@Test
public void testAdamUpdater() {
INDArray m, v;
double lr = 0.01;
int iteration = 0;
double beta1 = 0.8;
double beta2 = 0.888;
NeuralNetConfiguration conf =
new NeuralNetConfiguration.Builder()
.learningRate(lr)
.iterations(iteration)
.adamMeanDecay(beta1)
.adamVarDecay(beta2)
.layer(
new DenseLayer.Builder()
.nIn(nIn)
.nOut(nOut)
.updater(org.deeplearning4j.nn.conf.Updater.ADAM)
.build())
.build();
int numParams = LayerFactories.getFactory(conf).initializer().numParams(conf, true);
INDArray params = Nd4j.create(1, numParams);
Layer layer = LayerFactories.getFactory(conf).create(conf, null, 0, params, true);
Updater updater = UpdaterCreator.getUpdater(layer);
int updaterStateSize = updater.stateSizeForLayer(layer);
INDArray updaterState = Nd4j.create(1, updaterStateSize);
updater.setStateViewArray(layer, updaterState, true);
updater.update(layer, gradient, iteration, 1);
double beta1t = FastMath.pow(beta1, iteration);
double beta2t = FastMath.pow(beta2, iteration);
double alphat = lr * FastMath.sqrt(1 - beta2t) / (1 - beta1t);
if (Double.isNaN(alphat) || alphat == 0.0) alphat = epsilon;
Gradient gradientDup = new DefaultGradient();
gradientDup.setGradientFor(DefaultParamInitializer.WEIGHT_KEY, weightGradient);
gradientDup.setGradientFor(DefaultParamInitializer.BIAS_KEY, biasGradient);
for (Map.Entry<String, INDArray> entry : gradientDup.gradientForVariable().entrySet()) {
val = entry.getValue();
m = Nd4j.zeros(val.shape());
v = Nd4j.zeros(val.shape());
m.muli(beta1).addi(val.mul(1.0 - beta1));
v.muli(beta2).addi(val.mul(val).mul(1.0 - beta2));
gradExpected = m.mul(alphat).divi(Transforms.sqrt(v).addi(epsilon));
if (!gradExpected.equals(gradient.getGradientFor(entry.getKey()))) {
System.out.println(Arrays.toString(gradExpected.dup().data().asFloat()));
System.out.println(
Arrays.toString(gradient.getGradientFor(entry.getKey()).dup().data().asFloat()));
}
assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
}
assertEquals(beta1, layer.conf().getLayer().getAdamMeanDecay(), 1e-4);
assertEquals(beta2, layer.conf().getLayer().getAdamVarDecay(), 1e-4);
}
@Test
public void testDirSerializer() throws IOException {
long[][] l = new long[16][];
l[3] = new long[] {0, 0, 12, 13, 14, 0, Long.MAX_VALUE, 0};
l[6] = new long[] {1, 2, 3, 4, 5, 6, 7, 8};
DataOutput2 out = new DataOutput2();
HTreeMap.DIR_SERIALIZER.serialize(out, l);
DataInput2 in = swap(out);
long[][] b = HTreeMap.DIR_SERIALIZER.deserialize(in, -1);
assertEquals(null, b[0]);
assertEquals(null, b[1]);
assertEquals(null, b[2]);
assertEquals(
Arrays.toString(new long[] {0, 0, 12, 13, 14, 0, Long.MAX_VALUE, 0}),
Arrays.toString(b[3]));
assertEquals(null, b[4]);
assertEquals(null, b[5]);
assertEquals(Arrays.toString(new long[] {1, 2, 3, 4, 5, 6, 7, 8}), Arrays.toString(b[6]));
assertEquals(null, b[7]);
}
public TestFile file(Object... path) {
try {
return new TestFile(this, path);
} catch (RuntimeException e) {
throw new RuntimeException(
String.format(
"Could not locate file '%s' relative to '%s'.", Arrays.toString(path), this),
e);
}
}
private static <C> void assertListsEqual(
List<C> expectedList, List<C> actualList, Comparator<C> comparator, String name) {
List<C> notFoundExpected = new ArrayList<C>();
List<C> notFoundActual = new ArrayList<C>();
for (C expected : expectedList) {
boolean found = false;
for (C actual : actualList) {
if (comparator.compare(actual, expected) == 0) {
found = true;
break;
}
}
if (!found) {
notFoundExpected.add(expected);
}
}
for (C actual : actualList) {
boolean found = false;
for (C expected : expectedList) {
if (comparator.compare(actual, expected) == 0) {
found = true;
break;
}
}
if (!found) {
notFoundActual.add(actual);
}
}
if (!notFoundExpected.isEmpty()) {
fail("Not found expected " + name + " " + Arrays.toString(notFoundExpected.toArray()));
}
if (!notFoundActual.isEmpty()) {
fail("Not expected " + name + " " + Arrays.toString(notFoundActual.toArray()));
}
}
@Test
public void testMutualExclusion() {
List<String[]> testList = q.getMutualExclusion();
assertEquals("[testArg, testArg2]", Arrays.toString(testList.get(0)));
}
