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Various fixes (#143) 

* #8568 ArrayUtil optimization

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* #6171 Keras ReLU and ELU support

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Keras softmax layer import

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* #8549 Webjars dependency management

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Fix for TF import names ':0' suffix issue / NPE

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* BiasAdd: fix default data format for TF import

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Update zoo test ignores

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* #8509 SameDiff Listener API - provide frame + iteration

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* #8520 ND4J Environment

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Deconv3d

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Deconv3d fixes + gradient check

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Conv3d fixes + deconv3d DType test

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Fix issue with deconv3d gradinet check weight init

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* #8579 Fix BaseCudaDataBuffer constructor fix for UINT16

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* DataType.isNumerical() returns false for BOOL type

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* #8504 Reduce Spark log spam for tests

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Clean up DL4J gradient check test spam

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* More Gradient check spam reduction

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* SameDiff test spam reduction

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Fixes for FlatBuffers mapping

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* SameDiff log spam cleanup

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Tests should extend BaseNd4jTest

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Remove debug line in c++ op

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* ND4J test spam cleanup

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* DL4J test spam reduction

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* More Dl4J and datavec test spam cleanup

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Fix for bad conv3d test

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Additional test

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Embedding layers: don't inherit global default activation function

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Trigger CI

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Consolidate all BaseDL4JTest classes to single class used everywhere; make timeout configurable per class

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Test fixes and timeout increases

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Timeouts and PReLU fixes

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Restore libnd4j build threads arg for CUDA build

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Increase timeouts on a few tests to avoid spurious failures on some CI machines

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* More timeout fixes

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* More test timeout fixes

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Tweak timeout for one more test

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Final tweaks

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* One more ignore

Signed-off-by: AlexDBlack <blacka101@gmail.com>
master
Alex Black 2020-01-04 13:45:07 +11:00 committed by GitHub
parent ac0d249f07
commit 29104083cc
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
375 changed files with 3944 additions and 4078 deletions
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6
datavec/datavec-api/src/test/java/org/datavec/api/records/reader/impl/CSVRecordReaderTest.java
View File

@ -311,7 +311,11 @@ public class CSVRecordReaderTest {
rr.reset();
fail("Expected exception");
} catch (Exception e){
e.printStackTrace();
String msg = e.getMessage();
String msg2 = e.getCause().getMessage();
assertTrue(msg, msg.contains("Error during LineRecordReader reset"));
assertTrue(msg2, msg2.contains("Reset not supported from streams"));
// e.printStackTrace();
}
}

22
datavec/datavec-api/src/test/java/org/datavec/api/records/reader/impl/LineReaderTest.java
View File

@ -55,8 +55,7 @@ public class LineReaderTest {
@Test
public void testLineReader() throws Exception {
String tempDir = System.getProperty("java.io.tmpdir");
File tmpdir = new File(tempDir, "tmpdir-testLineReader");
File tmpdir = testDir.newFolder();
if (tmpdir.exists())
tmpdir.delete();
tmpdir.mkdir();
@ -84,12 +83,6 @@ public class LineReaderTest {
}
assertEquals(9, count);
try {
FileUtils.deleteDirectory(tmpdir);
} catch (Exception e) {
e.printStackTrace();
}
}
@Test
@ -145,13 +138,6 @@ public class LineReaderTest {
assertEquals(2, subset.size());
assertEquals(out3.get(4), subset.get(0));
assertEquals(out3.get(7), subset.get(1));
try {
FileUtils.deleteDirectory(tmpdir);
} catch (Exception e) {
e.printStackTrace();
}
}
@Test
@ -177,11 +163,5 @@ public class LineReaderTest {
}
assertEquals(9, count);
try {
FileUtils.deleteDirectory(tmpdir);
} catch (Exception e) {
e.printStackTrace();
}
}
}

6
datavec/datavec-data/datavec-data-image/src/test/java/org/datavec/image/transform/JsonYamlTest.java
View File

@ -66,9 +66,9 @@ public class JsonYamlTest {
String asJson = itp.toJson();
String asYaml = itp.toYaml();
System.out.println(asJson);
System.out.println("\n\n\n");
System.out.println(asYaml);
// System.out.println(asJson);
// System.out.println("\n\n\n");
// System.out.println(asYaml);
ImageWritable img = TestImageTransform.makeRandomImage(0, 0, 3);
ImageWritable imgJson = new ImageWritable(img.getFrame().clone());

7
datavec/datavec-spark-inference-parent/datavec-spark-inference-model/src/test/java/org/datavec/spark/transform/CSVSparkTransformTest.java
View File

@ -60,7 +60,7 @@ public class CSVSparkTransformTest {
Base64NDArrayBody body = csvSparkTransform.toArray(new SingleCSVRecord(values));
INDArray fromBase64 = Nd4jBase64.fromBase64(body.getNdarray());
assertTrue(fromBase64.isVector());
System.out.println("Base 64ed array " + fromBase64);
// System.out.println("Base 64ed array " + fromBase64);
}
@Test
@ -125,7 +125,7 @@ public class CSVSparkTransformTest {
SequenceBatchCSVRecord transformed = csvSparkTransform.transformSequence(sequenceBatchCSVRecord);
assertNotNull(transformed.getRecords());
System.out.println(transformed);
// System.out.println(transformed);
}
@ -153,7 +153,8 @@ public class CSVSparkTransformTest {
new SingleCSVRecord(data2)));
final CSVSparkTransform transform = new CSVSparkTransform(transformProcess);
System.out.println(transform.transformSequenceIncremental(batchCsvRecord));
// System.out.println(transform.transformSequenceIncremental(batchCsvRecord));
transform.transformSequenceIncremental(batchCsvRecord);
assertEquals(3,Nd4jBase64.fromBase64(transform.transformSequenceArrayIncremental(batchCsvRecord).getNdarray()).rank());
}

4
datavec/datavec-spark-inference-parent/datavec-spark-inference-model/src/test/java/org/datavec/spark/transform/ImageSparkTransformTest.java
View File

@ -54,7 +54,7 @@ public class ImageSparkTransformTest {
Base64NDArrayBody body = imgSparkTransform.toArray(imgRecord);
INDArray fromBase64 = Nd4jBase64.fromBase64(body.getNdarray());
System.out.println("Base 64ed array " + fromBase64);
// System.out.println("Base 64ed array " + fromBase64);
assertEquals(1, fromBase64.size(0));
}
@ -78,7 +78,7 @@ public class ImageSparkTransformTest {
Base64NDArrayBody body = imgSparkTransform.toArray(batch);
INDArray fromBase64 = Nd4jBase64.fromBase64(body.getNdarray());
System.out.println("Base 64ed array " + fromBase64);
// System.out.println("Base 64ed array " + fromBase64);
assertEquals(3, fromBase64.size(0));
}
}

6
datavec/datavec-spark-inference-parent/datavec-spark-inference-server/src/test/java/org/datavec/spark/transform/ImageSparkTransformServerTest.java
View File

@ -120,7 +120,7 @@ public class ImageSparkTransformServerTest {
INDArray batchResult = getNDArray(jsonNodeBatch);
assertEquals(3, batchResult.size(0));
System.out.println(array);
// System.out.println(array);
}
@Test
@ -136,7 +136,7 @@ public class ImageSparkTransformServerTest {
INDArray batchResult = getNDArray(jsonNode);
assertEquals(3, batchResult.size(0));
System.out.println(batchResult);
// System.out.println(batchResult);
}
@Test
@ -153,7 +153,7 @@ public class ImageSparkTransformServerTest {
INDArray result = getNDArray(jsonNode);
assertEquals(1, result.size(0));
System.out.println(result);
// System.out.println(result);
}
public INDArray getNDArray(JsonNode node) throws IOException {

12
datavec/datavec-spark/src/test/java/org/datavec/spark/transform/analysis/TestAnalysis.java
View File

@ -72,7 +72,9 @@ public class TestAnalysis extends BaseSparkTest {
DataAnalysis da = AnalyzeSpark.analyze(schema, rdd);
String daString = da.toString();
System.out.println(da);
// System.out.println(da);
da.toJson();
da.toString();
List<ColumnAnalysis> ca = da.getColumnAnalysis();
assertEquals(5, ca.size());
@ -151,7 +153,7 @@ public class TestAnalysis extends BaseSparkTest {
assertEquals(1, countD[countD.length - 1]);
File f = Files.createTempFile("datavec_spark_analysis_UITest", ".html").toFile();
System.out.println(f.getAbsolutePath());
// System.out.println(f.getAbsolutePath());
f.deleteOnExit();
HtmlAnalysis.createHtmlAnalysisFile(da, f);
}
@ -210,7 +212,7 @@ public class TestAnalysis extends BaseSparkTest {
for( int i=1; i<10; i++ ){
counter.merge(counters.get(i));
sparkCounter.merge(sparkCounters.get(i));
System.out.println();
// System.out.println();
}
assertEquals(sc1.sampleStdev(), counter.getStddev(false), 1e-6);
assertEquals(sparkCounter.sampleStdev(), counter.getStddev(false), 1e-6);
@ -356,7 +358,9 @@ public class TestAnalysis extends BaseSparkTest {
JavaRDD<List<Writable>> rdd = sc.parallelize(data);
DataAnalysis da = AnalyzeSpark.analyze(s, rdd);
System.out.println(da);
// System.out.println(da);
da.toString();
da.toJson();
}
}

68
deeplearning4j/deeplearning4j-common-tests/pom.xml Normal file
View File

@ -0,0 +1,68 @@
<?xml version="1.0" encoding="UTF-8"?>
<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~ Copyright (c) 2020 Konduit K.K.
~
~ This program and the accompanying materials are made available under the
~ terms of the Apache License, Version 2.0 which is available at
~ https://www.apache.org/licenses/LICENSE-2.0.
~
~ Unless required by applicable law or agreed to in writing, software
~ distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
~ WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
~ License for the specific language governing permissions and limitations
~ under the License.
~
~ SPDX-License-Identifier: Apache-2.0
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<parent>
<artifactId>deeplearning4j-parent</artifactId>
<groupId>org.deeplearning4j</groupId>
<version>1.0.0-SNAPSHOT</version>
</parent>
<modelVersion>4.0.0</modelVersion>
<artifactId>deeplearning4j-common-tests</artifactId>
<dependencies>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-api</artifactId>
<version>${project.version}</version>
</dependency>
</dependencies>
<profiles>
<profile>
<id>test-nd4j-native</id>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-native</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
</profile>
<profile>
<id>test-nd4j-cuda-10.2</id>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-cuda-10.2</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
</profile>
</profiles>
</project>

22
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/BaseDL4JTest.java → deeplearning4j/deeplearning4j-common-tests/src/main/java/org/deeplearning4j/BaseDL4JTest.java
View File

@ -1,5 +1,6 @@
/*******************************************************************************
/* ******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019-2020 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
@ -23,7 +24,7 @@ import org.junit.Before;
import org.junit.Rule;
import org.junit.rules.TestName;
import org.junit.rules.Timeout;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.config.ND4JSystemProperties;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
@ -31,24 +32,28 @@ import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.profiler.ProfilerConfig;
import java.lang.management.ManagementFactory;
import java.lang.management.ThreadMXBean;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import static org.junit.Assert.assertNull;
@Slf4j
public class BaseDL4JTest {
public abstract class BaseDL4JTest {
@Rule
public TestName name = new TestName();
@Rule
public Timeout timeout = Timeout.seconds(30);
public Timeout timeout = Timeout.millis(getTimeoutMilliseconds());
protected long startTime;
protected int threadCountBefore;
/**
* Override this method to set the default timeout for methods in the test class
*/
public long getTimeoutMilliseconds(){
return 30000;
}
/**
* Override this to set the profiling mode for the tests defined in the child class
*/
@ -70,6 +75,9 @@ public class BaseDL4JTest {
@Before
public void beforeTest(){
log.info("{}.{}", getClass().getSimpleName(), name.getMethodName());
//Suppress ND4J initialization - don't need this logged for every test...
System.setProperty(ND4JSystemProperties.LOG_INITIALIZATION, "false");
System.setProperty(ND4JSystemProperties.ND4J_IGNORE_AVX, "true");
Nd4j.getExecutioner().setProfilingMode(getProfilingMode());
Nd4j.getExecutioner().setProfilingConfig(ProfilerConfig.builder().build());
Nd4j.setDefaultDataTypes(getDataType(), getDefaultFPDataType());

6
deeplearning4j/deeplearning4j-core/pom.xml
View File

@ -95,6 +95,12 @@
<artifactId>junit</artifactId>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-common-tests</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>

1
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/TestUtils.java
View File

@ -75,7 +75,6 @@ public class TestUtils {
}
public static ComputationGraph testModelSerialization(ComputationGraph net){
ComputationGraph restored;
try {
ByteArrayOutputStream baos = new ByteArrayOutputStream();

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java
View File

@ -1006,7 +1006,9 @@ public class RecordReaderDataSetiteratorTest extends BaseDL4JTest {
for (RecordMetaData m : meta) {
Record r = csv.loadFromMetaData(m);
INDArray row = ds.getFeatures().getRow(i);
System.out.println(m.getLocation() + "\t" + r.getRecord() + "\t" + row);
if(i <= 3) {
System.out.println(m.getLocation() + "\t" + r.getRecord() + "\t" + row);
}
for (int j = 0; j < 4; j++) {
double exp = r.getRecord().get(j).toDouble();

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/AsyncDataSetIteratorTest.java
View File

@ -183,7 +183,7 @@ public class AsyncDataSetIteratorTest extends BaseDL4JTest {
}
adsi.reset();
log.info("Epoch {} finished...", e);
// log.info("Epoch {} finished...", e);
}
}
@ -215,7 +215,7 @@ public class AsyncDataSetIteratorTest extends BaseDL4JTest {
}
adsi.reset();
log.info("Epoch {} finished...", e);
// log.info("Epoch {} finished...", e);
}
}
}

9
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/DataSetIteratorTest.java
View File

@ -57,6 +57,11 @@ import static org.junit.Assert.*;
public class DataSetIteratorTest extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 90000;
}
@Test
public void testBatchSizeOfOneIris() throws Exception {
//Test for (a) iterators returning correct number of examples, and
@ -190,7 +195,7 @@ public class DataSetIteratorTest extends BaseDL4JTest {
INDArray output = model.output(dataTest.getFeatures());
Evaluation eval = new Evaluation(outputNum);
eval.eval(dataTest.getLabels(), output);
System.out.println(eval.stats());
// System.out.println(eval.stats());
}
@Test
@ -257,7 +262,7 @@ public class DataSetIteratorTest extends BaseDL4JTest {
INDArray output = model.output(testDS.getFeatures());
eval.eval(testDS.getLabels(), output);
}
System.out.println(eval.stats(true));
// System.out.println(eval.stats(true));
listener.exportScores(System.out);
}

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/tools/VariableMultiTimeseriesGenerator.java
View File

@ -68,8 +68,8 @@ public class VariableMultiTimeseriesGenerator implements MultiDataSetIterator {
int localMaxima = isFirst && firstMaxima > 0 ? firstMaxima
: minTS == maxTS ? minTS : rng.nextInt(maxTS - minTS) + minTS;
if (isFirst)
log.info("Local maxima: {}", localMaxima);
// if (isFirst)
// log.info("Local maxima: {}", localMaxima);
isFirst = false;

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/tools/VariableTimeseriesGenerator.java
View File

@ -69,8 +69,8 @@ public class VariableTimeseriesGenerator implements DataSetIterator {
int localMaxima = isFirst && firstMaxima > 0 ? firstMaxima
: minTS == maxTS ? minTS : rng.nextInt(maxTS - minTS) + minTS;
if (isFirst)
log.info("Local maxima: {}", localMaxima);
// if (isFirst)
// log.info("Local maxima: {}", localMaxima);
isFirst = false;

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/eval/EvalJsonTest.java
View File

@ -54,7 +54,7 @@ public class EvalJsonTest extends BaseDL4JTest {
@Test
public void testSerde() {
boolean print = true;
boolean print = false;
Nd4j.getRandom().setSeed(12345);
Evaluation evaluation = new Evaluation();
@ -105,7 +105,7 @@ public class EvalJsonTest extends BaseDL4JTest {
@Test
public void testSerdeExactRoc() {
Nd4j.getRandom().setSeed(12345);
boolean print = true;
boolean print = false;
ROC roc = new ROC(0);
ROCBinary roc2 = new ROCBinary(0);

34
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/eval/EvalTest.java
View File

@ -131,11 +131,15 @@ public class EvalTest extends BaseDL4JTest {
org.nd4j.evaluation.classification.Evaluation evalViaMethod = model.evaluate(new ListDataSetIterator<>(Collections.singletonList(test)));
checkEvaluationEquality(eval, evalViaMethod);
System.out.println(eval.getConfusionMatrix().toString());
System.out.println(eval.getConfusionMatrix().toCSV());
System.out.println(eval.getConfusionMatrix().toHTML());
// System.out.println(eval.getConfusionMatrix().toString());
// System.out.println(eval.getConfusionMatrix().toCSV());
// System.out.println(eval.getConfusionMatrix().toHTML());
// System.out.println(eval.confusionToString());
System.out.println(eval.confusionToString());
eval.getConfusionMatrix().toString();
eval.getConfusionMatrix().toCSV();
eval.getConfusionMatrix().toHTML();
eval.confusionToString();
}
private static void assertMapEquals(Map<Integer, Integer> first, Map<Integer, Integer> second) {
@ -205,9 +209,10 @@ public class EvalTest extends BaseDL4JTest {
e.eval(ds.getLabels(), out, meta); //*** New - evaluate and also store metadata ***
}
System.out.println(e.stats());
// System.out.println(e.stats());
e.stats();
System.out.println("\n\n*** Prediction Errors: ***");
// System.out.println("\n\n*** Prediction Errors: ***");
List<org.nd4j.evaluation.meta.Prediction> errors = e.getPredictionErrors(); //*** New - get list of prediction errors from evaluation ***
List<RecordMetaData> metaForErrors = new ArrayList<>();
@ -219,10 +224,11 @@ public class EvalTest extends BaseDL4JTest {
int count = 0;
for (org.nd4j.evaluation.meta.Prediction t : errors) {
System.out.println(t + "\t\tRaw Data: "
+ csv.loadFromMetaData((RecordMetaData) t.getRecordMetaData()).getRecord() //*** New - load subset of data from MetaData object (usually batched for efficiency) ***
+ "\tNormalized: " + ds.getFeatures().getRow(count) + "\tLabels: "
+ ds.getLabels().getRow(count) + "\tNetwork predictions: " + output.getRow(count));
String s = t + "\t\tRaw Data: "
+ csv.loadFromMetaData((RecordMetaData) t.getRecordMetaData()).getRecord() //*** New - load subset of data from MetaData object (usually batched for efficiency) ***
+ "\tNormalized: " + ds.getFeatures().getRow(count) + "\tLabels: "
+ ds.getLabels().getRow(count) + "\tNetwork predictions: " + output.getRow(count);
// System.out.println(s);
count++;
}
@ -322,9 +328,9 @@ public class EvalTest extends BaseDL4JTest {
List<DataSet> l = Arrays.asList(new DataSet(in1, out1, null, lMask1), new DataSet(in2, out2, null, lMask2));
DataSetIterator iter = new ExistingDataSetIterator(l);
System.out.println("Net 1 eval");
// System.out.println("Net 1 eval");
org.nd4j.evaluation.IEvaluation[] e1 = net1.doEvaluation(iter, new org.nd4j.evaluation.classification.Evaluation(), new org.nd4j.evaluation.classification.ROCMultiClass(), new org.nd4j.evaluation.regression.RegressionEvaluation());
System.out.println("Net 2 eval");
// System.out.println("Net 2 eval");
org.nd4j.evaluation.IEvaluation[] e2 = net2.doEvaluation(iter, new org.nd4j.evaluation.classification.Evaluation(), new org.nd4j.evaluation.classification.ROCMultiClass(), new org.nd4j.evaluation.regression.RegressionEvaluation());
assertEquals(e1[0], e2[0]);
@ -403,9 +409,9 @@ public class EvalTest extends BaseDL4JTest {
List<DataSet> l = Arrays.asList(new DataSet(in1, out1), new DataSet(in2, out2));
DataSetIterator iter = new ExistingDataSetIterator(l);
System.out.println("Eval net 1");
// System.out.println("Eval net 1");
org.nd4j.evaluation.IEvaluation[] e1 = net1.doEvaluation(iter, new org.nd4j.evaluation.classification.Evaluation(), new org.nd4j.evaluation.classification.ROCMultiClass(), new org.nd4j.evaluation.regression.RegressionEvaluation());
System.out.println("Eval net 2");
// System.out.println("Eval net 2");
org.nd4j.evaluation.IEvaluation[] e2 = net2.doEvaluation(iter, new org.nd4j.evaluation.classification.Evaluation(), new org.nd4j.evaluation.classification.ROCMultiClass(), new org.nd4j.evaluation.regression.RegressionEvaluation());
assertEquals(e1[0], e2[0]);

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/eval/EvaluationToolsTests.java
View File

@ -117,7 +117,7 @@ public class EvaluationToolsTests extends BaseDL4JTest {
String str = EvaluationTools.rocChartToHtml(roc, Arrays.asList("setosa", "versicolor", "virginica"));
System.out.println(str);
// System.out.println(str);
}
}

31
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/AttentionLayerTest.java
View File

@ -46,12 +46,6 @@ public class AttentionLayerTest extends BaseDL4JTest {
@Rule
public ExpectedException exceptionRule = ExpectedException.none();
private static final boolean PRINT_RESULTS = true;
private static final boolean RETURN_ON_FIRST_FAILURE = false;
private static final double DEFAULT_EPS = 1e-6;
private static final double DEFAULT_MAX_REL_ERROR = 1e-3;
private static final double DEFAULT_MIN_ABS_ERROR = 1e-8;
@Test
public void testSelfAttentionLayer() {
int nIn = 3;
@ -104,8 +98,8 @@ public class AttentionLayerTest extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, inMask, null, true, 100);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).inputMask(inMask).subset(true).maxPerParam(100));
assertTrue(name, gradOK);
}
}
@ -165,8 +159,8 @@ public class AttentionLayerTest extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, inMask, null, true, 100);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).inputMask(inMask).subset(true).maxPerParam(100));
assertTrue(name, gradOK);
}
}
@ -226,8 +220,8 @@ public class AttentionLayerTest extends BaseDL4JTest {
String name = "testLearnedSelfAttentionLayer() - mb=" + mb + ", tsLength = " + tsLength + ", maskType=" + maskType + ", projectInput = " + projectInput;
System.out.println("Starting test: " + name);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, inMask, null, true, 100);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).inputMask(inMask).subset(true).maxPerParam(100));
assertTrue(name, gradOK);
}
}
@ -320,8 +314,8 @@ public class AttentionLayerTest extends BaseDL4JTest {
net.init();
//System.out.println("Original");
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, inMask, null, true, 100, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).inputMask(inMask).subset(true).maxPerParam(100));
assertTrue(name, gradOK);
}
}
@ -383,8 +377,8 @@ public class AttentionLayerTest extends BaseDL4JTest {
ComputationGraph net = new ComputationGraph(graph);
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{in}, new INDArray[]{labels}, inMask != null ? new INDArray[]{inMask} : null, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(net).inputs(new INDArray[]{in})
.labels(new INDArray[]{labels}).inputMask(inMask != null ? new INDArray[]{inMask} : null).subset(true).maxPerParam(100));
assertTrue(name, gradOK);
}
}
@ -445,9 +439,8 @@ public class AttentionLayerTest extends BaseDL4JTest {
ComputationGraph net = new ComputationGraph(graph);
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{in},
new INDArray[]{labels}, inMask != null ? new INDArray[]{inMask} : null, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(net).inputs(new INDArray[]{in})
.labels(new INDArray[]{labels}).inputMask(inMask != null ? new INDArray[]{inMask} : null));
assertTrue(name, gradOK);
}
}

101
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/BNGradientCheckTest.java
View File

@ -56,11 +56,6 @@ import static org.junit.Assert.assertTrue;
*
*/
public class BNGradientCheckTest extends BaseDL4JTest {
private static final boolean PRINT_RESULTS = true;
private static final boolean RETURN_ON_FIRST_FAILURE = false;
private static final double DEFAULT_EPS = 1e-5;
private static final double DEFAULT_MAX_REL_ERROR = 1e-5;
private static final double DEFAULT_MIN_ABS_ERROR = 1e-9;
static {
Nd4j.setDataType(DataType.DOUBLE);
@ -93,17 +88,15 @@ public class BNGradientCheckTest extends BaseDL4JTest {
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
mln.init();
if (PRINT_RESULTS) {
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
//Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
//i.e., runningMean = decay * runningMean + (1-decay) * batchMean
//However, numerical gradient will be 0 as forward pass doesn't depend on this "parameter"
Set<String> excludeParams = new HashSet<>(Arrays.asList("1_mean", "1_var", "1_log10stdev"));
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, excludeParams);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
@ -140,17 +133,15 @@ public class BNGradientCheckTest extends BaseDL4JTest {
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
mln.init();
if (PRINT_RESULTS) {
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
//Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
//i.e., runningMean = decay * runningMean + (1-decay) * batchMean
//However, numerical gradient will be 0 as forward pass doesn't depend on this "parameter"
Set<String> excludeParams = new HashSet<>(Arrays.asList("1_mean", "1_var", "1_log10stdev"));
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, excludeParams);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
@ -220,7 +211,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
String name = new Object() {
}.getClass().getEnclosingMethod().getName();
System.out.println("Num params: " + mln.numParams());
// System.out.println("Num params: " + mln.numParams());
if (doLearningFirst) {
//Run a number of iterations of learning
@ -241,20 +232,18 @@ public class BNGradientCheckTest extends BaseDL4JTest {
assertTrue(msg, scoreAfter < 0.9 * scoreBefore);
}
if (PRINT_RESULTS) {
System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf
+ ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
for (int k = 0; k < mln.getnLayers(); k++)
System.out.println("Layer " + k + " # params: " + mln.getLayer(k).numParams());
}
System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf
+ ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
// for (int k = 0; k < mln.getnLayers(); k++)
// System.out.println("Layer " + k + " # params: " + mln.getLayer(k).numParams());
//Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
//i.e., runningMean = decay * runningMean + (1-decay) * batchMean
//However, numerical gradient will be 0 as forward pass doesn't depend on this "parameter"
Set<String> excludeParams = new HashSet<>(Arrays.asList("1_mean", "1_var", "3_mean", "3_var", "1_log10stdev", "3_log10stdev"));
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 25, excludeParams); //Most params are in output layer, only these should be skipped with this threshold
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).excludeParams(excludeParams).subset(true).maxPerParam(25)); //Most params are in output layer, only these should be skipped with this threshold
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
@ -347,20 +336,18 @@ public class BNGradientCheckTest extends BaseDL4JTest {
assertTrue(msg, scoreAfter < 0.8 * scoreBefore);
}
if (PRINT_RESULTS) {
System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf
+ ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
for (int k = 0; k < mln.getnLayers(); k++)
System.out.println("Layer " + k + " # params: " + mln.getLayer(k).numParams());
}
System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf
+ ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
// for (int k = 0; k < mln.getnLayers(); k++)
// System.out.println("Layer " + k + " # params: " + mln.getLayer(k).numParams());
//Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
//i.e., runningMean = decay * runningMean + (1-decay) * batchMean
//However, numerical gradient will be 0 as forward pass doesn't depend on this "parameter"
Set<String> excludeParams = new HashSet<>(Arrays.asList("1_mean", "1_var", "3_mean", "3_var", "1_log10stdev", "3_log10stdev"));
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, excludeParams);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
@ -396,17 +383,15 @@ public class BNGradientCheckTest extends BaseDL4JTest {
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
mln.init();
if (PRINT_RESULTS) {
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
//Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
//i.e., runningMean = decay * runningMean + (1-decay) * batchMean
//However, numerical gradient will be 0 as forward pass doesn't depend on this "parameter"
Set<String> excludeParams = new HashSet<>(Arrays.asList("1_mean", "1_var", "1_log10stdev"));
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, excludeParams);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
@ -443,17 +428,15 @@ public class BNGradientCheckTest extends BaseDL4JTest {
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
mln.init();
if (PRINT_RESULTS) {
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
//Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
//i.e., runningMean = decay * runningMean + (1-decay) * batchMean
//However, numerical gradient will be 0 as forward pass doesn't depend on this "parameter"
Set<String> excludeParams = new HashSet<>(Arrays.asList("1_mean", "1_var", "1_log10stdev"));
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, excludeParams);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
@ -496,9 +479,8 @@ public class BNGradientCheckTest extends BaseDL4JTest {
//i.e., runningMean = decay * runningMean + (1-decay) * batchMean
//However, numerical gradient will be 0 as forward pass doesn't depend on this "parameter"
Set<String> excludeParams = new HashSet<>(Arrays.asList("bn_mean", "bn_var"));
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{input},
new INDArray[]{labels}, null, null, excludeParams);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(net).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(net);
@ -585,21 +567,18 @@ public class BNGradientCheckTest extends BaseDL4JTest {
assertTrue(msg, scoreAfter < 0.9 * scoreBefore);
}
if (PRINT_RESULTS) {
System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf
+ ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
for (int k = 0; k < net.getNumLayers(); k++)
System.out.println("Layer " + k + " # params: " + net.getLayer(k).numParams());
}
System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf
+ ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
// for (int k = 0; k < net.getNumLayers(); k++)
// System.out.println("Layer " + k + " # params: " + net.getLayer(k).numParams());
//Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
//i.e., runningMean = decay * runningMean + (1-decay) * batchMean
//However, numerical gradient will be 0 as forward pass doesn't depend on this "parameter"
Set<String> excludeParams = new HashSet<>(Arrays.asList("1_mean", "1_var", "3_mean", "3_var", "1_log10stdev", "3_log10stdev"));
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE,
new INDArray[]{input}, new INDArray[]{labels}, null, null, excludeParams);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(net).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(net);

24
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DGradientCheckTest.java
View File

@ -108,8 +108,8 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -188,8 +188,8 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -272,8 +272,8 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -349,8 +349,8 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -414,8 +414,8 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
INDArray label = TestUtils.randomOneHot(2, finalNOut);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, f, label, fm, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(f)
.labels(label).inputMask(fm));
assertTrue(s, gradOK);
TestUtils.testModelSerialization(net);
@ -509,8 +509,8 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
INDArray label = TestUtils.randomOneHotTimeSeries(2, finalNOut, (int)outSize2);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, f, label, fm, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(f)
.labels(label).inputMask(fm));
assertTrue(s, gradOK);
TestUtils.testModelSerialization(net);

125
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java
View File

@ -144,14 +144,13 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
log.info(msg);
for (int j = 0; j < net.getnLayers(); j++) {
log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
// for (int j = 0; j < net.getnLayers(); j++) {
// log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
// }
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 128);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(128));
assertTrue(msg, gradOK);
@ -248,14 +247,13 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
log.info(msg);
for (int j = 0; j < net.getnLayers(); j++) {
log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
// for (int j = 0; j < net.getnLayers(); j++) {
// log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
// }
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS,
RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 512);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(512));
assertTrue(msg, gradOK);
@ -431,9 +429,9 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
log.info(msg);
for (int j = 0; j < net.getnLayers(); j++) {
log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
// for (int j = 0; j < net.getnLayers(); j++) {
// log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
// }
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
@ -530,9 +528,9 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
log.info(msg);
for (int j = 0; j < net.getnLayers(); j++) {
log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
// for (int j = 0; j < net.getnLayers(); j++) {
// log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
// }
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
@ -547,4 +545,95 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
}
}
}
@Test
public void testDeconv3d() {
Nd4j.getRandom().setSeed(12345);
// Note: we checked this with a variety of parameters, but it takes a lot of time.
int[] depths = {8, 8, 9};
int[] heights = {8, 9, 9};
int[] widths = {8, 8, 9};
int[][] kernels = {{2, 2, 2}, {3, 3, 3}, {2, 3, 2}};
int[][] strides = {{1, 1, 1}, {1, 1, 1}, {2, 2, 2}};
Activation[] activations = {Activation.SIGMOID, Activation.TANH, Activation.IDENTITY};
ConvolutionMode[] modes = {ConvolutionMode.Truncate, ConvolutionMode.Same, ConvolutionMode.Same};
int[] mbs = {1, 3, 2};
Convolution3D.DataFormat[] dataFormats = new Convolution3D.DataFormat[]{Convolution3D.DataFormat.NCDHW, Convolution3D.DataFormat.NDHWC, Convolution3D.DataFormat.NCDHW};
int convNIn = 2;
int finalNOut = 2;
int[] deconvOut = {2, 3, 4};
for (int i = 0; i < activations.length; i++) {
Activation afn = activations[i];
int miniBatchSize = mbs[i];
int depth = depths[i];
int height = heights[i];
int width = widths[i];
ConvolutionMode mode = modes[i];
int[] kernel = kernels[i];
int[] stride = strides[i];
Convolution3D.DataFormat df = dataFormats[i];
int dOut = deconvOut[i];
INDArray input;
if (df == Convolution3D.DataFormat.NDHWC) {
input = Nd4j.rand(new int[]{miniBatchSize, depth, height, width, convNIn});
} else {
input = Nd4j.rand(new int[]{miniBatchSize, convNIn, depth, height, width});
}
INDArray labels = Nd4j.zeros(miniBatchSize, finalNOut);
for (int j = 0; j < miniBatchSize; j++) {
labels.putScalar(new int[]{j, j % finalNOut}, 1.0);
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp())
.weightInit(new NormalDistribution(0, 0.1))
.list()
.layer(0, new Convolution3D.Builder().activation(afn).kernelSize(kernel)
.stride(stride).nIn(convNIn).nOut(dOut).hasBias(false)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(1, new Deconvolution3D.Builder().activation(afn).kernelSize(kernel)
.stride(stride).nOut(dOut).hasBias(false)
.convolutionMode(mode).dataFormat(df)
.build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(finalNOut).build())
.setInputType(InputType.convolutional3D(df, depth, height, width, convNIn)).build();
String json = conf.toJson();
MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, c2);
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = "DataFormat = " + df + ", minibatch size = " + miniBatchSize + ", activationFn=" + afn
+ ", kernel = " + Arrays.toString(kernel) + ", stride = "
+ Arrays.toString(stride) + ", mode = " + mode.toString()
+ ", input depth " + depth + ", input height " + height
+ ", input width " + width;
if (PRINT_RESULTS) {
log.info(msg);
// for (int j = 0; j < net.getnLayers(); j++) {
// log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
// }
}
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(128));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
}
}
}

57
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java
View File

@ -122,8 +122,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation="
+ outputActivation + ", doLearningFirst=" + doLearningFirst);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -213,8 +213,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
System.out.println(testName + "- activationFn=" + afn + ", lossFn=" + lf
+ ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -275,8 +275,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
@ -336,8 +336,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
@ -346,8 +346,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
//Also check compgraph:
ComputationGraph cg = net.toComputationGraph();
gradOK = GradientCheckUtil.checkGradients(cg, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{input}, new INDArray[]{labels});
gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(cg).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
assertTrue(msg + " - compgraph", gradOK);
TestUtils.testModelSerialization(net);
@ -399,8 +399,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -468,8 +468,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -793,9 +793,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
+ convFirst;
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input,
labels, null, null, true, 128);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(128));
assertTrue(msg, gradOK);
@ -863,8 +862,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -937,8 +936,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
+ k + ", s=" + s + ", d=" + d + ", cm=" + cm;
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 100);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(100));
assertTrue(msg, gradOK);
@ -1009,8 +1008,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
+ k + ", s=" + s + ", d=" + d + ", cm=" + cm;
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 50); //Most params are in output layer
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(50)); //Most params are in output layer
assertTrue(msg, gradOK);
@ -1160,12 +1159,12 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 160);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(160));
assertTrue(msg, gradOK);
@ -1235,8 +1234,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
+ k + ", nIn=" + nIn + ", depthMul=" + depthMultiplier + ", s=" + s + ", cm=" + cm;
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 256);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(256));
assertTrue(msg, gradOK);

6
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CapsnetGradientCheckTest.java
View File

@ -110,10 +110,8 @@ public class CapsnetGradientCheckTest extends BaseDL4JTest {
" capsules with " + capsuleDim + " dimensions and " + routing + " routings";
System.out.println(msg);
boolean gradOK = GradientCheckUtil
.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input,
labels, null, null, true, 100);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(100));
assertTrue(msg, gradOK);

12
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/DropoutGradientCheck.java
View File

@ -36,6 +36,7 @@ import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.function.Consumer;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
@ -171,10 +172,15 @@ public class DropoutGradientCheck extends BaseDL4JTest {
INDArray[] in = new INDArray[]{Nd4j.rand(mb, 5)};
INDArray[] l = new INDArray[]{TestUtils.randomOneHot(mb, 5)};
boolean ok = GradientCheckUtil.checkGradients(cg, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, l, null, null, null, 12345);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(cg).inputs(in)
.labels(l).callEachIter(new Consumer<ComputationGraph>() {
@Override
public void accept(ComputationGraph net) {
Nd4j.getRandom().setSeed(12345);
}
}));
assertTrue(ok);
assertTrue(gradOK);
}
}

24
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GlobalPoolingGradientCheckTests.java
View File

@ -92,8 +92,8 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testLSTMGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = "
+ miniBatchSize);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -150,8 +150,8 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(
"testCnnGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = " + miniBatchSize);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -209,12 +209,12 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testLSTMGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = " + miniBatchSize);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, featuresMask, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).inputMask(featuresMask));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
@ -292,12 +292,12 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testCnnGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = "
+ miniBatchSize);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, inputMask, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).inputMask(inputMask));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);

36
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTests.java
View File

@ -120,8 +120,8 @@ public class GradientCheckTests extends BaseDL4JTest {
System.out.println("testMinibatchApplication() - activationFn=" + afn + ", lossFn="
+ lf + ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -200,8 +200,8 @@ public class GradientCheckTests extends BaseDL4JTest {
System.out.println("testGradientMLP2LayerIrisSimpleRandom() - activationFn=" + afn + ", lossFn="
+ lf + ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -294,8 +294,8 @@ public class GradientCheckTests extends BaseDL4JTest {
System.out.println("testGradientMLP2LayerIrisSimpleRandom() - activationFn=" + afn
+ ", lossFn=" + lf + ", outputActivation=" + outputActivation
+ ", doLearningFirst=" + doLearningFirst + ", l2=" + l2 + ", l1=" + l1);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -339,8 +339,8 @@ public class GradientCheckTests extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testEmbeddingLayerSimple");
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -379,8 +379,8 @@ public class GradientCheckTests extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testEmbeddingLayerSimple");
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -469,8 +469,8 @@ public class GradientCheckTests extends BaseDL4JTest {
+ doLearningFirst + ", l2=" + l2 + ", l1=" + l1;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -539,8 +539,8 @@ public class GradientCheckTests extends BaseDL4JTest {
// expectation in case linear regression(with only element wise multiplication layer): large weight for the fourth weight
log.info("params after learning: " + netGraph.getLayer(1).paramTable());
boolean gradOK = checkGradients(netGraph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{features}, new INDArray[]{labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(netGraph).inputs(new INDArray[]{features})
.labels(new INDArray[]{labels}));
msg = "elementWiseMultiplicationLayerTest() - activationFn=" + "ID" + ", lossFn=" + "Cos-sim"
+ ", outputActivation=" + "Id" + ", doLearningFirst=" + "true";
@ -592,8 +592,8 @@ public class GradientCheckTests extends BaseDL4JTest {
}
String msg = "mask=" + maskArray + ", inputRank=" + inputRank;
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, label, fMask, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(label).inputMask(fMask));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
@ -767,8 +767,8 @@ public class GradientCheckTests extends BaseDL4JTest {
System.out.println("testGradientMLP2LayerIrisSimpleRandom() - activationFn=" + afn + ", lossFn="
+ lf + ", outputActivation=" + outputActivation + ", doLearningFirst="
+ doLearningFirst + ", layerNorm=" + layerNorm);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,

246
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTestsComputationGraph.java
View File

@ -103,13 +103,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testBasicIris()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testBasicIris()";
assertTrue(msg, gradOK);
@ -155,13 +154,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testBasicIrisWithMerging()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testBasicIrisWithMerging()";
assertTrue(msg, gradOK);
@ -213,13 +211,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testBasicIrisWithElementWiseVertex(op=" + op + ")");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testBasicIrisWithElementWiseVertex(op=" + op + ")";
assertTrue(msg, gradOK);
@ -274,13 +271,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testBasicIrisWithElementWiseVertex(op=" + op + ")");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testBasicIrisWithElementWiseVertex(op=" + op + ")";
assertTrue(msg, gradOK);
@ -328,9 +324,8 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
graph.fit(new DataSet(in, labels));
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{in},
new INDArray[]{labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in})
.labels(new INDArray[]{labels}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
}
@ -372,13 +367,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testCnnDepthMerge()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testCnnDepthMerge()";
assertTrue(msg, gradOK);
@ -430,13 +424,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testLSTMWithMerging()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testLSTMWithMerging()";
assertTrue(msg, gradOK);
@ -466,13 +459,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testLSTMWithSubset()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testLSTMWithSubset()";
assertTrue(msg, gradOK);
@ -504,26 +496,24 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testLSTMWithLastTimeStepVertex()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
//First: test with no input mask array
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testLSTMWithLastTimeStepVertex()";
assertTrue(msg, gradOK);
//Second: test with input mask arrays.
INDArray inMask = Nd4j.zeros(3, 5);
inMask.putRow(0, Nd4j.create(new double[] {1, 1, 1, 0, 0}));
inMask.putRow(1, Nd4j.create(new double[] {1, 1, 1, 1, 0}));
inMask.putRow(2, Nd4j.create(new double[] {1, 1, 1, 1, 1}));
graph.setLayerMaskArrays(new INDArray[] {inMask}, null);
gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input}, new INDArray[] {labels});
INDArray inMask = Nd4j.zeros(3, 4);
inMask.putRow(0, Nd4j.create(new double[] {1, 1, 0, 0}));
inMask.putRow(1, Nd4j.create(new double[] {1, 1, 1, 0}));
inMask.putRow(2, Nd4j.create(new double[] {1, 1, 1, 1}));
gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}).inputMask(new INDArray[]{inMask}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
@ -566,13 +556,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testLSTMWithDuplicateToTimeSeries()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input1, input2},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input1, input2})
.labels(new INDArray[]{labels}));
String msg = "testLSTMWithDuplicateToTimeSeries()";
assertTrue(msg, gradOK);
@ -615,13 +604,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testLSTMWithReverseTimeSeriesVertex()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testLSTMWithDuplicateToTimeSeries()";
assertTrue(msg, gradOK);
@ -632,8 +620,8 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
inMask.putRow(1, Nd4j.create(new double[] {1, 1, 0, 1, 0}));
inMask.putRow(2, Nd4j.create(new double[] {1, 1, 1, 1, 1}));
graph.setLayerMaskArrays(new INDArray[] {inMask}, null);
gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input}, new INDArray[] {labels});
gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
@ -671,13 +659,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
String msg = "testMultipleInputsLayer() - minibatchSize = " + mb;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, inputs,
new INDArray[] {out});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(inputs)
.labels(new INDArray[]{out}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
@ -712,13 +699,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
String msg = "testMultipleOutputsLayer() - minibatchSize = " + mb;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {out});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{out}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
@ -759,12 +745,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
String msg = "testMultipleOutputsMergeVertex() - minibatchSize = " + mb;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, new INDArray[] {out});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(input)
.labels(new INDArray[]{out}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
@ -810,13 +796,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
String msg = "testMultipleOutputsMergeVertex() - minibatchSize = " + mb;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input},
new INDArray[] {out});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{out}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
@ -873,19 +858,18 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
Map<String, INDArray> out = graph.feedForward(new INDArray[] {pos, anc, neg}, true);
for (String s : out.keySet()) {
System.out.println(s + "\t" + Arrays.toString(out.get(s).shape()));
}
// for (String s : out.keySet()) {
// System.out.println(s + "\t" + Arrays.toString(out.get(s).shape()));
// }
if (PRINT_RESULTS) {
System.out.println("testBasicIrisTripletStackingL2Loss()");
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {pos, anc, neg},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{pos, anc, neg})
.labels(new INDArray[]{labels}));
String msg = "testBasicIrisTripletStackingL2Loss()";
assertTrue(msg, gradOK);
@ -941,13 +925,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
String msg = "testBasicCenterLoss() - lambda = " + lambda + ", trainFirst = " + train;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {example},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{example})
.labels(new INDArray[]{labels}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
@ -1007,8 +990,8 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
String msg = "testBasicCenterLoss() - trainFirst = " + train;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < net.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -1056,13 +1039,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {in1, in2},
new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
.labels(new INDArray[]{labels}));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(graph);
@ -1115,13 +1097,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {in1, in2},
new INDArray[] {labels1, labels2});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
.labels(new INDArray[]{labels1, labels2}));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(graph);
@ -1174,13 +1155,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {in1, in2},
new INDArray[] {labels1, labels2});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
.labels(new INDArray[]{labels1, labels2}));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(graph);
@ -1238,15 +1218,14 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
graph.setLayerMaskArrays(new INDArray[] {inMask1, inMask2}, null);
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {in1, in2},
new INDArray[] {labels1, labels2}, new INDArray[] {inMask1, inMask2}, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
.labels(new INDArray[]{labels1, labels2}).inputMask(new INDArray[]{inMask1, inMask2}));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(graph);
@ -1298,13 +1277,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {in1, in2},
new INDArray[] {labels1, labels2});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
.labels(new INDArray[]{labels1, labels2}));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(graph);
}
@ -1341,13 +1319,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {in1},
new INDArray[] {labels1});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1})
.labels(new INDArray[]{labels1}));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(graph);
@ -1391,13 +1368,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < graph.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {in1},
new INDArray[] {labels1});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1})
.labels(new INDArray[]{labels1}));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(graph);
@ -1430,12 +1406,12 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testGraphEmbeddingLayerSimple");
for (int j = 0; j < cg.getNumLayers(); j++)
System.out.println("Layer " + j + " # params: " + cg.getLayer(j).numParams());
// for (int j = 0; j < cg.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + cg.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(cg, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR,
PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] {input}, new INDArray[] {labels});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(cg).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testGraphEmbeddingLayerSimple";
assertTrue(msg, gradOK);

38
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTestsMasking.java
View File

@ -51,10 +51,6 @@ import static org.nd4j.linalg.indexing.NDArrayIndex.*;
public class GradientCheckTestsMasking extends BaseDL4JTest {
private static final boolean PRINT_RESULTS = true;
private static final boolean RETURN_ON_FIRST_FAILURE = false;
private static final double DEFAULT_EPS = 1e-6;
private static final double DEFAULT_MAX_REL_ERROR = 1e-3;
private static final double DEFAULT_MIN_ABS_ERROR = 1e-7;
static {
Nd4j.setDataType(DataType.DOUBLE);
@ -130,8 +126,8 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
MultiLayerNetwork mln = new MultiLayerNetwork(conf);
mln.init();
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, maskArr);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).labelMask(maskArr));
String msg = "gradientCheckMaskingOutputSimple() - timeSeriesLength=" + timeSeriesLength
+ ", miniBatchSize=" + 1;
@ -186,12 +182,12 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
if (PRINT_RESULTS) {
System.out.println("testBidirectionalLSTMMasking() - testNum = " + testNum++);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, mask, mask, true, 16);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).inputMask(mask).labelMask(mask).subset(true).maxPerParam(16));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
@ -271,8 +267,8 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, features, labels, null, labelMask);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(features)
.labels(labels).labelMask(labelMask));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
@ -366,8 +362,8 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, features, labels, null, labelMask);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(features)
.labels(labels).labelMask(labelMask));
assertTrue(msg, gradOK);
@ -387,9 +383,8 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
ComputationGraph graph = new ComputationGraph(cg);
graph.init();
gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE,
new INDArray[] {features}, new INDArray[] {labels}, null, new INDArray[]{labelMask}, null);
gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{features})
.labels(new INDArray[]{labels}).labelMask(new INDArray[]{labelMask}));
assertTrue(msg + " (compgraph)", gradOK);
TestUtils.testModelSerialization(graph);
@ -425,8 +420,8 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
assertTrue(lm.sumNumber().intValue() > 0);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, f, l, null, lm);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(f)
.labels(l).labelMask(lm));
assertTrue(gradOK);
//Also ensure score doesn't depend on masked feature or label values
@ -478,9 +473,8 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
assertTrue(lm.sumNumber().intValue() > 0);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{f}, new INDArray[]{l},
null, new INDArray[]{lm});
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(net).inputs(new INDArray[]{f})
.labels(new INDArray[]{l}).labelMask(new INDArray[]{lm}));
assertTrue(gradOK);
//Also ensure score doesn't depend on masked feature or label values

8
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/LRNGradientCheckTests.java
View File

@ -82,10 +82,10 @@ public class LRNGradientCheckTests extends BaseDL4JTest {
MultiLayerNetwork mln = new MultiLayerNetwork(builder.build());
mln.init();
if (PRINT_RESULTS) {
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
// if (PRINT_RESULTS) {
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// }
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);

24
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/LSTMGradientCheckTests.java
View File

@ -124,8 +124,8 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
String testName = "testLSTMBasic(" + (graves ? "GravesLSTM" : "LSTM") + ")";
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -213,12 +213,12 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
+ outputActivation + ", l2=" + l2 + ", l1=" + l1;
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 128);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).subset(true).maxPerParam(128));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(mln);
@ -341,8 +341,8 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
System.out.println("testGradientGravesBidirectionalLSTMFull() - activationFn=" + afn
+ ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", l2=" + l2
+ ", l1=" + l1);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -394,8 +394,8 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
MultiLayerNetwork mln = new MultiLayerNetwork(conf);
mln.init();
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 128);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).subset(true).maxPerParam(128));
String msg = "testGradientGravesLSTMEdgeCases() - timeSeriesLength=" + timeSeriesLength[i]
+ ", miniBatchSize=" + miniBatchSize[i];
@ -452,8 +452,8 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
System.out.println("layer " + i + "\t" + mln.getLayer(i).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 32);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).subset(true).maxPerParam(32));
assertTrue(gradOK);
TestUtils.testModelSerialization(mln);
}

23
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/LossFunctionGradientCheck.java
View File

@ -206,21 +206,19 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
} else {
failed.add(testName);
}
System.out.println("\n\n");
TestUtils.testModelSerialization(net);
}
}
System.out.println("---- Passed ----");
for (String s : passed) {
System.out.println(s);
}
System.out.println("---- Failed ----");
for (String s : failed) {
System.out.println(s);
if(failed.size() > 0) {
System.out.println("---- Passed ----");
for (String s : passed) {
System.out.println(s);
}
System.out.println("---- Failed ----");
for (String s : failed) {
System.out.println(s);
}
}
assertEquals("Tests failed", 0, failed.size());
@ -376,7 +374,6 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
failed.add(testName);
}
System.out.println("\n\n");
TestUtils.testModelSerialization(net);
}
}
@ -684,8 +681,6 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
} else {
failed.add(testName);
}
System.out.println("\n\n");
}
}
}

24
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/OutputLayerGradientChecks.java
View File

@ -136,13 +136,13 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
String testName = "testRnnLossLayer(lf=" + lf + ", maskType=" + mt + ", outputActivation = " + oa + ")";
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
System.out.println("Starting test: " + testName);
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, labelMask);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).labelMask(labelMask));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(mln);
@ -243,13 +243,13 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
String testName = "testCnnLossLayer(lf=" + lf + ", maskType=" + mt + ", outputActivation = " + oa + ")";
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
System.out.println("Starting test: " + testName);
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, labelMask);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).labelMask(labelMask));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(mln);
@ -392,13 +392,13 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
String testName = "testCnn3dLossLayer(dataFormat=" + dataFormat + ",lf=" + lf + ", maskType=" + mt + ", outputActivation = " + oa + ")";
if (PRINT_RESULTS) {
System.out.println(testName);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
System.out.println("Starting test: " + testName);
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, labelMask);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
.labels(labels).labelMask(labelMask));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(mln);

16
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/RnnGradientChecks.java
View File

@ -127,8 +127,8 @@ public class RnnGradientChecks extends BaseDL4JTest {
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, inMask, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).inputMask(inMask));
assertTrue(gradOK);
@ -207,8 +207,8 @@ public class RnnGradientChecks extends BaseDL4JTest {
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, inMask, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).inputMask(inMask));
assertTrue(gradOK);
TestUtils.testModelSerialization(net);
}
@ -282,8 +282,8 @@ public class RnnGradientChecks extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, inMask, null, true, 16);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).inputMask(inMask).subset(true).maxPerParam(16));
assertTrue(name, gradOK);
TestUtils.testModelSerialization(net);
}
@ -346,8 +346,8 @@ public class RnnGradientChecks extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, inMask, null, true, 16);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).inputMask(inMask).subset(true).maxPerParam(16));
assertTrue(name, gradOK);
TestUtils.testModelSerialization(net);
}

16
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/UtilLayerGradientChecks.java
View File

@ -182,9 +182,9 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, label, inMask, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.minAbsoluteError(1e-7)
.labels(label).inputMask(inMask));
assertTrue(gradOK);
TestUtils.testModelSerialization(net);
@ -223,9 +223,8 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
Set<String> excludeParams = new HashSet<>();
excludeParams.addAll(Arrays.asList("1_W", "1_b", "2_W", "2_b"));
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, in, labels, null, null,
false, -1, excludeParams);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(net);
@ -234,9 +233,8 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
//Test ComputationGraph equivalent:
ComputationGraph g = net.toComputationGraph();
boolean gradOKCG = GradientCheckUtil.checkGradients(g, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{in}, new INDArray[]{labels},
null, null, excludeParams);
boolean gradOKCG = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(g).inputs(new INDArray[]{in})
.labels(new INDArray[]{labels}).excludeParams(excludeParams));
assertTrue(gradOKCG);
TestUtils.testModelSerialization(g);

18
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/VaeGradientCheckTests.java
View File

@ -46,7 +46,7 @@ import static org.junit.Assert.assertTrue;
*/
public class VaeGradientCheckTests extends BaseDL4JTest {
private static final boolean PRINT_RESULTS = true;
private static final boolean PRINT_RESULTS = false;
private static final boolean RETURN_ON_FIRST_FAILURE = false;
private static final double DEFAULT_EPS = 1e-6;
private static final double DEFAULT_MAX_REL_ERROR = 1e-3;
@ -122,8 +122,8 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
+ Arrays.toString(decoderSizes) + ", l2=" + l2 + ", l1=" + l1;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@ -193,8 +193,8 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
+ l1;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int l = 0; l < mln.getnLayers(); l++)
System.out.println("Layer " + l + " # params: " + mln.getLayer(l).numParams());
// for (int l = 0; l < mln.getnLayers(); l++)
// System.out.println("Layer " + l + " # params: " + mln.getLayer(l).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradientsPretrainLayer(layer, DEFAULT_EPS,
@ -281,8 +281,8 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
String msg = "testVaePretrainReconstructionDistributions() - " + reconstructionDistributions[i];
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradientsPretrainLayer(layer, DEFAULT_EPS,
@ -323,8 +323,8 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
String msg = "testVaePretrainMultipleSamples() - numSamples = " + numSamples;
if (PRINT_RESULTS) {
System.out.println(msg);
for (int j = 0; j < mln.getnLayers(); j++)
System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
// for (int j = 0; j < mln.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradientsPretrainLayer(layer, DEFAULT_EPS,

8
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/YoloGradientCheckTests.java
View File

@ -120,8 +120,8 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
String msg = "testYoloOutputLayer() - minibatch = " + mb + ", w=" + w + ", h=" + h + ", l1=" + l1[i] + ", l2=" + l2[i];
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 100);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
.labels(labels).subset(true).maxPerParam(100));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(net);
@ -228,8 +228,8 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
INDArray f = ds.getFeatures();
INDArray l = ds.getLabels();
boolean ok = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, f, l, null, null, true, 64);
boolean ok = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(f)
.labels(l).inputMask(null).subset(true).maxPerParam(64));
assertTrue(ok);
TestUtils.testModelSerialization(net);

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/conf/ComputationGraphConfigurationTest.java
View File

@ -130,7 +130,7 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
.setOutputs("out").build();
String json = conf.toJson();
System.out.println(json);
// System.out.println(json);
ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
@ -258,7 +258,7 @@ public class ComputationGraphConfigurationTest extends BaseDL4JTest {
.addVertex("test2", new StaticInnerGraphVertex(4, 5), "in").setOutputs("test", "test2").build();
String json = conf.toJson();
System.out.println(json);
// System.out.println(json);
ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/conf/preprocessor/CustomPreprocessorTest.java
View File

@ -54,7 +54,7 @@ public class CustomPreprocessorTest extends BaseDL4JTest {
String json = conf.toJson();
String yaml = conf.toYaml();
System.out.println(json);
// System.out.println(json);
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, confFromJson);

6
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/dtypes/DTypeTests.java
View File

@ -99,6 +99,11 @@ public class DTypeTests extends BaseDL4JTest {
Convolution1D.class //Alias for Convolution1DLayer
));
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@AfterClass
public static void after() {
ImmutableSet<ClassPath.ClassInfo> info;
@ -545,6 +550,7 @@ public class DTypeTests extends BaseDL4JTest {
.layer(new Convolution3D.Builder().kernelSize(2, 2, 2).stride(1, 1, 1).nOut(3).activation(Activation.TANH).build())
.layer(new Convolution3D.Builder().kernelSize(2, 2, 2).stride(1, 1, 1).nOut(3).activation(Activation.TANH).build())
.layer(new Subsampling3DLayer.Builder().poolingType(PoolingType.AVG).kernelSize(2, 2, 2).stride(2, 2, 2).build())
.layer(new Deconvolution3D.Builder().kernelSize(2,2,2).stride(1,1,1).nIn(3).nOut(3).activation(Activation.TANH).build())
.layer(new Cropping3D.Builder(1, 1, 1, 1, 1, 1).build())
.layer(new ZeroPadding3DLayer.Builder(1, 1, 1, 1, 1, 1).build())
.layer(new ActivationLayer(Activation.LEAKYRELU))

56
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/graph/TestComputationGraphNetwork.java
View File

@ -531,28 +531,38 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
ComputationGraph graph = new ComputationGraph(conf1);
graph.init();
System.out.println(graph.summary());
System.out.println(graph.summary(InputType.feedForward(5)));
// System.out.println(graph.summary());
// System.out.println(graph.summary(InputType.feedForward(5)));
graph.summary();
graph.summary(InputType.feedForward(5));
graph = new ComputationGraph(conf2);
graph.init();
System.out.println(graph.summary());
System.out.println(graph.summary(InputType.recurrent(5)));
// System.out.println(graph.summary());
// System.out.println(graph.summary(InputType.recurrent(5)));
graph.summary();
graph.summary(InputType.recurrent(5));
graph = new ComputationGraph(conf3);
graph.init();
System.out.println(graph.summary());
System.out.println(graph.summary(InputType.convolutional(28, 28, 1)));
// System.out.println(graph.summary());
// System.out.println(graph.summary(InputType.convolutional(28, 28, 1)));
graph.summary();
graph.summary(InputType.convolutional(28, 28, 1));
graph = new ComputationGraph(conf4);
graph.init();
System.out.println(graph.summary());
System.out.println(graph.summary(InputType.convolutional(28, 28, 1), InputType.recurrent(5)));
// System.out.println(graph.summary());
// System.out.println(graph.summary(InputType.convolutional(28, 28, 1), InputType.recurrent(5)));
graph.summary();
graph.summary(InputType.convolutional(28, 28, 1), InputType.recurrent(5));
graph = new ComputationGraph(conf5);
graph.init();
System.out.println(graph.summary());
System.out.println(graph.summary(InputType.convolutional(28, 28, 1)));
// System.out.println(graph.summary());
// System.out.println(graph.summary(InputType.convolutional(28, 28, 1)));
graph.summary();
graph.summary(InputType.convolutional(28, 28, 1));
}
@Test
@ -753,7 +763,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
int nOut = 3;
for(WorkspaceMode ws : WorkspaceMode.values()) {
System.out.println("***** WORKSPACE: " + ws);
// System.out.println("***** WORKSPACE: " + ws);
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(new Adam(0.01))
@ -981,7 +991,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
OptimizationAlgorithm.LBFGS};
for (OptimizationAlgorithm oa : oas) {
System.out.println(oa);
// System.out.println(oa);
ComputationGraphConfiguration conf =
new NeuralNetConfiguration.Builder().optimizationAlgo(oa).graphBuilder()
.addInputs("input")
@ -1065,12 +1075,15 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
ComputationGraph modelToTune = new ComputationGraph(conf);
modelToTune.init();
System.out.println(modelToTune.summary());
// System.out.println(modelToTune.summary());
modelToTune.summary();
ComputationGraph modelNow =
new TransferLearning.GraphBuilder(modelToTune).setFeatureExtractor("denseCentre2").build();
System.out.println(modelNow.summary());
System.out.println(modelNow.summary(InputType.feedForward(10),InputType.feedForward(2)));
// System.out.println(modelNow.summary());
// System.out.println(modelNow.summary(InputType.feedForward(10),InputType.feedForward(2)));
modelNow.summary();
modelNow.summary(InputType.feedForward(10),InputType.feedForward(2));
}
@Test
@ -1315,9 +1328,12 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
ComputationGraph modelExpectedArch = new ComputationGraph(confForArchitecture);
modelExpectedArch.init();
ComputationGraph modelMow = new TransferLearning.GraphBuilder(modelExpectedArch).setFeatureExtractor("layer2").build();
System.out.println(modelExpectedArch.summary());
System.out.println(modelMow.summary());
System.out.println(modelExpectedArch.summary(InputType.recurrent(V_HEIGHT* V_WIDTH* 3)));
// System.out.println(modelExpectedArch.summary());
// System.out.println(modelMow.summary());
// System.out.println(modelExpectedArch.summary(InputType.recurrent(V_HEIGHT* V_WIDTH* 3)));
modelExpectedArch.summary();
modelMow.summary();
modelExpectedArch.summary(InputType.recurrent(V_HEIGHT* V_WIDTH* 3));
}
@Test
@ -2117,8 +2133,8 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
INDArray features = Nd4j.rand(new int[] {dataSize, inputSize});
INDArray labels = Nd4j.rand(new int[] {dataSize, outputSize});
boolean gradOK = GradientCheckUtil.checkGradients(net, 1e-6, 1e-3,
1e-8, false, true, new INDArray[]{features}, new INDArray[]{labels}, null, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(net).inputs(new INDArray[]{features})
.labels(new INDArray[]{labels}));
assertTrue(gradOK);
}

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/custom/TestCustomActivation.java
View File

@ -53,7 +53,7 @@ public class TestCustomActivation extends BaseDL4JTest {
String json = conf.toJson();
String yaml = conf.toYaml();
System.out.println(json);
// System.out.println(json);
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, confFromJson);

8
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/custom/TestCustomLayers.java
View File

@ -64,7 +64,7 @@ public class TestCustomLayers extends BaseDL4JTest {
String json = conf.toJson();
String yaml = conf.toYaml();
System.out.println(json);
// System.out.println(json);
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, confFromJson);
@ -88,7 +88,7 @@ public class TestCustomLayers extends BaseDL4JTest {
String json = conf.toJson();
String yaml = conf.toYaml();
System.out.println(json);
// System.out.println(json);
ComputationGraphConfiguration confFromJson = ComputationGraphConfiguration.fromJson(json);
assertEquals(conf, confFromJson);
@ -135,7 +135,7 @@ public class TestCustomLayers extends BaseDL4JTest {
String json = conf.toJson();
String yaml = conf.toYaml();
System.out.println(json);
// System.out.println(json);
MultiLayerConfiguration confFromJson = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, confFromJson);
@ -188,7 +188,7 @@ public class TestCustomLayers extends BaseDL4JTest {
String json = conf.toJson();
String yaml = conf.toYaml();
System.out.println(json);
// System.out.println(json);
ComputationGraphConfiguration confFromJson = ComputationGraphConfiguration.fromJson(json);
assertEquals(conf, confFromJson);

36
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayerTest.java
View File

@ -35,6 +35,7 @@ import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.embeddings.EmbeddingInitializer;
import org.junit.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.impl.ActivationIdentity;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
@ -156,7 +157,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
.layer(new RnnOutputLayer.Builder().nIn(embeddingDim).nOut(nOut).activation(Activation.SOFTMAX).build())
.build();
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().activation(Activation.TANH).list()
.layer(0, new DenseLayer.Builder().nIn(nClassesIn).nOut(5).build())
.layer(0, new DenseLayer.Builder().nIn(nClassesIn).nOut(5).activation(Activation.IDENTITY).build())
.layer(1, new OutputLayer.Builder().nIn(5).nOut(4).activation(Activation.SOFTMAX).build())
.inputPreProcessor(0, new RnnToFeedForwardPreProcessor())
.build();
@ -204,7 +205,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
.layer(1, new OutputLayer.Builder().nIn(5).nOut(4).activation(Activation.SOFTMAX).build())
.build();
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().activation(Activation.TANH).list()
.layer(0, new DenseLayer.Builder().nIn(nClassesIn).nOut(5).build())
.layer(0, new DenseLayer.Builder().nIn(nClassesIn).nOut(5).activation(Activation.IDENTITY).build())
.layer(1, new OutputLayer.Builder().nIn(5).nOut(4).activation(Activation.SOFTMAX).build())
.build();
@ -249,8 +250,8 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
.build();
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(nClassesIn).nOut(5).build()).layer(1,
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(4)
.layer(new DenseLayer.Builder().nIn(nClassesIn).nOut(5).activation(Activation.IDENTITY).build())
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(5).nOut(4)
.activation(Activation.SOFTMAX).build())
.build();
@ -309,7 +310,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
.layer(new RnnOutputLayer.Builder().nIn(embeddingDim).nOut(nOut).activation(Activation.SOFTMAX).build())
.build();
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().activation(Activation.TANH).list()
.layer(new DenseLayer.Builder().nIn(nClassesIn).nOut(embeddingDim).build())
.layer(new DenseLayer.Builder().nIn(nClassesIn).nOut(embeddingDim).activation(Activation.IDENTITY).build())
.layer(new RnnOutputLayer.Builder().nIn(embeddingDim).nOut(nOut).activation(Activation.SOFTMAX).build())
.setInputType(InputType.recurrent(nClassesIn))
.build();
@ -344,7 +345,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
net.computeGradientAndScore();
net2.computeGradientAndScore();
System.out.println(net.score() + "\t" + net2.score());
// System.out.println(net.score() + "\t" + net2.score());
assertEquals(net2.score(), net.score(), 1e-6);
Map<String, INDArray> gradient = net.gradient().gradientForVariable();
@ -375,7 +376,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
.weightInit(WeightInit.XAVIER)
.dataType(DataType.DOUBLE)
.list()
.layer(0, new DenseLayer.Builder().nIn(nClassesIn).nOut(5).build())
.layer(0, new DenseLayer.Builder().nIn(nClassesIn).nOut(5).activation(Activation.IDENTITY).build())
.layer(1, new GravesLSTM.Builder().nIn(5).nOut(7).activation(Activation.SOFTSIGN).build())
.layer(2, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(7).nOut(4)
.activation(Activation.SOFTMAX).build())
@ -416,7 +417,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
net.computeGradientAndScore();
net2.computeGradientAndScore();
System.out.println(net.score() + "\t" + net2.score());
// System.out.println(net.score() + "\t" + net2.score());
assertEquals(net2.score(), net.score(), 1e-5);
Map<String, INDArray> gradient = net.gradient().gradientForVariable();
@ -513,7 +514,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
net.computeGradientAndScore();
net2.computeGradientAndScore();
System.out.println(net.score() + "\t" + net2.score());
// System.out.println(net.score() + "\t" + net2.score());
assertEquals(net2.score(), net.score(), 1e-5);
Map<String, INDArray> gradients = net.gradient().gradientForVariable();
@ -707,4 +708,21 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
return true;
}
}
@Test
public void testEmbeddingDefaultActivation(){
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.list()
.layer(new EmbeddingLayer.Builder().nIn(10).nOut(10).build())
.layer(new EmbeddingSequenceLayer.Builder().nIn(10).nOut(10).build())
.build();
EmbeddingLayer l = (EmbeddingLayer) conf.getConf(0).getLayer();
assertEquals(new ActivationIdentity(), l.getActivationFn());
EmbeddingSequenceLayer l2 = (EmbeddingSequenceLayer) conf.getConf(1).getLayer();
assertEquals(new ActivationIdentity(), l2.getActivationFn());
}
}

21
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/normalization/BatchNormalizationTest.java
View File

@ -90,6 +90,11 @@ public class BatchNormalizationTest extends BaseDL4JTest {
public void doBefore() {
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testDnnForwardPass() {
int nOut = 10;
@ -102,7 +107,7 @@ public class BatchNormalizationTest extends BaseDL4JTest {
INDArray mean = output.mean(0);
INDArray stdev = output.std(false, 0);
System.out.println(Arrays.toString(mean.data().asFloat()));
// System.out.println(Arrays.toString(mean.data().asFloat()));
assertArrayEquals(new float[nOut], mean.data().asFloat(), 1e-6f);
assertEquals(Nd4j.ones(nOut), stdev);
@ -161,8 +166,8 @@ public class BatchNormalizationTest extends BaseDL4JTest {
INDArray out = l.activate(input, true, LayerWorkspaceMgr.noWorkspaces());
System.out.println(Arrays.toString(outExpected.data().asDouble()));
System.out.println(Arrays.toString(out.data().asDouble()));
// System.out.println(Arrays.toString(outExpected.data().asDouble()));
// System.out.println(Arrays.toString(out.data().asDouble()));
assertEquals(outExpected, out);
@ -190,9 +195,9 @@ public class BatchNormalizationTest extends BaseDL4JTest {
assertEquals(dldgammaExp, dldgamma);
assertEquals(dldbetaExp, dldbeta);
System.out.println("EPSILONS");
System.out.println(Arrays.toString(dldinExp.data().asDouble()));
System.out.println(Arrays.toString(p.getSecond().dup().data().asDouble()));
// System.out.println("EPSILONS");
// System.out.println(Arrays.toString(dldinExp.data().asDouble()));
// System.out.println(Arrays.toString(p.getSecond().dup().data().asDouble()));
assertEquals(dldinExp, p.getSecond());
}
@ -303,8 +308,8 @@ public class BatchNormalizationTest extends BaseDL4JTest {
INDArray out = l.activate(input, true, LayerWorkspaceMgr.noWorkspaces());
System.out.println(Arrays.toString(outExpected.data().asDouble()));
System.out.println(Arrays.toString(out.data().asDouble()));
// System.out.println(Arrays.toString(outExpected.data().asDouble()));
// System.out.println(Arrays.toString(out.data().asDouble()));
assertEquals(outExpected, out);

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/objdetect/TestYolo2OutputLayer.java
View File

@ -140,7 +140,7 @@ public class TestYolo2OutputLayer extends BaseDL4JTest {
y2impl.setLabels(labels);
double score = y2impl.computeScore(0.0, true, LayerWorkspaceMgr.noWorkspaces());
System.out.println("SCORE: " + score);
// System.out.println("SCORE: " + score);
assertTrue(score > 0.0);

20
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/GravesLSTMTest.java
View File

@ -220,20 +220,20 @@ public class GravesLSTMTest extends BaseDL4JTest {
INDArray out1 = net.output(in1);
INDArray out2 = net.output(in2);
System.out.println(Arrays.toString(net.output(in1).data().asFloat()));
System.out.println(Arrays.toString(net.output(in2).data().asFloat()));
// System.out.println(Arrays.toString(net.output(in1).data().asFloat()));
// System.out.println(Arrays.toString(net.output(in2).data().asFloat()));
List<INDArray> activations1 = net.feedForward(in1);
List<INDArray> activations2 = net.feedForward(in2);
for (int i = 0; i < 3; i++) {
System.out.println("-----\n" + i);
System.out.println(Arrays.toString(activations1.get(i).dup().data().asDouble()));
System.out.println(Arrays.toString(activations2.get(i).dup().data().asDouble()));
System.out.println(activations1.get(i));
System.out.println(activations2.get(i));
}
// for (int i = 0; i < 3; i++) {
// System.out.println("-----\n" + i);
// System.out.println(Arrays.toString(activations1.get(i).dup().data().asDouble()));
// System.out.println(Arrays.toString(activations2.get(i).dup().data().asDouble()));
//
// System.out.println(activations1.get(i));
// System.out.println(activations2.get(i));
// }

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/TestSameDiffConv.java
View File

@ -306,8 +306,8 @@ public class TestSameDiffConv extends BaseDL4JTest {
INDArray l = TestUtils.randomOneHot(minibatch, nOut);
log.info("Starting: " + msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, f, l, null, null, true, 50); //Most of weights are in output layer
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(f)
.labels(l).subset(true).maxPerParam(50));
assertTrue(msg, gradOK);

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/TestSameDiffDenseVertex.java
View File

@ -135,7 +135,7 @@ public class TestSameDiffDenseVertex extends BaseDL4JTest {
assertEquals(gStd.gradient(), gSD.gradient());
System.out.println("========================================================================");
// System.out.println("========================================================================");
//Sanity check: different minibatch size
in = Nd4j.rand(2 * minibatch, nIn);

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/variational/TestReconstructionDistributions.java
View File

@ -317,7 +317,7 @@ public class TestReconstructionDistributions extends BaseDL4JTest {
INDArray gradient = rd.gradient(x, distributionParams);
String testName = "minibatch = " + minibatch + ", size = " + inputSize + ", Distribution = " + rd;
System.out.println("\n\n***** Starting test: " + testName + "*****");
System.out.println("***** Starting test: " + testName + "*****");
int totalFailureCount = 0;
for (int i = 0; i < distributionParams.size(1); i++) {
@ -349,7 +349,7 @@ public class TestReconstructionDistributions extends BaseDL4JTest {
totalFailureCount++;
}
} else {
log.info("Input (" + j + "," + i + ") passed: grad= " + backpropGrad + ", numericalGrad= "
log.trace("Input (" + j + "," + i + ") passed: grad= " + backpropGrad + ", numericalGrad= "
+ numericalGrad + ", relError= " + relError);
}
}

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/misc/WorkspaceTests.java
View File

@ -472,7 +472,7 @@ public class WorkspaceTests extends BaseDL4JTest {
final ComputationGraph computationGraph = new ComputationGraph(config);
computationGraph.init();
computationGraph.setListeners(new ScoreIterationListener(1));
computationGraph.setListeners(new ScoreIterationListener(3));
WSTestDataSetIterator iterator = new WSTestDataSetIterator();
computationGraph.fit(iterator);

8
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/multilayer/BackPropMLPTest.java
View File

@ -66,7 +66,7 @@ public class BackPropMLPTest extends BaseDL4JTest {
public void testMLP() {
//Simple mini-batch test with multiple hidden layers
MultiLayerConfiguration conf = getIrisMLPSimpleConfig(new int[] {5, 4, 3}, Activation.SIGMOID);
System.out.println(conf);
// System.out.println(conf);
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
DataSetIterator iter = new IrisDataSetIterator(10, 100);
@ -80,7 +80,7 @@ public class BackPropMLPTest extends BaseDL4JTest {
public void testMLP2() {
//Simple mini-batch test with multiple hidden layers
MultiLayerConfiguration conf = getIrisMLPSimpleConfig(new int[] {5, 15, 3}, Activation.TANH);
System.out.println(conf);
// System.out.println(conf);
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
@ -104,7 +104,7 @@ public class BackPropMLPTest extends BaseDL4JTest {
Layer[] layers = network.getLayers();
final boolean printCalculations = true;
final boolean printCalculations = false;
while (iris.hasNext()) {
DataSet data = iris.next();
@ -212,7 +212,7 @@ public class BackPropMLPTest extends BaseDL4JTest {
assertEquals(l1BiasFloatAfter,expectedL1BiasAfter,eps);
assertArrayEquals(l2BiasFloatAfter,expectedL2BiasAfter,eps);
*/
System.out.println("\n\n--------------");
// System.out.println("\n\n--------------");
}
}

8
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/multilayer/MultiLayerTest.java
View File

@ -922,9 +922,9 @@ public class MultiLayerTest extends BaseDL4JTest {
MultiLayerNetwork modelExpectedArch = new MultiLayerNetwork(confForArchitecture);
modelExpectedArch.init();
MultiLayerNetwork modelMow = new TransferLearning.Builder(modelExpectedArch).setFeatureExtractor(2).build();
System.out.println(modelExpectedArch.summary());
System.out.println(modelMow.summary());
System.out.println(modelMow.summary(InputType.recurrent(V_HEIGHT*V_WIDTH*3)));
// System.out.println(modelExpectedArch.summary());
// System.out.println(modelMow.summary());
// System.out.println(modelMow.summary(InputType.recurrent(V_HEIGHT*V_WIDTH*3)));
}
@Test(expected = DL4JException.class)
@ -1149,7 +1149,7 @@ public class MultiLayerTest extends BaseDL4JTest {
int nOut = 3;
for(WorkspaceMode ws : WorkspaceMode.values()) {
System.out.println("***** WORKSPACE: " + ws);
// System.out.println("***** WORKSPACE: " + ws);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(new Adam(0.01))

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/multilayer/MultiLayerTestRNN.java
View File

@ -570,8 +570,8 @@ public class MultiLayerTestRNN extends BaseDL4JTest {
for (int j = 0; j < expOut.size(); j++) {
INDArray exp = expOut.get(j);
INDArray act = outSlice.get(j);
System.out.println(j);
System.out.println(exp.sub(act));
// System.out.println(j);
// System.out.println(exp.sub(act));
assertEquals(exp, act);
}

10
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/multilayer/TestVariableLengthTS.java
View File

@ -219,10 +219,10 @@ public class TestVariableLengthTS extends BaseDL4JTest {
INDArray g1s = g1map.get(s);
INDArray g2s = g2map.get(s);
System.out.println("-------");
System.out.println("Variable: " + s);
System.out.println(Arrays.toString(g1s.dup().data().asFloat()));
System.out.println(Arrays.toString(g2s.dup().data().asFloat()));
// System.out.println("-------");
// System.out.println("Variable: " + s);
// System.out.println(Arrays.toString(g1s.dup().data().asFloat()));
// System.out.println(Arrays.toString(g2s.dup().data().asFloat()));
assertNotEquals(s, g1s, g2s);
}
@ -507,7 +507,7 @@ public class TestVariableLengthTS extends BaseDL4JTest {
for (boolean bidirectional : isBidirectional) {
for (PoolingType pt : poolingTypes) {
System.out.println("Starting test: bidirectional = " + bidirectional + ", poolingType = " + pt);
// System.out.println("Starting test: bidirectional = " + bidirectional + ", poolingType = " + pt);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER)
.activation(Activation.TANH).list().layer(0, bidirectional

1
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TestFrozenLayers.java
View File

@ -51,7 +51,6 @@ public class TestFrozenLayers extends BaseDL4JTest {
for(double l1 : new double[]{0.0, 0.3}){
for( double l2 : new double[]{0.0, 0.4}){
System.out.println("--------------------");
String msg = "l1=" + l1 + ", l2=" + l2;
FineTuneConfiguration ftc = new FineTuneConfiguration.Builder()

7
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningComplex.java
View File

@ -273,8 +273,9 @@ public class TransferLearningComplex extends BaseDL4JTest {
MultiDataSet rand = new MultiDataSet(new INDArray[] {Nd4j.rand(2, 2), Nd4j.rand(2, 2)},
new INDArray[] {Nd4j.rand(2, 2), Nd4j.rand(2, 3)});
modelNow.fit(rand);
log.info(modelNow.summary());
log.info(modelNow.summary(InputType.feedForward(2),InputType.feedForward(2)));
// log.info(modelNow.summary());
// log.info(modelNow.summary(InputType.feedForward(2),InputType.feedForward(2)));
modelNow.summary();
modelNow.summary(InputType.feedForward(2),InputType.feedForward(2));
}
}

7
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningHelperTest.java
View File

@ -195,9 +195,10 @@ public class TransferLearningHelperTest extends BaseDL4JTest {
assertEquals(modelIdentical.getLayer("denseLeft0").params(), modelToTune.getLayer("denseLeft0").params());
assertEquals(modelIdentical.getLayer("outLeft").params(), modelToTune.getLayer("outLeft").params());
log.info(modelIdentical.summary());
log.info(helper.unfrozenGraph().summary());
// log.info(modelIdentical.summary());
// log.info(helper.unfrozenGraph().summary());
modelIdentical.summary();
helper.unfrozenGraph().summary();
}
@Test

4
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/util/TestDataSetConsumer.java
View File

@ -84,8 +84,8 @@ public class TestDataSetConsumer {
count.incrementAndGet();
if (count.get() % 100 == 0)
logger.info("Passed {} datasets...", count.get());
// if (count.get() % 100 == 0)
// logger.info("Passed {} datasets...", count.get());
return count.get();
}

6
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/optimize/solver/BackTrackLineSearchTest.java
View File

@ -186,7 +186,7 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.SIGMOID, optimizer));
network.init();
TrainingListener listener = new ScoreIterationListener(1);
TrainingListener listener = new ScoreIterationListener(10);
network.setListeners(Collections.singletonList(listener));
double oldScore = network.score(data);
for( int i=0; i<100; i++ ) {
@ -204,7 +204,7 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
data.normalizeZeroMeanZeroUnitVariance();
MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.RELU, optimizer));
network.init();
TrainingListener listener = new ScoreIterationListener(1);
TrainingListener listener = new ScoreIterationListener(10);
network.setListeners(Collections.singletonList(listener));
double firstScore = network.score(data);
@ -223,7 +223,7 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
data.normalizeZeroMeanZeroUnitVariance();
MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.RELU, optimizer));
network.init();
TrainingListener listener = new ScoreIterationListener(1);
TrainingListener listener = new ScoreIterationListener(10);
network.setListeners(Collections.singletonList(listener));
double oldScore = network.score(data);

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/optimize/solver/TestOptimizers.java
View File

@ -66,7 +66,7 @@ import static org.junit.Assert.assertTrue;
public class TestOptimizers extends BaseDL4JTest {
//For debugging.
private static final boolean PRINT_OPT_RESULTS = true;
private static final boolean PRINT_OPT_RESULTS = false;
@Test
public void testOptimizersBasicMLPBackprop() {

79
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/optimizer/listener/TestParamAndGradientIterationListener.java
View File

@ -1,79 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.optimizer.listener;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.optimize.listeners.ParamAndGradientIterationListener;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.learning.config.Sgd;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.io.File;
import static org.junit.Assert.assertEquals;
public class TestParamAndGradientIterationListener extends BaseDL4JTest {
@Rule
public TemporaryFolder testDir = new TemporaryFolder();
@Test
public void test() throws Exception {
IrisDataSetIterator iter = new IrisDataSetIterator(30, 150);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new Sgd(1e-5))
.list().layer(0, new DenseLayer.Builder().nIn(4).nOut(20).build())
.layer(1, new DenseLayer.Builder().nIn(20).nOut(30).build())
.layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(30).nOut(3).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
File f = testDir.newFile("paramAndGradTest.txt");
TrainingListener listener = ParamAndGradientIterationListener.builder().outputToFile(true)
.file(f)
.outputToConsole(true).outputToLogger(false).iterations(2).printHeader(true).printMean(false)
.printMinMax(false).printMeanAbsValue(true).delimiter("\t").build();
net.setListeners(listener);
for (int i = 0; i < 2; i++) {
net.fit(iter);
}
}
}

16
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/plot/BarnesHutTsneTest.java
View File

@ -91,7 +91,7 @@ public class BarnesHutTsneTest extends BaseDL4JTest {
.useAdaGrad(false).build();
b.fit(data);
log.info("Result: {}", b.getData());
// log.info("Result: {}", b.getData());
val exp = Nd4j.createFromArray(new double[]{-3.5318212819287327, 35.40331834897696, 3.890809489531651, -1.291195609955519, -42.854099388207466, 7.8761368019456635, 28.798057251442877, 7.1456564000935225, 2.9518396278984786, -42.860181054199636, -34.989343304202, -108.99770355680282, 31.78123839126566, -29.322118879730205, 163.87558311206212, 2.9538984612478396, 31.419519824305546, 13.105400907817279, 25.46987139120746, -43.27317406736858, 32.455151773056144, 25.28067703547214, 0.005442008567682552, 21.005029233370358, -61.71390311950051, 5.218417653362599, 47.15762099517554, 8.834739256343404, 17.845790108867153, -54.31654219224107, -18.71285871476804, -16.446982180909007, -71.22568781913213, -12.339975548387091, 70.49096598213703, 25.022454385237456, -14.572652938207126, -5.320080866729078, 1.5874449933639676, -40.60960510287835, -31.98564381157643, -95.40875746933808, 19.196346639002364, -38.80930682421929, 135.00454225923906, 5.277879540549592, 30.79963767087089, -0.007276462027131683, 31.278796123365815, -38.47381680049993, 10.415728497075905, 36.567265019013085, -7.406587944733211, -18.376174615781114, -45.26976962854271}).reshape(-1, 5);
@ -178,7 +178,7 @@ public class BarnesHutTsneTest extends BaseDL4JTest {
INDArray data = iter.next().getFeatures();
INDArray perplexityOutput = b.computeGaussianPerplexity(data, 30.0);
System.out.println(perplexityOutput);
// System.out.println(perplexityOutput);
}
@Test
@ -217,17 +217,17 @@ public class BarnesHutTsneTest extends BaseDL4JTest {
StopWatch watch = new StopWatch();
watch.start();
b.fit(data);
System.out.println(b.getData());
// System.out.println(b.getData());
watch.stop();
File outDir = testDir.newFolder();
ClassPathResource labels = new ClassPathResource("mnist2500_labels.txt");
List<String> labelsList = IOUtils.readLines(labels.getInputStream());
b.saveAsFile(/*labelsList,*/ new File(outDir, "raw.txt").getAbsolutePath());
System.out.println(b.getData());
// System.out.println(b.getData());
System.out.println("Fit done in " + watch);
assertEquals(2500, b.getData().size(0));
System.out.println(b.getData());
// System.out.println(b.getData());
INDArray a1 = b.getData().getRow(0);
INDArray a2 = b.getData().getRow(1);
@ -338,7 +338,7 @@ public class BarnesHutTsneTest extends BaseDL4JTest {
double[] dC = {-0.0618386320333619, -0.06266654959379839, 0.029998268806149204, 0.10780566335888186, -0.19449543068355346, -0.14763764361792697, 0.17493572758118422, 0.1926109839221966, -0.15176648259935419, 0.10974665709698186, 0.13102419155322598, 0.004941641352409449, 0.19159764518354974, -0.26332838053474944, -0.023631441261541583, 0.09838669432305949, 0.09709129638394683, -0.01605053000727605, 0.06566171635025217, -0.17325078066035252, -0.1090854255505605, 0.023350644966904276, 0.075192354899586, -0.08278373866517603, 0.18431338134579323, 0.2766031655578053, -0.17557907233268688, 0.10616148241800637, -0.09999024423215641, -0.017181932145255287, 0.06711331400576945, -0.01388231800826619, -0.10248189290485302, 0.20786521034824304, 0.11254913977572988, -0.289564646781519, 0.13491805919337516, -0.07504249344962562, 0.004154656287570634, -0.10516715438388784, -0.27984655075804576, 0.09811828071286613, 0.03684521473995052, -0.054645216532387256, -0.18147132772800725, 0.027588750493223044, 0.214734364419479, -0.026729138234415008, -0.28410504978879136, 0.007015481601883835, 0.04427981739424874, -0.059253265830134655, -0.05325479031206952, -0.11319889109674944, 0.1530133971867549};
INDArray actual = gradient.getGradientFor("yIncs");
System.out.println(actual);
// System.out.println(actual);
assertArrayEquals(dC, actual.reshape(1,55).toDoubleVector(), 1e-05);
}
@ -482,8 +482,8 @@ public class BarnesHutTsneTest extends BaseDL4JTest {
List<DataPoint> results = new ArrayList<>();
List<Double> distances = new ArrayList<>();
tree.search(target, 11, results, distances);
System.out.println("Results:" + results);
System.out.println("Distances:" + distances);
// System.out.println("Results:" + results);
// System.out.println("Distances:" + distances);
}
}

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/samediff/CompareTrainingImplementations.java
View File

@ -250,7 +250,7 @@ public class CompareTrainingImplementations extends BaseDL4JTest {
sd.evaluate(iter, "softmax", rEvalSd);
assertEquals(rEvalDl4j, rEvalSd);
System.out.println("---------------------------------");
// System.out.println("---------------------------------");
}
}
}

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/util/CrashReportingUtilTest.java
View File

@ -47,6 +47,11 @@ import static org.junit.Assert.*;
public class CrashReportingUtilTest extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 120000;
}
@Rule
public TemporaryFolder testDir = new TemporaryFolder();

32
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/util/ModelValidatorTests.java
View File

@ -51,7 +51,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("MultiLayerNetwork", vr0.getFormatType());
assertEquals(MultiLayerNetwork.class, vr0.getFormatClass());
assertNull(vr0.getException());
System.out.println(vr0.toString());
// System.out.println(vr0.toString());
//Test empty file
File f1 = new File(f, "empty.bin");
@ -63,7 +63,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("MultiLayerNetwork", vr1.getFormatType());
assertEquals(MultiLayerNetwork.class, vr1.getFormatClass());
assertNull(vr1.getException());
System.out.println(vr1.toString());
// System.out.println(vr1.toString());
//Test invalid zip file
File f2 = new File(f, "notReallyZip.zip");
@ -75,7 +75,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("MultiLayerNetwork", vr2.getFormatType());
assertEquals(MultiLayerNetwork.class, vr2.getFormatClass());
assertNotNull(vr2.getException());
System.out.println(vr2.toString());
// System.out.println(vr2.toString());
//Test valid zip, but missing configuration
File f3 = new File(f, "modelNoConfig.zip");
@ -92,7 +92,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("MultiLayerNetwork", vr3.getFormatType());
assertEquals(MultiLayerNetwork.class, vr3.getFormatClass());
assertNull(vr3.getException());
System.out.println(vr3.toString());
// System.out.println(vr3.toString());
//Test valid sip, but missing params
@ -110,7 +110,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("MultiLayerNetwork", vr4.getFormatType());
assertEquals(MultiLayerNetwork.class, vr4.getFormatClass());
assertNull(vr4.getException());
System.out.println(vr4.toString());
// System.out.println(vr4.toString());
//Test valid model
@ -122,7 +122,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("MultiLayerNetwork", vr5.getFormatType());
assertEquals(MultiLayerNetwork.class, vr5.getFormatClass());
assertNull(vr5.getException());
System.out.println(vr5.toString());
// System.out.println(vr5.toString());
//Test valid model with corrupted JSON
@ -141,7 +141,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
bytes = IOUtils.toByteArray(zis);
}
zo.write(bytes);
System.out.println("WROTE: " + ze.getName());
// System.out.println("WROTE: " + ze.getName());
}
}
}
@ -153,7 +153,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("MultiLayerNetwork", vr6.getFormatType());
assertEquals(MultiLayerNetwork.class, vr6.getFormatClass());
assertNotNull(vr6.getException());
System.out.println(vr6.toString());
// System.out.println(vr6.toString());
}
@ -169,7 +169,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("ComputationGraph", vr0.getFormatType());
assertEquals(ComputationGraph.class, vr0.getFormatClass());
assertNull(vr0.getException());
System.out.println(vr0.toString());
// System.out.println(vr0.toString());
//Test empty file
File f1 = new File(f, "empty.bin");
@ -181,7 +181,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("ComputationGraph", vr1.getFormatType());
assertEquals(ComputationGraph.class, vr1.getFormatClass());
assertNull(vr1.getException());
System.out.println(vr1.toString());
// System.out.println(vr1.toString());
//Test invalid zip file
File f2 = new File(f, "notReallyZip.zip");
@ -193,7 +193,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("ComputationGraph", vr2.getFormatType());
assertEquals(ComputationGraph.class, vr2.getFormatClass());
assertNotNull(vr2.getException());
System.out.println(vr2.toString());
// System.out.println(vr2.toString());
//Test valid zip, but missing configuration
File f3 = new File(f, "modelNoConfig.zip");
@ -210,7 +210,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("ComputationGraph", vr3.getFormatType());
assertEquals(ComputationGraph.class, vr3.getFormatClass());
assertNull(vr3.getException());
System.out.println(vr3.toString());
// System.out.println(vr3.toString());
//Test valid sip, but missing params
@ -228,7 +228,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("ComputationGraph", vr4.getFormatType());
assertEquals(ComputationGraph.class, vr4.getFormatClass());
assertNull(vr4.getException());
System.out.println(vr4.toString());
// System.out.println(vr4.toString());
//Test valid model
@ -240,7 +240,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("ComputationGraph", vr5.getFormatType());
assertEquals(ComputationGraph.class, vr5.getFormatClass());
assertNull(vr5.getException());
System.out.println(vr5.toString());
// System.out.println(vr5.toString());
//Test valid model with corrupted JSON
@ -259,7 +259,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
bytes = IOUtils.toByteArray(zis);
}
zo.write(bytes);
System.out.println("WROTE: " + ze.getName());
// System.out.println("WROTE: " + ze.getName());
}
}
}
@ -271,7 +271,7 @@ public class ModelValidatorTests extends BaseDL4JTest {
assertEquals("ComputationGraph", vr6.getFormatType());
assertEquals(ComputationGraph.class, vr6.getFormatClass());
assertNotNull(vr6.getException());
System.out.println(vr6.toString());
// System.out.println(vr6.toString());
}

6
deeplearning4j/deeplearning4j-cuda/pom.xml
View File

@ -83,6 +83,12 @@
<artifactId>junit</artifactId>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-common-tests</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
<build>

141
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/BaseDL4JTest.java
View File

@ -1,141 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.javacpp.Pointer;
import org.junit.After;
import org.junit.Before;
import org.junit.Rule;
import org.junit.rules.TestName;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.profiler.ProfilerConfig;
import java.lang.management.ManagementFactory;
import java.util.List;
import java.util.Map;
import java.util.Properties;
@Slf4j
public class BaseDL4JTest {
@Rule
public TestName name = new TestName();
protected long startTime;
protected int threadCountBefore;
/**
* Override this to set the profiling mode for the tests defined in the child class
*/
public OpExecutioner.ProfilingMode getProfilingMode(){
return OpExecutioner.ProfilingMode.SCOPE_PANIC;
}
/**
* Override this to set the datatype of the tests defined in the child class
*/
public DataType getDataType(){
return DataType.DOUBLE;
}
public DataType getDefaultFPDataType(){
return getDataType();
}
@Before
public void beforeTest(){
log.info("{}.{}", getClass().getSimpleName(), name.getMethodName());
Nd4j.getExecutioner().setProfilingMode(getProfilingMode());
Nd4j.getExecutioner().setProfilingConfig(ProfilerConfig.builder().build());
Nd4j.setDefaultDataTypes(getDataType(), getDefaultFPDataType());
startTime = System.currentTimeMillis();
threadCountBefore = ManagementFactory.getThreadMXBean().getThreadCount();
}
@After
public void afterTest(){
//Attempt to keep workspaces isolated between tests
Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();
MemoryWorkspace currWS = Nd4j.getMemoryManager().getCurrentWorkspace();
Nd4j.getMemoryManager().setCurrentWorkspace(null);
if(currWS != null){
//Not really safe to continue testing under this situation... other tests will likely fail with obscure
// errors that are hard to track back to this
log.error("Open workspace leaked from test! Exiting - {}, isOpen = {} - {}", currWS.getId(), currWS.isScopeActive(), currWS);
System.exit(1);
}
StringBuilder sb = new StringBuilder();
long maxPhys = Pointer.maxPhysicalBytes();
long maxBytes = Pointer.maxBytes();
long currPhys = Pointer.physicalBytes();
long currBytes = Pointer.totalBytes();
long jvmTotal = Runtime.getRuntime().totalMemory();
long jvmMax = Runtime.getRuntime().maxMemory();
int threadsAfter = ManagementFactory.getThreadMXBean().getThreadCount();
long duration = System.currentTimeMillis() - startTime;
sb.append(getClass().getSimpleName()).append(".").append(name.getMethodName())
.append(": ").append(duration).append(" ms")
.append(", threadCount: (").append(threadCountBefore).append("->").append(threadsAfter).append(")")
.append(", jvmTotal=").append(jvmTotal)
.append(", jvmMax=").append(jvmMax)
.append(", totalBytes=").append(currBytes).append(", maxBytes=").append(maxBytes)
.append(", currPhys=").append(currPhys).append(", maxPhys=").append(maxPhys);
List<MemoryWorkspace> ws = Nd4j.getWorkspaceManager().getAllWorkspacesForCurrentThread();
if(ws != null && ws.size() > 0){
long currSize = 0;
for(MemoryWorkspace w : ws){
currSize += w.getCurrentSize();
}
if(currSize > 0){
sb.append(", threadWSSize=").append(currSize)
.append(" (").append(ws.size()).append(" WSs)");
}
}
Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
Object o = p.get("cuda.devicesInformation");
if(o instanceof List){
List<Map<String,Object>> l = (List<Map<String, Object>>) o;
if(l.size() > 0) {
sb.append(" [").append(l.size())
.append(" GPUs: ");
for (int i = 0; i < l.size(); i++) {
Map<String,Object> m = l.get(i);
if(i > 0)
sb.append(",");
sb.append("(").append(m.get("cuda.freeMemory")).append(" free, ")
.append(m.get("cuda.totalMemory")).append(" total)");
}
sb.append("]");
}
}
log.info(sb.toString());
}
}

31
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java
View File

@ -735,9 +735,10 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
+ convFirst;
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input,
labels, null, null, true, 128);
boolean gradOK = GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net)
.input(input).labels(labels)
.subset(true).maxPerParam(128));
assertTrue(msg, gradOK);
@ -879,8 +880,10 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
+ k + ", s=" + s + ", d=" + d + ", cm=" + cm;
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 100);
boolean gradOK = GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net)
.input(input).labels(labels)
.subset(true).maxPerParam(100));
assertTrue(msg, gradOK);
@ -948,8 +951,10 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
+ k + ", nIn=" + nIn + ", depthMul=" + depthMultiplier + ", s=" + s + ", cm=" + cm;
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 256);
boolean gradOK = GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net)
.input(input).labels(labels)
.subset(true).maxPerParam(256));
assertTrue(msg, gradOK);
@ -1021,8 +1026,10 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
+ k + ", s=" + s + ", d=" + d + ", cm=" + cm;
System.out.println(msg);
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 50); //Most params are in output layer
boolean gradOK = GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net)
.input(input).labels(labels)
.subset(true).maxPerParam(50));
assertTrue(msg, gradOK);
@ -1176,8 +1183,10 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels, null, null, true, 160);
boolean gradOK = GradientCheckUtil.checkGradients(
new GradientCheckUtil.MLNConfig().net(net)
.input(input).labels(labels)
.subset(true).maxPerParam(160));
assertTrue(msg, gradOK);

7
deeplearning4j/deeplearning4j-graph/pom.xml
View File

@ -51,6 +51,13 @@
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-common-tests</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
<profiles>

140
deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/BaseDL4JTest.java
View File

@ -1,140 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.graph;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.javacpp.Pointer;
import org.junit.After;
import org.junit.Before;
import org.junit.Rule;
import org.junit.rules.TestName;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.profiler.ProfilerConfig;
import java.lang.management.ManagementFactory;
import java.util.List;
import java.util.Map;
import java.util.Properties;
@Slf4j
public class BaseDL4JTest {
@Rule
public TestName name = new TestName();
protected long startTime;
protected int threadCountBefore;
/**
* Override this to set the profiling mode for the tests defined in the child class
*/
public OpExecutioner.ProfilingMode getProfilingMode(){
return OpExecutioner.ProfilingMode.SCOPE_PANIC;
}
/**
* Override this to set the datatype of the tests defined in the child class
*/
public DataType getDataType(){
return DataType.DOUBLE;
}
public DataType getDefaultFPDataType(){
return getDataType();
}
@Before
public void beforeTest(){
log.info("{}.{}", getClass().getSimpleName(), name.getMethodName());
Nd4j.getExecutioner().setProfilingMode(getProfilingMode());
Nd4j.getExecutioner().setProfilingConfig(ProfilerConfig.builder().build());
Nd4j.setDefaultDataTypes(getDataType(), getDefaultFPDataType());
startTime = System.currentTimeMillis();
threadCountBefore = ManagementFactory.getThreadMXBean().getThreadCount();
}
@After
public void afterTest(){
//Attempt to keep workspaces isolated between tests
Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();
MemoryWorkspace currWS = Nd4j.getMemoryManager().getCurrentWorkspace();
Nd4j.getMemoryManager().setCurrentWorkspace(null);
if(currWS != null){
//Not really safe to continue testing under this situation... other tests will likely fail with obscure
// errors that are hard to track back to this
log.error("Open workspace leaked from test! Exiting - {}, isOpen = {} - {}", currWS.getId(), currWS.isScopeActive(), currWS);
System.exit(1);
}
StringBuilder sb = new StringBuilder();
long maxPhys = Pointer.maxPhysicalBytes();
long maxBytes = Pointer.maxBytes();
long currPhys = Pointer.physicalBytes();
long currBytes = Pointer.totalBytes();
long jvmTotal = Runtime.getRuntime().totalMemory();
long jvmMax = Runtime.getRuntime().maxMemory();
int threadsAfter = ManagementFactory.getThreadMXBean().getThreadCount();
long duration = System.currentTimeMillis() - startTime;
sb.append(getClass().getSimpleName()).append(".").append(name.getMethodName())
.append(": ").append(duration).append(" ms")
.append(", threadCount: (").append(threadCountBefore).append("->").append(threadsAfter).append(")")
.append(", jvmTotal=").append(jvmTotal)
.append(", jvmMax=").append(jvmMax)
.append(", totalBytes=").append(currBytes).append(", maxBytes=").append(maxBytes)
.append(", currPhys=").append(currPhys).append(", maxPhys=").append(maxPhys);
List<MemoryWorkspace> ws = Nd4j.getWorkspaceManager().getAllWorkspacesForCurrentThread();
if(ws != null && ws.size() > 0){
long currSize = 0;
for(MemoryWorkspace w : ws){
currSize += w.getCurrentSize();
}
if(currSize > 0){
sb.append(", threadWSSize=").append(currSize)
.append(" (").append(ws.size()).append(" WSs)");
}
}
Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
Object o = p.get("cuda.devicesInformation");
if(o instanceof List){
List<Map<String,Object>> l = (List<Map<String, Object>>) o;
if(l.size() > 0) {
sb.append(" [").append(l.size())
.append(" GPUs: ");
for (int i = 0; i < l.size(); i++) {
Map<String,Object> m = l.get(i);
if(i > 0)
sb.append(",");
sb.append("(").append(m.get("cuda.freeMemory")).append(" free, ")
.append(m.get("cuda.totalMemory")).append(" total)");
}
sb.append("]");
}
}
log.info(sb.toString());
}
}

2
deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/data/TestGraphLoading.java
View File

@ -17,7 +17,7 @@
package org.deeplearning4j.graph.data;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.graph.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.graph.api.Edge;
import org.deeplearning4j.graph.api.IGraph;
import org.deeplearning4j.graph.data.impl.DelimitedEdgeLineProcessor;

2
deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/data/TestGraphLoadingWeighted.java
View File

@ -17,7 +17,7 @@
package org.deeplearning4j.graph.data;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.graph.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.graph.api.Edge;
import org.deeplearning4j.graph.api.IGraph;
import org.deeplearning4j.graph.data.impl.WeightedEdgeLineProcessor;

2
deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/graph/TestGraph.java
View File

@ -17,7 +17,7 @@
package org.deeplearning4j.graph.graph;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.graph.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.graph.api.*;
import org.deeplearning4j.graph.data.GraphLoader;
import org.deeplearning4j.graph.iterator.RandomWalkIterator;

2
deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/models/deepwalk/DeepWalkGradientCheck.java
View File

@ -16,7 +16,7 @@
package org.deeplearning4j.graph.models.deepwalk;
import org.deeplearning4j.graph.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.graph.data.GraphLoader;
import org.deeplearning4j.graph.graph.Graph;
import org.deeplearning4j.graph.iterator.GraphWalkIterator;

2
deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/models/deepwalk/TestDeepWalk.java
View File

@ -17,7 +17,7 @@
package org.deeplearning4j.graph.models.deepwalk;
import org.apache.commons.io.FilenameUtils;
import org.deeplearning4j.graph.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.graph.api.Edge;
import org.deeplearning4j.graph.api.IGraph;
import org.deeplearning4j.graph.data.GraphLoader;

2
deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/models/deepwalk/TestGraphHuffman.java
View File

@ -16,7 +16,7 @@
package org.deeplearning4j.graph.models.deepwalk;
import org.deeplearning4j.graph.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.junit.Test;
import java.util.Arrays;

7
deeplearning4j/deeplearning4j-manifold/deeplearning4j-tsne/pom.xml
View File

@ -55,6 +55,13 @@
<artifactId>nd4j-api</artifactId>
<version>${nd4j.version}</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-common-tests</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
<profiles>

3
deeplearning4j/deeplearning4j-manifold/deeplearning4j-tsne/src/test/java/org/deeplearning4j/plot/Test6058.java
View File

@ -17,6 +17,7 @@
package org.deeplearning4j.plot;
import lombok.val;
import org.deeplearning4j.BaseDL4JTest;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
@ -25,7 +26,7 @@ import java.util.ArrayList;
import static org.junit.Assert.assertTrue;
public class Test6058 {
public class Test6058 extends BaseDL4JTest {
@Test
public void test() throws Exception {

6
deeplearning4j/deeplearning4j-modelimport/pom.xml
View File

@ -86,6 +86,12 @@
<artifactId>junit</artifactId> <!-- Version set by deeplearning4j-parent dependency management -->
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-common-tests</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>

3
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/config/KerasLayerConfiguration.java
View File

@ -108,6 +108,9 @@ public class KerasLayerConfiguration {
private final String LAYER_CLASS_NAME_LEAKY_RELU = "LeakyReLU";
private final String LAYER_CLASS_NAME_PRELU = "PReLU";
private final String LAYER_CLASS_NAME_THRESHOLDED_RELU = "ThresholdedReLU";
private final String LAYER_CLASS_NAME_RELU = "ReLU";
private final String LAYER_CLASS_NAME_ELU = "ELU";
private final String LAYER_CLASS_NAME_SOFTMAX = "Softmax";
private final String LAYER_CLASS_NAME_UPSAMPLING_1D = "UpSampling1D";
private final String LAYER_CLASS_NAME_UPSAMPLING_2D = "UpSampling2D";
private final String LAYER_CLASS_NAME_UPSAMPLING_3D = "UpSampling3D";

95
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasELU.java Normal file
View File

@ -0,0 +1,95 @@
/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.nn.modelimport.keras.layers.advanced.activations;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ActivationLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.ActivationELU;
import org.nd4j.linalg.activations.impl.ActivationLReLU;
import java.util.Map;
/**
* Imports ELU layer from Keras
*
* @author Alex Black
*/
public class KerasELU extends KerasLayer {
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Unsupported Invalid Keras config
*/
public KerasELU(Map<String, Object> layerConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
this(layerConfig, true);
}
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @param enforceTrainingConfig whether to enforce training-related configuration options
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Invalid Keras config
*/
public KerasELU(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
double alpha = 1.0; // Set default alpha to default in nd4j
String layerFieldLeakyReluAlpha = "alpha";
if (innerConfig.containsKey(layerFieldLeakyReluAlpha)) {
alpha = (double) innerConfig.get(layerFieldLeakyReluAlpha);
}
IActivation leakyReLU = new ActivationELU(alpha);
this.layer = new ActivationLayer.Builder().name(this.layerName).activation(leakyReLU).build();
}
/**
* Get layer output type.
*
* @param inputType Array of InputTypes
* @return output type as InputType
* @throws InvalidKerasConfigurationException Invalid Keras config
*/
public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
if (inputType.length > 1)
throw new InvalidKerasConfigurationException(
"Keras Activation layer accepts only one input (received " + inputType.length + ")");
return this.getActivationLayer().getOutputType(-1, inputType[0]);
}
/**
* Get DL4J ActivationLayer.
*
* @return ActivationLayer
*/
public ActivationLayer getActivationLayer() {
return (ActivationLayer) this.layer;
}
}

99
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasReLU.java Normal file
View File

@ -0,0 +1,99 @@
/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.nn.modelimport.keras.layers.advanced.activations;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ActivationLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.ActivationLReLU;
import org.nd4j.linalg.activations.impl.ActivationReLU;
import java.util.Map;
/**
* Imports ReLU layer from Keras
*
* @author Alex Black
*/
public class KerasReLU extends KerasLayer {
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Unsupported Invalid Keras config
*/
public KerasReLU(Map<String, Object> layerConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
this(layerConfig, true);
}
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @param enforceTrainingConfig whether to enforce training-related configuration options
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Invalid Keras config
*/
public KerasReLU(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
Double maxValue = (Double) innerConfig.get("max_value");
double negativeSlope = 0.0;
double threshold = 0.0;
if (innerConfig.containsKey("negative_slope")) {
negativeSlope = (double) innerConfig.get("negative_slope");
}
if (innerConfig.containsKey("threshold")) {
threshold = (double) innerConfig.get("threshold");
}
this.layer = new ActivationLayer.Builder().name(this.layerName)
.activation(new ActivationReLU(maxValue, threshold, negativeSlope)).build();
}
/**
* Get layer output type.
*
* @param inputType Array of InputTypes
* @return output type as InputType
* @throws InvalidKerasConfigurationException Invalid Keras config
*/
public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
if (inputType.length > 1)
throw new InvalidKerasConfigurationException(
"Keras Activation layer accepts only one input (received " + inputType.length + ")");
return this.getActivationLayer().getOutputType(-1, inputType[0]);
}
/**
* Get DL4J ActivationLayer.
*
* @return ActivationLayer
*/
public ActivationLayer getActivationLayer() {
return (ActivationLayer) this.layer;
}
}

85
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasSoftmax.java Normal file
View File

@ -0,0 +1,85 @@
/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.nn.modelimport.keras.layers.advanced.activations;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ActivationLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.nd4j.linalg.activations.impl.ActivationSoftmax;
import java.util.Map;
/**
* Imports Softmax layer from Keras
*
* @author Alex Black
*/
public class KerasSoftmax extends KerasLayer {
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Unsupported Invalid Keras config
*/
public KerasSoftmax(Map<String, Object> layerConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
this(layerConfig, true);
}
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @param enforceTrainingConfig whether to enforce training-related configuration options
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Invalid Keras config
*/
public KerasSoftmax(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
this.layer = new ActivationLayer.Builder().name(this.layerName).activation(new ActivationSoftmax()).build();
}
/**
* Get layer output type.
*
* @param inputType Array of InputTypes
* @return output type as InputType
* @throws InvalidKerasConfigurationException Invalid Keras config
*/
public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
if (inputType.length > 1)
throw new InvalidKerasConfigurationException(
"Keras Activation layer accepts only one input (received " + inputType.length + ")");
return this.getActivationLayer().getOutputType(-1, inputType[0]);
}
/**
* Get DL4J ActivationLayer.
*
* @return ActivationLayer
*/
public ActivationLayer getActivationLayer() {
return (ActivationLayer) this.layer;
}
}

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasLayerUtils.java
View File

@ -25,9 +25,7 @@ import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.layers.KerasInput;
import org.deeplearning4j.nn.modelimport.keras.layers.advanced.activations.KerasLeakyReLU;
import org.deeplearning4j.nn.modelimport.keras.layers.advanced.activations.KerasPReLU;
import org.deeplearning4j.nn.modelimport.keras.layers.advanced.activations.KerasThresholdedReLU;
import org.deeplearning4j.nn.modelimport.keras.layers.advanced.activations.*;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.*;
import org.deeplearning4j.nn.modelimport.keras.layers.core.*;
import org.deeplearning4j.nn.modelimport.keras.layers.embeddings.KerasEmbedding;
@ -313,6 +311,12 @@ public class KerasLayerUtils {
if (lambdaLayer != null){
layer = new KerasLambda(layerConfig, enforceTrainingConfig, lambdaLayer);
}
} else if(layerClassName.equals(conf.getLAYER_CLASS_NAME_RELU())){
layer = new KerasReLU(layerConfig, enforceTrainingConfig);
} else if(layerClassName.equals(conf.getLAYER_CLASS_NAME_ELU())){
layer = new KerasELU(layerConfig, enforceTrainingConfig);
} else if(layerClassName.equals(conf.getLAYER_CLASS_NAME_SOFTMAX())){
layer = new KerasSoftmax(layerConfig, enforceTrainingConfig);
}
if (layer == null){
Class<? extends KerasLayer> customConfig = customLayers.get(layerClassName);

140
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/BaseDL4JTest.java
View File

@ -1,140 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.nn.modelimport.keras;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.javacpp.Pointer;
import org.junit.After;
import org.junit.Before;
import org.junit.Rule;
import org.junit.rules.TestName;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.profiler.ProfilerConfig;
import java.lang.management.ManagementFactory;
import java.util.List;
import java.util.Map;
import java.util.Properties;
@Slf4j
public class BaseDL4JTest {
@Rule
public TestName name = new TestName();
protected long startTime;
protected int threadCountBefore;
/**
* Override this to set the profiling mode for the tests defined in the child class
*/
public OpExecutioner.ProfilingMode getProfilingMode(){
return OpExecutioner.ProfilingMode.SCOPE_PANIC;
}
/**
* Override this to set the datatype of the tests defined in the child class
*/
public DataType getDataType(){
return DataType.DOUBLE;
}
public DataType getDefaultFPDataType(){
return getDataType();
}
@Before
public void beforeTest(){
log.info("{}.{}", getClass().getSimpleName(), name.getMethodName());
Nd4j.getExecutioner().setProfilingMode(getProfilingMode());
Nd4j.getExecutioner().setProfilingConfig(ProfilerConfig.builder().build());
Nd4j.setDefaultDataTypes(getDataType(), getDefaultFPDataType());
startTime = System.currentTimeMillis();
threadCountBefore = ManagementFactory.getThreadMXBean().getThreadCount();
}
@After
public void afterTest(){
//Attempt to keep workspaces isolated between tests
Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();
MemoryWorkspace currWS = Nd4j.getMemoryManager().getCurrentWorkspace();
Nd4j.getMemoryManager().setCurrentWorkspace(null);
if(currWS != null){
//Not really safe to continue testing under this situation... other tests will likely fail with obscure
// errors that are hard to track back to this
log.error("Open workspace leaked from test! Exiting - {}, isOpen = {} - {}", currWS.getId(), currWS.isScopeActive(), currWS);
System.exit(1);
}
StringBuilder sb = new StringBuilder();
long maxPhys = Pointer.maxPhysicalBytes();
long maxBytes = Pointer.maxBytes();
long currPhys = Pointer.physicalBytes();
long currBytes = Pointer.totalBytes();
long jvmTotal = Runtime.getRuntime().totalMemory();
long jvmMax = Runtime.getRuntime().maxMemory();
int threadsAfter = ManagementFactory.getThreadMXBean().getThreadCount();
long duration = System.currentTimeMillis() - startTime;
sb.append(getClass().getSimpleName()).append(".").append(name.getMethodName())
.append(": ").append(duration).append(" ms")
.append(", threadCount: (").append(threadCountBefore).append("->").append(threadsAfter).append(")")
.append(", jvmTotal=").append(jvmTotal)
.append(", jvmMax=").append(jvmMax)
.append(", totalBytes=").append(currBytes).append(", maxBytes=").append(maxBytes)
.append(", currPhys=").append(currPhys).append(", maxPhys=").append(maxPhys);
List<MemoryWorkspace> ws = Nd4j.getWorkspaceManager().getAllWorkspacesForCurrentThread();
if(ws != null && ws.size() > 0){
long currSize = 0;
for(MemoryWorkspace w : ws){
currSize += w.getCurrentSize();
}
if(currSize > 0){
sb.append(", threadWSSize=").append(currSize)
.append(" (").append(ws.size()).append(" WSs)");
}
}
Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
Object o = p.get("cuda.devicesInformation");
if(o instanceof List){
List<Map<String,Object>> l = (List<Map<String, Object>>) o;
if(l.size() > 0) {
sb.append(" [").append(l.size())
.append(" GPUs: ");
for (int i = 0; i < l.size(); i++) {
Map<String,Object> m = l.get(i);
if(i > 0)
sb.append(",");
sb.append("(").append(m.get("cuda.freeMemory")).append(" free, ")
.append(m.get("cuda.totalMemory")).append(" total)");
}
sb.append("]");
}
}
log.info(sb.toString());
}
}

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/MiscTests.java
View File

@ -17,6 +17,7 @@
package org.deeplearning4j.nn.modelimport.keras;
import org.apache.commons.io.FileUtils;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.utils.DL4JKerasModelValidator;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Rule;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/FullModelComparisons.java
View File

@ -23,7 +23,7 @@ import org.datavec.api.split.NumberedFileInputSplit;
import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator;
import org.deeplearning4j.nn.layers.recurrent.LSTM;
import org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/JsonTest.java
View File

@ -18,7 +18,7 @@ package org.deeplearning4j.nn.modelimport.keras.configurations;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.KerasFlattenRnnPreprocessor;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.PermutePreprocessor;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/Keras1ModelConfigurationTest.java
View File

@ -20,7 +20,7 @@ import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Test;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/Keras2ModelConfigurationTest.java
View File

@ -21,7 +21,7 @@ import lombok.val;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/KerasInitilizationTest.java
View File

@ -18,7 +18,7 @@ package org.deeplearning4j.nn.modelimport.keras.configurations;
import org.deeplearning4j.nn.conf.distribution.*;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/KerasModelImportTest.java
View File

@ -17,7 +17,7 @@
package org.deeplearning4j.nn.modelimport.keras.configurations;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasCustomLayerTest.java
View File

@ -20,7 +20,7 @@ import lombok.extern.slf4j.Slf4j;
import org.apache.commons.io.FileUtils;
import org.deeplearning4j.common.resources.DL4JResources;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.layers.custom.KerasLRN;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasLambdaTest.java
View File

@ -19,7 +19,7 @@ package org.deeplearning4j.nn.modelimport.keras.e2e;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel;

29
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java
View File

@ -30,7 +30,7 @@ import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
import org.deeplearning4j.nn.conf.layers.LossLayer;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel;
@ -724,6 +724,29 @@ public class KerasModelEndToEndTest extends BaseDL4JTest {
}
}
@Test
public void testActivationLayers() throws Exception {
String[] names = new String[]{
"ELU_0_model.h5",
"LeakyReLU_0_model.h5",
"ReLU_0_model.h5",
"ReLU_1_model.h5",
"ReLU_2_model.h5",
"ReLU_3_model.h5",
"Softmax_0_model.h5",
"ThresholdReLU_0_model.h5",
};
for(String name : names ){
System.out.println("Starting test: " + name);
String modelPath = "modelimport/keras/examples/activations/" + name;
String inputsOutputPath = "modelimport/keras/examples/activations/" + (name.substring(0,name.length()-"model.h5".length()) + "inputs_and_outputs.h5");
importEndModelTest(modelPath, inputsOutputPath, true, true,
true, true, false, null, null);
}
}
private ComputationGraph importFunctionalModelH5Test(String modelPath) throws Exception {
return importFunctionalModelH5Test(modelPath, null, false);
}
@ -991,8 +1014,8 @@ public class KerasModelEndToEndTest extends BaseDL4JTest {
}
Nd4j.setDataType(DataType.DOUBLE);
boolean passed = GradientCheckUtil.checkGradients(netToTest, eps, max_rel_error, min_abs_error, true, false,
input, labels, null, null, true, 9);
boolean passed = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(netToTest).input(input)
.labels(labels).subset(true).maxPerParam(9));
assertTrue("Gradient check failed", passed);
}

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasYolo9000PredictTest.java
View File

@ -19,7 +19,7 @@ package org.deeplearning4j.nn.modelimport.keras.e2e;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasSpaceToDepth;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasYolo9000Test.java
View File

@ -18,7 +18,7 @@ package org.deeplearning4j.nn.modelimport.keras.e2e;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasSpaceToDepth;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activation/KerasLeakyReLUTest.java
View File

@ -17,7 +17,7 @@
package org.deeplearning4j.nn.modelimport.keras.layers.advanced.activation;
import org.deeplearning4j.nn.conf.layers.ActivationLayer;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;

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