(){
final INDArray keys = Nd4j.rand(new int[]{10, 4, 3});
final INDArray values = Nd4j.rand(new int[]{10, 4, 3});
final INDArray query = Nd4j.rand(new int[]{10, 4, 1});
final INDArray mask = Nd4j.rand(10, 3).gte(0.2).castTo(DataType.DOUBLE);
final INDArray exec = Nd4j.matmul(keys, query, true, false, false)
.divi(Math.sqrt(keys.size(1)));
exec.addi(mask.reshape(10, 3, 1).sub(1).muli(1e9));
Nd4j.exec((CustomOp) new SoftMax(exec, exec, 1));
final INDArray finalOut = Nd4j.matmul(values, exec).norm1();
SameDiff sd = SameDiff.create();
SDVariable sdQ = sd.var("q", query);
SDVariable sdK = sd.var("k", keys);
SDVariable sdV = sd.var("v", values);
SDVariable sdMask = sd.constant("mask", mask);
SDVariable t = sd.nn.dotProductAttention(sdQ, sdK, sdV, sdMask, true);
t.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", finalOut)
.gradCheckSkipVariables("mask")
.gradientCheck(true));
assertNull(err);
}
Example 10
| Source File: ReductionOpValidation.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testDotProductAttentionMultiHeadInputWithMask(){
final INDArray keys = Nd4j.rand(new int[]{2, 5, 4, 3});
final INDArray values = Nd4j.rand(new int[]{2, 5, 4, 3});
final INDArray query = Nd4j.rand(new int[]{2, 5, 4, 2});
final INDArray mask = Nd4j.rand(2, 3).gte(0.2).castTo(DataType.DOUBLE);
final INDArray exec = Nd4j.matmul(keys, query, true, false, false)
.divi(Math.sqrt(keys.size(-2)));
exec.addi(Nd4j.tile(mask.reshape(2, 1, 3, 1), 1, 5, 1, 2).sub(1).muli(1e9));
Nd4j.exec((CustomOp) new SoftMax(exec, exec, -2));
final INDArray finalOut = Nd4j.matmul(values, exec).norm1();
SameDiff sd = SameDiff.create();
SDVariable sdQ = sd.var("q", query);
SDVariable sdK = sd.var("k", keys);
SDVariable sdV = sd.var("v", values);
SDVariable sdMask = sd.constant("mask", mask);
SDVariable t = sd.nn.dotProductAttention(sdQ, sdK, sdV, sdMask, true);
t.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.expectedOutput("out", finalOut)
.gradCheckSkipVariables("mask")
.gradientCheck(true));
assertNull(err);
}
Example 11
| Source File: TransformOpValidation.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testMatMulTensorTranspose() {
for (boolean transposeA : new boolean[]{false, true}) {
for (boolean transposeB : new boolean[]{false, true}) {
for (boolean transposeResult : new boolean[]{false, true}) {
log.info("Testing with transposeA={}; transposeB={}; transposeResult={};", transposeA, transposeB, transposeResult);
int m = 0, n = 0, k = 0, l = 0, i = 0, j = 0;
if (!transposeA && !transposeB && !transposeResult) {
m = 4;
n = 5;
k = 5;
l = 6;
i = 4;
j = 6;
}
if (!transposeA && transposeB && !transposeResult) {
m = 4;
n = 5;
k = 6;
l = 5;
i = 4;
j = 6;
}
if (!transposeA && !transposeB && transposeResult) {
m = 4;
n = 5;
k = 5;
l = 6;
i = 6;
j = 4;
}
if (!transposeA && transposeB && transposeResult) {
m = 4;
n = 5;
k = 6;
l = 5;
i = 6;
j = 4;
}
if (transposeA && !transposeB && !transposeResult) {
m = 5;
n = 4;
k = 5;
l = 6;
i = 4;
j = 6;
}
if (transposeA && transposeB && !transposeResult) {
m = 5;
n = 4;
k = 6;
l = 5;
i = 4;
j = 6;
}
if (transposeA && !transposeB && transposeResult) {
m = 5;
n = 4;
k = 5;
l = 6;
i = 6;
j = 4;
}
if (transposeA && transposeB && transposeResult) {
m = 5;
n = 4;
k = 6;
l = 5;
i = 6;
j = 4;
}
final INDArray a = Nd4j.rand(new int[]{1, 2, 3, m, n});
final INDArray b = Nd4j.rand(new int[]{1, 2, 3, k, l});
final INDArray z = Nd4j.matmul(a, b, transposeA, transposeB, transposeResult);
assertArrayEquals(z.shape(), new long[]{1, 2, 3, i, j});
SameDiff sd = SameDiff.create();
SDVariable sdA = sd.var("a", a);
SDVariable sdB = sd.var("b", b);
SDVariable t = sd.mmul(sdA, sdB, transposeA, transposeB, transposeResult);
t.norm1("out");
String err = OpValidation.validate(new TestCase(sd)
.gradientCheck(true));
assertNull(err, err);
}
}
}
}
