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Merge 

Signed-off-by: AlexDBlack <blacka101@gmail.com>
master
AlexDBlack 2019-11-16 23:21:45 +11:00
parent cab90d3dc7 a856922fe9
commit da4bf0209b
200 changed files with 1245 additions and 1766 deletions
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2
CONTRIBUTING.md
View File

@ -9,7 +9,7 @@ Deeplearning4j's [open issues are here](https://github.com/eclipse/deeplearning4
Note that you will need to [build dl4j from source](https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source)
For some tips on contributing to open source, this [post is helpful](http://blog.smartbear.com/programming/14-ways-to-contribute-to-open-source-without-being-a-programming-genius-or-a-rock-star/).
For some tips on contributing to open source, this [post is helpful](https://smartbear.com/blog/test-and-monitor/14-ways-to-contribute-to-open-source-without-being/).
## Contributions

2
arbiter/arbiter-core/src/assembly/bin.xml
View File

@ -61,7 +61,7 @@
<outputDirectory>examples</outputDirectory>
<!--
<lineEnding>unix</lineEnding>
http://stackoverflow.com/questions/2958282/stranges-files-in-my-assembly-since-switching-to-lineendingunix-lineending
https://stackoverflow.com/questions/2958282/stranges-files-in-my-assembly-since-switching-to-lineendingunix-lineending
-->
</fileSet>

5
datavec/datavec-api/pom.xml
View File

@ -52,11 +52,6 @@
<artifactId>joda-time</artifactId>
<version>${jodatime.version}</version>
</dependency>
<dependency>
<groupId>org.yaml</groupId>
<artifactId>snakeyaml</artifactId>
<version>${snakeyaml.version}</version>
</dependency>
<!-- ND4J Shaded Jackson Dependencies -->
<dependency>
<groupId>org.nd4j</groupId>

10
datavec/datavec-arrow/pom.xml
View File

@ -29,21 +29,11 @@
<name>datavec-arrow</name>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-arrow</artifactId>
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>org.datavec</groupId>
<artifactId>datavec-api</artifactId>
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>com.carrotsearch</groupId>
<artifactId>hppc</artifactId>
<version>${hppc.version}</version>
</dependency>
<dependency>
<groupId>org.apache.arrow</groupId>
<artifactId>arrow-vector</artifactId>

20
datavec/datavec-data/datavec-data-nlp/pom.xml
View File

@ -44,26 +44,6 @@
<artifactId>datavec-api</artifactId>
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>commons-logging</groupId>
<artifactId>commons-logging</artifactId>
<version>${commons-logging.version}</version>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-core</artifactId>
<version>${spring.version}</version>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-context</artifactId>
<version>${spring.version}</version>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-beans</artifactId>
<version>${spring.version}</version>
</dependency>
<dependency>
<groupId>org.cleartk</groupId>
<artifactId>cleartk-snowball</artifactId>

30
datavec/datavec-geo/pom.xml
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@ -31,36 +31,6 @@
<artifactId>datavec-api</artifactId>
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>${geo.jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>${geo.jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-annotations</artifactId>
<version>${geo.jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.dataformat</groupId>
<artifactId>jackson-dataformat-yaml</artifactId>
<version>${geo.jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.dataformat</groupId>
<artifactId>jackson-dataformat-xml</artifactId>
<version>${geo.jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.datatype</groupId>
<artifactId>jackson-datatype-joda</artifactId>
<version>${geo.jackson.version}</version>
</dependency>
<dependency>
<groupId>com.maxmind.geoip2</groupId>
<artifactId>geoip2</artifactId>

30
datavec/datavec-hadoop/pom.xml
View File

@ -35,41 +35,11 @@
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>com.sun.xml.bind</groupId>
<artifactId>jaxb-core</artifactId>
<version>${jaxb.version}</version>
</dependency>
<dependency>
<groupId>com.sun.xml.bind</groupId>
<artifactId>jaxb-impl</artifactId>
<version>${jaxb.version}</version>
</dependency>
<dependency>
<groupId>io.netty</groupId>
<artifactId>netty</artifactId>
<version>${netty.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-compress</artifactId>
<version>${commons-compress.version}</version>
</dependency>
<dependency>
<groupId>org.apache.zookeeper</groupId>
<artifactId>zookeeper</artifactId>
<version>${zookeeper.version}</version>
<exclusions>
<exclusion>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
</exclusion>
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>

37
datavec/datavec-local/pom.xml
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@ -73,42 +73,7 @@
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>${geo.jackson.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>${geo.jackson.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-annotations</artifactId>
<version>${geo.jackson.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.dataformat</groupId>
<artifactId>jackson-dataformat-yaml</artifactId>
<version>${geo.jackson.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.dataformat</groupId>
<artifactId>jackson-dataformat-xml</artifactId>
<version>${geo.jackson.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.datatype</groupId>
<artifactId>jackson-datatype-joda</artifactId>
<version>${geo.jackson.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.datavec</groupId>
<artifactId>datavec-python</artifactId>

5
datavec/datavec-perf/pom.xml
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@ -41,11 +41,6 @@
<artifactId>slf4j-api</artifactId>
<version>${slf4j.version}</version>
</dependency>
<dependency>
<groupId>com.github.oshi</groupId>
<artifactId>oshi-core</artifactId>
<version>${oshi.version}</version>
</dependency>
<dependency>
<groupId>org.datavec</groupId>
<artifactId>datavec-data-image</artifactId>

20
datavec/datavec-spark-inference-parent/datavec-spark-inference-client/pom.xml
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@ -41,26 +41,6 @@
<version>1.0.0-SNAPSHOT</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>commons-codec</groupId>
<artifactId>commons-codec</artifactId>
<version>${commons-codec.version}</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpclient</artifactId>
<version>${httpclient.version}</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpcore</artifactId>
<version>${httpcore.version}</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpmime</artifactId>
<version>${httpmime.version}</version>
</dependency>
<dependency>
<groupId>com.mashape.unirest</groupId>
<artifactId>unirest-java</artifactId>

6
datavec/datavec-spark-inference-parent/datavec-spark-inference-server/pom.xml
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@ -94,12 +94,6 @@
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.yaml</groupId>
<artifactId>snakeyaml</artifactId>
<version>${snakeyaml.version}</version>
</dependency>
<dependency>
<groupId>com.typesafe.play</groupId>
<artifactId>play-java_2.11</artifactId>

5
datavec/datavec-spark/pom.xml
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@ -39,11 +39,6 @@
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>

10
deeplearning4j/deeplearning4j-common/src/main/java/org/deeplearning4j/common/resources/DL4JResources.java
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@ -64,7 +64,7 @@ public class DL4JResources {
/**
* Set the base download URL for (most) DL4J datasets and models.<br>
* This usually doesn't need to be set manually unless there is some issue with the default location
* @param baseDownloadURL Base download URL to set. For example, http://blob.deeplearning4j.org/
* @param baseDownloadURL Base download URL to set. For example, https://dl4jdata.blob.core.windows.net/
*/
public static void setBaseDownloadURL(@NonNull String baseDownloadURL){
baseURL = baseDownloadURL;
@ -79,8 +79,8 @@ public class DL4JResources {
/**
* Get the URL relative to the base URL.<br>
* For example, if baseURL is "http://blob.deeplearning4j.org/", and relativeToBase is "/datasets/iris.dat"
* this simply returns "http://blob.deeplearning4j.org/datasets/iris.dat"
* For example, if baseURL is "https://dl4jdata.blob.core.windows.net/", and relativeToBase is "/datasets/iris.dat"
* this simply returns "https://dl4jdata.blob.core.windows.net/datasets/iris.dat"
*
* @param relativeToBase Relative URL
* @return URL
@ -92,8 +92,8 @@ public class DL4JResources {
/**
* Get the URL relative to the base URL as a String.<br>
* For example, if baseURL is "http://blob.deeplearning4j.org/", and relativeToBase is "/datasets/iris.dat"
* this simply returns "http://blob.deeplearning4j.org/datasets/iris.dat"
* For example, if baseURL is "https://dl4jdata.blob.core.windows.net/", and relativeToBase is "/datasets/iris.dat"
* this simply returns "https://dl4jdata.blob.core.windows.net/datasets/iris.dat"
*
* @param relativeToBase Relative URL
* @return URL

54
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/LayerHelperValidationUtil.java
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@ -35,6 +35,7 @@ import org.nd4j.linalg.indexing.conditions.Conditions;
import org.nd4j.linalg.ops.transforms.Transforms;
import java.lang.reflect.Field;
import java.lang.reflect.Method;
import java.util.*;
import static org.junit.Assert.*;
@ -63,6 +64,30 @@ public class LayerHelperValidationUtil {
private DataSetIterator data;
}
public static void disableCppHelpers(){
try {
Class<?> c = Class.forName("org.nd4j.nativeblas.Nd4jCpu$Environment");
Method m = c.getMethod("getInstance");
Object instance = m.invoke(null);
Method m2 = c.getMethod("allowHelpers", boolean.class);
m2.invoke(instance, false);
} catch (Throwable t){
throw new RuntimeException(t);
}
}
public static void enableCppHelpers(){
try{
Class<?> c = Class.forName("org.nd4j.nativeblas.Nd4jCpu$Environment");
Method m = c.getMethod("getInstance");
Object instance = m.invoke(null);
Method m2 = c.getMethod("allowHelpers", boolean.class);
m2.invoke(instance, true);
} catch (Throwable t){
throw new RuntimeException(t);
}
}
public static void validateMLN(MultiLayerNetwork netOrig, TestCase t){
assertNotNull(t.getAllowHelpersForClasses());
assertFalse(t.getAllowHelpersForClasses().isEmpty());
@ -95,7 +120,13 @@ public class LayerHelperValidationUtil {
for (boolean train : new boolean[]{false, true}) {
assertEquals(net1NoHelper.params(), net2With.params());
String s = "Feed forward test - " + t.getTestName() + " - " + (train ? "Train: " : "Test: ");
List<INDArray> ff1 = net1NoHelper.feedForward(t.getFeatures(), train);
List<INDArray> ff1;
try {
disableCppHelpers();
ff1 = net1NoHelper.feedForward(t.getFeatures(), train);
} finally {
enableCppHelpers();
}
List<INDArray> ff2 = net2With.feedForward(t.getFeatures(), train);
List<String> paramKeys = new ArrayList<>(net1NoHelper.paramTable().keySet());
Collections.sort(paramKeys);
@ -131,7 +162,13 @@ public class LayerHelperValidationUtil {
log.info("Forward pass, max relative error: " + layerName + " - " + maxRE);
}
INDArray out1 = net1NoHelper.output(t.getFeatures(), train);
INDArray out1;
try {
disableCppHelpers();
out1 = net1NoHelper.output(t.getFeatures(), train);
} finally {
enableCppHelpers();
}
INDArray out2 = net2With.output(t.getFeatures(), train);
INDArray relError = relError(out1, out2, t.getMinAbsError());
double maxRE = relError.maxNumber().doubleValue();
@ -148,7 +185,13 @@ public class LayerHelperValidationUtil {
Preconditions.checkNotNull(t.getLabels(), "Labels are not set (null)");
log.info("Validation - checking scores");
double s1 = net1NoHelper.score(new DataSet(t.getFeatures(), t.getLabels()));
double s1;
try {
disableCppHelpers();
s1 = net1NoHelper.score(new DataSet(t.getFeatures(), t.getLabels()));
} finally {
enableCppHelpers();
}
double s2 = net2With.score(new DataSet(t.getFeatures(), t.getLabels()));
double re = relError(s1, s2);
@ -168,7 +211,12 @@ public class LayerHelperValidationUtil {
net2With.setInput(t.getFeatures());
net2With.setLabels(t.getLabels());
try {
disableCppHelpers();
net1NoHelper.computeGradientAndScore();
} finally {
enableCppHelpers();
}
net2With.computeGradientAndScore();
List<String> paramKeys = new ArrayList<>(net1NoHelper.paramTable().keySet());

107
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/TestBatchNormBp.java
View File

@ -1,107 +0,0 @@
package org.deeplearning4j;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.WorkspaceMode;
import org.deeplearning4j.nn.conf.layers.BatchNormalization;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.mkldnn.MKLDNNBatchNormHelper;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.junit.Test;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import java.lang.reflect.Field;
import static junit.framework.TestCase.*;
public class TestBatchNormBp {
@Test
public void test(){
Nd4j.getRandom().setSeed(12345);
// INDArray in = Nd4j.rand(DataType.FLOAT, 1, 3, 4, 4);
INDArray in = Nd4j.rand(DataType.FLOAT, 1, 3, 15, 15);
INDArray mean = in.mean(0, 2, 3); //Nd4j.rand(DataType.FLOAT, 3);
INDArray var = in.var(0, 2, 3); //Nd4j.rand(DataType.FLOAT, 3);
INDArray eps = Nd4j.rand(DataType.FLOAT, in.shape());
// INDArray gamma = Nd4j.ones(DataType.FLOAT, 3);
// INDArray beta = Nd4j.zeros(DataType.FLOAT, 3);
INDArray gamma = Nd4j.rand(DataType.FLOAT, 3);
INDArray beta = Nd4j.rand(DataType.FLOAT, 3);
double e = 1e-5;
INDArray dLdIn = in.ulike();
INDArray dLdm = mean.ulike();
INDArray dLdv = var.ulike();
INDArray dLdg = gamma.ulike();
INDArray dLdb = beta.ulike();
DynamicCustomOp op = DynamicCustomOp.builder("batchnorm_bp")
.addInputs(in, mean, var, eps, gamma, beta)
.addIntegerArguments(
1, //Apply scale
1, //Apply beta
1) //Axis (NCHW)
.addFloatingPointArguments(e)
.addOutputs(dLdIn, dLdm, dLdv, dLdg, dLdb)
.build();
Nd4j.exec(op);
System.out.println(dLdIn);
}
@Test
public void compareImpls() throws Exception {
Nd4j.getRandom().setSeed(12345);
INDArray in = Nd4j.rand(DataType.FLOAT, 1, 3, 15, 15);
INDArray mean = in.mean(0, 2, 3).reshape(1,3);
INDArray var = in.var(0, 2, 3).reshape(1,3);
INDArray eps = Nd4j.rand(DataType.FLOAT, in.shape());
INDArray gamma = Nd4j.rand(DataType.FLOAT, 1,3);
INDArray beta = Nd4j.rand(DataType.FLOAT, 1,3);
double e = 1e-3;
INDArray dLdIn = in.ulike();
INDArray dLdm = mean.ulike();
INDArray dLdv = var.ulike();
INDArray dLdg = gamma.ulike();
INDArray dLdb = beta.ulike();
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.inferenceWorkspaceMode(WorkspaceMode.NONE)
.trainingWorkspaceMode(WorkspaceMode.NONE)
.list()
.layer(new BatchNormalization.Builder().nIn(3).nOut(3).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
org.deeplearning4j.nn.layers.normalization.BatchNormalization bn = (org.deeplearning4j.nn.layers.normalization.BatchNormalization) net.getLayer(0);
assertNotNull(bn.getHelper());
Field f = bn.getClass().getDeclaredField("helper");
f.setAccessible(true);
f.set(bn, null);
assertNull(bn.getHelper());
MKLDNNBatchNormHelper h = new MKLDNNBatchNormHelper(DataType.FLOAT);
net.output(in, true);
bn.setInput(in, LayerWorkspaceMgr.noWorkspaces());
Pair<Gradient,INDArray> p = net.backpropGradient(eps, LayerWorkspaceMgr.noWorkspaces());
h.preOutput(in, true, new long[]{1,3}, gamma, beta, mean, var, 0.5, e, LayerWorkspaceMgr.noWorkspaces());
Pair<Gradient,INDArray> pmkl = h.backpropGradient(in, eps, new long[]{1,3}, gamma, beta, dLdg, dLdb, e, LayerWorkspaceMgr.noWorkspaces());
INDArray dldin_dl4j = p.getSecond();
System.out.println("dl4j == mkldnn: " + p.getSecond().equals(pmkl.getSecond()));
}
}

19
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/graph/TestComputationGraphNetwork.java
View File

@ -2143,4 +2143,23 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
INDArray in = Nd4j.create(DataType.FLOAT, 1, 3, 16, 16, 16);
INDArray out = cg.outputSingle(in);
}
@Test
public void testDualEmbedding(){
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
.graphBuilder()
.addInputs("in")
.addLayer("e1", new EmbeddingLayer.Builder().nIn(10).nOut(5).build(), "in")
.addLayer("e2", new EmbeddingLayer.Builder().nIn(10).nOut(5).build(), "in")
.addLayer("out", new OutputLayer.Builder().nIn(10).nOut(2).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "e1", "e2")
.setOutputs("out")
.build();
ComputationGraph cg = new ComputationGraph(conf);
cg.init();
INDArray in = Nd4j.createFromArray(3).reshape(1, 1);
INDArray label = Nd4j.createFromArray(1, 0).reshape(1, 2);
cg.fit(new DataSet(in, label));
}
}

11
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/MinimalSameDiffDense.java
View File

@ -70,9 +70,20 @@ public class MinimalSameDiffDense extends SameDiffLayer {
@Override
public void initializeParameters(Map<String, INDArray> params) {
String b = DefaultParamInitializer.BIAS_KEY;
if(paramWeightInit != null && paramWeightInit.containsKey(b)){
paramWeightInit.get(b).init(nIn, nOut, params.get(b).shape(), 'c', params.get(b));
} else {
params.get(DefaultParamInitializer.BIAS_KEY).assign(0);
}
String w = DefaultParamInitializer.WEIGHT_KEY;
if(paramWeightInit != null && paramWeightInit.containsKey(w)){
paramWeightInit.get(w).init(nIn, nOut, params.get(w).shape(), 'c', params.get(w));
} else {
initWeights(nIn, nOut, weightInit, params.get(DefaultParamInitializer.WEIGHT_KEY));
}
}
//OPTIONAL methods:
// public void setNIn(InputType inputType, boolean override)

8
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/samediff/testlayers/SameDiffConv.java
View File

@ -109,17 +109,21 @@ public class SameDiffConv extends SameDiffLayer {
@Override
public void initializeParameters(Map<String, INDArray> params) {
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
double fanIn = nIn * kernel[0] * kernel[1];
double fanOut = nOut * kernel[0] * kernel[1] / ((double) stride[0] * stride[1]);
for (Map.Entry<String, INDArray> e : params.entrySet()) {
if(paramWeightInit != null && paramWeightInit.containsKey(e.getKey())){
paramWeightInit.get(e.getKey()).init(fanIn, fanOut, e.getValue().shape(), 'c', e.getValue());
} else {
if (ConvolutionParamInitializer.BIAS_KEY.equals(e.getKey())) {
e.getValue().assign(0);
} else {
double fanIn = nIn * kernel[0] * kernel[1];
double fanOut = nOut * kernel[0] * kernel[1] / ((double) stride[0] * stride[1]);
WeightInitUtil.initWeights(fanIn, fanOut, e.getValue().shape(), weightInit, null, 'c', e.getValue());
}
}
}
}
}
@Override
public SDVariable defineLayer(SameDiff sameDiff, SDVariable layerInput, Map<String, SDVariable> paramTable, SDVariable mask) {

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

@ -88,6 +88,9 @@ public class SameDiffDense extends SameDiffLayer {
@Override
public void initializeParameters(Map<String,INDArray> params){
for(Map.Entry<String,INDArray> e : params.entrySet()){
if(paramWeightInit != null && paramWeightInit.containsKey(e.getKey())){
paramWeightInit.get(e.getKey()).init(nIn, nOut, e.getValue().shape(), 'c', e.getValue());
} else {
if(DefaultParamInitializer.BIAS_KEY.equals(e.getKey())){
e.getValue().assign(0.0);
} else {
@ -96,6 +99,7 @@ public class SameDiffDense extends SameDiffLayer {
}
}
}
}
@Override
public SDVariable defineLayer(SameDiff sd, SDVariable layerInput, Map<String, SDVariable> paramTable, SDVariable mask) {

7
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/mkldnn/ValidateMKLDNN.java
View File

@ -50,6 +50,7 @@ import static org.junit.Assume.assumeTrue;
public class ValidateMKLDNN extends BaseDL4JTest {
@Test
public void validateConvSubsampling() throws Exception {
//Only run test if using nd4j-native backend
@ -138,12 +139,14 @@ public class ValidateMKLDNN extends BaseDL4JTest {
ConvolutionMode cm = ConvolutionMode.Truncate;
for (int minibatch : new int[]{1, 3}) {
for (boolean b : new boolean[]{true, false}) {
inputSize[0] = minibatch;
INDArray f = Nd4j.rand(Nd4j.defaultFloatingPointType(), inputSize);
INDArray l = TestUtils.randomOneHot(minibatch, 10);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.FLOAT)
.updater(new Adam(0.01))
.convolutionMode(cm)
.seed(12345)
@ -154,7 +157,7 @@ public class ValidateMKLDNN extends BaseDL4JTest {
.padding(0, 0)
.nOut(3)
.build())
.layer(new BatchNormalization.Builder().helperAllowFallback(false)/*.eps(0)*/.build())
.layer(new BatchNormalization.Builder().useLogStd(b).helperAllowFallback(false)/*.eps(0)*/.build())
.layer(new ConvolutionLayer.Builder().activation(Activation.TANH)
.kernelSize(kernel)
.stride(stride)
@ -186,6 +189,7 @@ public class ValidateMKLDNN extends BaseDL4JTest {
LayerHelperValidationUtil.validateMLN(netWith, tc);
}
}
}
@Test @Ignore //https://github.com/deeplearning4j/deeplearning4j/issues/7272
public void validateLRN() {
@ -265,6 +269,7 @@ public class ValidateMKLDNN extends BaseDL4JTest {
@Test
public void compareBatchNormBackward() throws Exception {
assumeTrue(Nd4j.getBackend().getClass().getName().toLowerCase().contains("native"));
Nd4j.getRandom().setSeed(12345);
INDArray in = Nd4j.rand(DataType.FLOAT, 1, 3, 15, 15);

6
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b4.java
View File

@ -339,7 +339,13 @@ public class RegressionTest100b4 extends BaseDL4JTest {
INDArray outAct = net.output(in);
//19 layers - CPU vs. GPU difference accumulates notably, but appears to be correct
if(Nd4j.getBackend().getClass().getName().toLowerCase().contains("native")){
assertEquals(outExp, outAct);
} else {
boolean eq = outExp.equalsWithEps(outAct, 0.1);
assertTrue(eq);
}
}
@Test

98
deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/SamplingDataSetIterator.java
View File

@ -24,101 +24,11 @@ import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import java.util.List;
/**
* A wrapper for a dataset to sample from.
* This will randomly sample from the given dataset.
* @author Adam GIbson
*/
public class SamplingDataSetIterator implements DataSetIterator {
/**
*
*/
private static final long serialVersionUID = -2700563801361726914L;
private DataSet sampleFrom;
private int batchSize;
private int totalNumberSamples;
private int numTimesSampled;
@Getter
private DataSetPreProcessor preProcessor;
/**
*
* @param sampleFrom the dataset to sample from
* @param batchSize the batch size to sample
* @param totalNumberSamples the sample size
* @deprecated Use {@link org.nd4j.linalg.dataset.api.iterator.SamplingDataSetIterator}
*/
@Deprecated
public class SamplingDataSetIterator extends org.nd4j.linalg.dataset.api.iterator.SamplingDataSetIterator {
public SamplingDataSetIterator(DataSet sampleFrom, int batchSize, int totalNumberSamples) {
super();
this.sampleFrom = sampleFrom;
this.batchSize = batchSize;
this.totalNumberSamples = totalNumberSamples;
super(sampleFrom, batchSize, totalNumberSamples);
}
@Override
public boolean hasNext() {
return numTimesSampled < totalNumberSamples;
}
@Override
public DataSet next() {
DataSet ret = sampleFrom.sample(batchSize);
numTimesSampled += batchSize;
return ret;
}
@Override
public void remove() {
throw new UnsupportedOperationException();
}
@Override
public int inputColumns() {
return sampleFrom.numInputs();
}
@Override
public int totalOutcomes() {
return sampleFrom.numOutcomes();
}
@Override
public boolean resetSupported() {
return true;
}
@Override
public boolean asyncSupported() {
return true;
}
@Override
public void reset() {
numTimesSampled = 0;
}
@Override
public int batch() {
return batchSize;
}
@Override
public void setPreProcessor(DataSetPreProcessor preProcessor) {
this.preProcessor = preProcessor;
}
@Override
public List<String> getLabels() {
return null;
}
@Override
public DataSet next(int num) {
DataSet ret = sampleFrom.sample(num);
numTimesSampled++;
return ret;
}
}

2
deeplearning4j/deeplearning4j-graph/src/main/java/org/deeplearning4j/graph/models/deepwalk/DeepWalk.java
View File

@ -38,7 +38,7 @@ import java.util.concurrent.atomic.AtomicLong;
/**Implementation of the DeepWalk graph vectorization model, based on the paper
* <i>DeepWalk: Online Learning of Social Representations</i> by Perozzi, Al-Rfou & Skiena (2014),
* <a href="http://arxiv.org/abs/1403.6652">http://arxiv.org/abs/1403.6652</a><br>
* <a href="https://arxiv.org/abs/1403.6652">https://arxiv.org/abs/1403.6652</a><br>
* Similar to word2vec in nature, DeepWalk is an unsupervised learning algorithm that learns a vector representation
* of each vertex in a graph. Vector representations are learned using walks (usually random walks) on the vertices in
* the graph.<br>

2
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/Hdf5Archive.java
View File

@ -17,6 +17,7 @@
package org.deeplearning4j.nn.modelimport.keras;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.hdf5.*;
import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.FloatPointer;
import org.bytedeco.javacpp.Loader;
@ -32,7 +33,6 @@ import java.lang.Exception;
import java.util.ArrayList;
import java.util.List;
import org.bytedeco.hdf5.*;
import static org.bytedeco.hdf5.global.hdf5.*;
/**

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/KerasSequentialModel.java
View File

@ -17,7 +17,6 @@
package org.deeplearning4j.nn.modelimport.keras;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.IOutputLayer;
import org.deeplearning4j.nn.conf.BackpropType;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;

2
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/exceptions/InvalidKerasConfigurationException.java
View File

@ -40,6 +40,6 @@ public class InvalidKerasConfigurationException extends Exception {
}
private static String appendDocumentationURL(String message) {
return message + ". For more information, see http://deeplearning4j.org/model-import-keras.";
return message + ". For more information, see http://deeplearning4j.org/docs/latest/keras-import-overview";
}
}

4
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/exceptions/UnsupportedKerasConfigurationException.java
View File

@ -22,7 +22,7 @@ package org.deeplearning4j.nn.modelimport.keras.exceptions;
* is not currently supported.
*
* See <a href="https://deeplearning4j.org/docs/latest/keras-import-overview">https://deeplearning4j.org/docs/latest/keras-import-overview</a>
* for more information and file an issue at <a href="http://github.com/deeplearning4j/deeplearning4j/issues">http://github.com/deeplearning4j/deeplearning4j/issues</a>.
* for more information and file an issue at <a href="https://github.com/eclipse/deeplearning4j/issues">https://github.com/eclipse/deeplearning4j/issues</a>.
*
* @author dave@skymind.io
*/
@ -41,6 +41,6 @@ public class UnsupportedKerasConfigurationException extends Exception {
}
private static String appendDocumentationURL(String message) {
return message + ". Please file an issue at http://github.com/deeplearning4j/deeplearning4j/issues.";
return message + ". Please file an issue at https://github.com/eclipse/deeplearning4j/issues.";
}
}

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasPReLU.java
View File

@ -18,7 +18,6 @@ package org.deeplearning4j.nn.modelimport.keras.layers.advanced.activations;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.PReLULayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
@ -27,9 +26,8 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfig
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.PReLUParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.linalg.util.ArrayUtil;
import java.util.HashMap;
@ -79,14 +77,12 @@ public class KerasPReLU extends KerasLayer {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, ALPHA_CONSTRAINT, conf, kerasMajorVersion);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, ALPHA_INIT,
IWeightInit init = getWeightInitFromConfig(layerConfig, ALPHA_INIT,
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
long[] axes = getSharedAxes(layerConfig);
PReLULayer.Builder builder = new PReLULayer.Builder().sharedAxes(axes)
.weightInit(weightInit.getWeightInitFunction(distribution)).name(layerName);
.weightInit(init).name(layerName);
if (weightConstraint != null){
builder.constrainWeights(weightConstraint);
}

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution1D.java
View File

@ -17,14 +17,12 @@
package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.weights.IWeightInit;
import java.util.Map;
@ -83,15 +81,13 @@ public class KerasAtrousConvolution1D extends KerasConvolution {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
Convolution1DLayer.Builder builder = new Convolution1DLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInit(init)
.dilation(getDilationRate(layerConfig, 1, conf, true)[0])
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))

9
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution2D.java
View File

@ -17,14 +17,12 @@
package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.weights.IWeightInit;
import java.util.Map;
@ -84,14 +82,13 @@ public class KerasAtrousConvolution2D extends KerasConvolution {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
ConvolutionLayer.Builder builder = new ConvolutionLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction())
.weightInit(init)
.dilation(getDilationRate(layerConfig, 2, conf, true))
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))

2
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution.java
View File

@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
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.params.ConvolutionParamInitializer;
@ -30,7 +29,6 @@ import org.nd4j.linalg.factory.Nd4j;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.removeDefaultWeights;

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution1D.java
View File

@ -22,7 +22,6 @@ import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
@ -30,10 +29,9 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurat
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap;
import java.util.Map;
@ -94,15 +92,13 @@ public class KerasConvolution1D extends KerasConvolution {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
Convolution1DLayer.Builder builder = new Convolution1DLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInit(init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 1, conf, kerasMajorVersion)[0])

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution2D.java
View File

@ -21,14 +21,12 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.weights.IWeightInit;
import java.util.Map;
@ -87,10 +85,8 @@ public class KerasConvolution2D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 2, conf, false);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion);
@ -100,7 +96,7 @@ public class KerasConvolution2D extends KerasConvolution {
ConvolutionLayer.Builder builder = new ConvolutionLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInit(init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 2, conf, kerasMajorVersion))

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution3D.java
View File

@ -21,15 +21,13 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution3D;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.weights.IWeightInit;
import java.util.Map;
@ -88,10 +86,8 @@ public class KerasConvolution3D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 3, conf, false);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion);
@ -101,7 +97,7 @@ public class KerasConvolution3D extends KerasConvolution {
Convolution3D.Builder builder = new Convolution3D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInit(init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 3, conf, kerasMajorVersion))

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasDeconvolution2D.java
View File

@ -20,14 +20,12 @@ import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Deconvolution2D;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.weights.IWeightInit;
import java.util.Map;
@ -86,10 +84,8 @@ public class KerasDeconvolution2D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 2, conf, false);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion);
@ -99,7 +95,7 @@ public class KerasDeconvolution2D extends KerasConvolution {
Deconvolution2D.Builder builder = new Deconvolution2D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInit(init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 2, conf, kerasMajorVersion))

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasDepthwiseConvolution2D.java
View File

@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
@ -30,9 +29,8 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfig
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasRegularizerUtils;
import org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.Collections;
import java.util.HashMap;
@ -126,10 +124,8 @@ public class KerasDepthwiseConvolution2D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 2, conf, false);
Pair<WeightInit, Distribution> depthWiseInit = getWeightInitFromConfig(layerConfig,
IWeightInit depthWiseInit = getWeightInitFromConfig(layerConfig,
conf.getLAYER_FIELD_DEPTH_WISE_INIT(), enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit depthWeightInit = depthWiseInit.getFirst();
Distribution depthDistribution = depthWiseInit.getSecond();
val nIn = getNInFromConfig(previousLayers);
@ -152,7 +148,7 @@ public class KerasDepthwiseConvolution2D extends KerasConvolution {
.nIn(nIn)
.nOut(nIn * depthMultiplier)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(depthWeightInit.getWeightInitFunction(depthDistribution))
.weightInit(depthWiseInit)
.depthMultiplier(depthMultiplier)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))

16
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasSeparableConvolution2D.java
View File

@ -20,7 +20,6 @@ import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.SeparableConvolution2D;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
@ -28,9 +27,8 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfig
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasRegularizerUtils;
import org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap;
import java.util.Map;
@ -93,17 +91,13 @@ public class KerasSeparableConvolution2D extends KerasConvolution {
int depthMultiplier = getDepthMultiplier(layerConfig, conf);
Pair<WeightInit, Distribution> depthWiseInit = getWeightInitFromConfig(layerConfig,
IWeightInit depthWiseInit = getWeightInitFromConfig(layerConfig,
conf.getLAYER_FIELD_DEPTH_WISE_INIT(), enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit depthWeightInit = depthWiseInit.getFirst();
Distribution depthDistribution = depthWiseInit.getSecond();
Pair<WeightInit, Distribution> pointWiseInit = getWeightInitFromConfig(layerConfig,
IWeightInit pointWiseInit = getWeightInitFromConfig(layerConfig,
conf.getLAYER_FIELD_POINT_WISE_INIT(), enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit pointWeightInit = pointWiseInit.getFirst();
Distribution pointDistribution = pointWiseInit.getSecond();
if (depthWeightInit != pointWeightInit || depthDistribution != pointDistribution)
if ( !depthWiseInit.getClass().equals(pointWiseInit.getClass()) )
if (enforceTrainingConfig)
throw new UnsupportedKerasConfigurationException(
"Specifying different initialization for depth- and point-wise weights not supported.");
@ -126,7 +120,7 @@ public class KerasSeparableConvolution2D extends KerasConvolution {
SeparableConvolution2D.Builder builder = new SeparableConvolution2D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(depthWeightInit.getWeightInitFunction(depthDistribution))
.weightInit(depthWiseInit)
.depthMultiplier(depthMultiplier)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasUpsampling3D.java
View File

@ -17,7 +17,6 @@
package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Upsampling2D;
import org.deeplearning4j.nn.conf.layers.Upsampling3D;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasZeroPadding3D.java
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@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer;
import org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDense.java
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@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
@ -29,9 +28,8 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurat
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap;
import java.util.Map;
@ -95,15 +93,13 @@ public class KerasDense extends KerasLayer {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
DenseLayer.Builder builder = new DenseLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout).activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInit(init)
.biasInit(0.0)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.hasBias(hasBias);

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasFlatten.java
View File

@ -22,7 +22,6 @@ import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional;
import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;
import org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasRepeatVector.java
View File

@ -18,7 +18,6 @@ package org.deeplearning4j.nn.modelimport.keras.layers.core;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.DropoutLayer;
import org.deeplearning4j.nn.conf.layers.misc.RepeatVector;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;

2
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasReshape.java
View File

@ -18,7 +18,6 @@ package org.deeplearning4j.nn.modelimport.keras.layers.core;
import lombok.val;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
@ -26,7 +25,6 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurat
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.nd4j.linalg.util.ArrayUtil;
import java.util.List;
import java.util.Map;

12
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings/KerasEmbedding.java
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@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
@ -30,11 +29,10 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfig
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap;
import java.util.Map;
@ -104,12 +102,10 @@ public class KerasEmbedding extends KerasLayer {
"on Embedding layers. Zero Masking for the Embedding layer only works with unidirectional LSTM for now."
+ " If you want to have this behaviour for your imported model " +
"in DL4J, apply masking as a pre-processing step to your input." +
"See https://deeplearning4j.org/usingrnns#masking for more on this.");
"See http://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent#masking for more on this.");
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_EMBEDDING_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_EMBEDDING_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
LayerConstraint embeddingConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_EMBEDDINGS_CONSTRAINT(), conf, kerasMajorVersion);
@ -121,7 +117,7 @@ public class KerasEmbedding extends KerasLayer {
.inferInputLength(inferInputLength)
.nOut(getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout).activation(Activation.IDENTITY)
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInit(init)
.biasInit(0.0)
.l1(this.weightL1Regularization)
.l2(this.weightL2Regularization)

11
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/local/KerasLocallyConnected1D.java
View File

@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.LocallyConnected1D;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
@ -29,9 +28,8 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfig
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasConvolution;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap;
import java.util.Map;
@ -90,11 +88,8 @@ public class KerasLocallyConnected1D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 1, conf, false);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
// TODO: take care of distribution and bias init
//Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion);
@ -104,7 +99,7 @@ public class KerasLocallyConnected1D extends KerasConvolution {
LocallyConnected1D.Builder builder = new LocallyConnected1D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getActivationFromConfig(layerConfig, conf))
.weightInit(weightInit)
.weightInit(conf.getKERAS_PARAM_NAME_W(), init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 1, conf, kerasMajorVersion)[0])

16
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/local/KerasLocallyConnected2D.java
View File

@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.LocallyConnected2D;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
@ -29,9 +28,8 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfig
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasConvolution;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap;
import java.util.Map;
@ -39,9 +37,7 @@ import java.util.Map;
import static org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasConvolutionUtils.*;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasActivationUtils.getActivationFromConfig;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasInitilizationUtils.getWeightInitFromConfig;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.getHasBiasFromConfig;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.getNOutFromConfig;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.removeDefaultWeights;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.*;
/**
@ -92,11 +88,9 @@ public class KerasLocallyConnected2D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 2, conf, false);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
// TODO: take care of distribution and bias init
//Distribution distribution = init.getSecond();
// TODO: take care of bias init
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion);
@ -106,7 +100,7 @@ public class KerasLocallyConnected2D extends KerasConvolution {
LocallyConnected2D.Builder builder = new LocallyConnected2D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getActivationFromConfig(layerConfig, conf))
.weightInit(weightInit)
.weightInit(conf.getKERAS_PARAM_NAME_W(), init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 2, conf, kerasMajorVersion))

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java
View File

@ -31,7 +31,6 @@ import org.deeplearning4j.nn.params.BatchNormalizationParamInitializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;

15
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasLSTM.java
View File

@ -22,7 +22,6 @@ import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
import org.deeplearning4j.nn.conf.layers.LSTM;
@ -35,7 +34,7 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfig
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.LSTMParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
@ -151,15 +150,11 @@ public class KerasLSTM extends KerasLayer {
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
Pair<WeightInit, Distribution> recurrentInit = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INNER_INIT(),
IWeightInit recurrentInit = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INNER_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit recurrentWeightInit = recurrentInit.getFirst();
Distribution recurrentDistribution = recurrentInit.getSecond();
boolean hasBias = getHasBiasFromConfig(layerConfig, conf);
@ -186,8 +181,8 @@ public class KerasLSTM extends KerasLayer {
.nOut(getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInitRecurrent(recurrentWeightInit.getWeightInitFunction(recurrentDistribution))
.weightInit(init)
.weightInitRecurrent(recurrentInit)
.biasInit(0.0) // TODO: this is incorrect
.l1(this.weightL1Regularization)
.l2(this.weightL2Regularization);

15
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasSimpleRnn.java
View File

@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
import org.deeplearning4j.nn.conf.layers.Layer;
@ -34,7 +33,7 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfig
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.SimpleRnnParamInitializer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
@ -124,15 +123,11 @@ public class KerasSimpleRnn extends KerasLayer {
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
Pair<WeightInit, Distribution> recurrentInit = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INNER_INIT(),
IWeightInit recurrentInit = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INNER_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit recurrentWeightInit = recurrentInit.getFirst();
Distribution recurrentDistribution = recurrentInit.getSecond();
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
this.returnSequences = (Boolean) innerConfig.get(conf.getLAYER_FIELD_RETURN_SEQUENCES());
@ -154,8 +149,8 @@ public class KerasSimpleRnn extends KerasLayer {
.nOut(getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution))
.weightInitRecurrent(recurrentWeightInit.getWeightInitFunction(recurrentDistribution))
.weightInit(init)
.weightInitRecurrent(recurrentInit)
.biasInit(0.0)
.l1(this.weightL1Regularization)
.l2(this.weightL2Regularization);

3
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/preprocessing/sequence/TimeSeriesGenerator.java
View File

@ -20,9 +20,7 @@ import com.google.gson.Gson;
import com.google.gson.reflect.TypeToken;
import lombok.Data;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.preprocessing.text.KerasTokenizer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;
@ -31,7 +29,6 @@ import org.nd4j.linalg.primitives.Pair;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

3
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/preprocessors/KerasFlattenRnnPreprocessor.java
View File

@ -22,9 +22,8 @@ import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonProperty;
/**

4
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/preprocessors/ReshapePreprocessor.java
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@ -19,17 +19,15 @@ package org.deeplearning4j.nn.modelimport.keras.preprocessors;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.util.ArrayUtil;
import org.nd4j.shade.jackson.annotation.JsonIgnoreProperties;
import org.nd4j.shade.jackson.annotation.JsonProperty;

2
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/preprocessors/TensorFlowCnnToFeedForwardPreProcessor.java
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@ -20,9 +20,9 @@ import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.shade.jackson.annotation.JsonCreator;
import org.nd4j.shade.jackson.annotation.JsonProperty;

13
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/DL4JKerasModelValidator.java
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@ -1,28 +1,15 @@
package org.deeplearning4j.nn.modelimport.keras.utils;
import lombok.NonNull;
import org.apache.commons.io.IOUtils;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.config.KerasModelConfiguration;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.validation.Nd4jCommonValidator;
import org.nd4j.validation.ValidationResult;
import java.io.BufferedReader;
import java.io.File;
import java.io.IOException;
import java.io.InputStreamReader;
import java.nio.charset.StandardCharsets;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.zip.ZipEntry;
import java.util.zip.ZipFile;
/**
* A utility for validating serialized Keras sequential and functional models for import into DL4J

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasActivationUtils.java
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@ -21,7 +21,6 @@ import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurat
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.*;
import java.util.Map;

85
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasInitilizationUtils.java
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@ -21,8 +21,7 @@ import org.deeplearning4j.nn.conf.distribution.*;
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.weights.WeightInit;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.weights.*;
import java.util.HashMap;
import java.util.Map;
@ -42,7 +41,7 @@ public class KerasInitilizationUtils {
* @return DL4J weight initialization enum
* @see WeightInit
*/
public static Pair<WeightInit, Distribution> mapWeightInitialization(String kerasInit,
public static IWeightInit mapWeightInitialization(String kerasInit,
KerasLayerConfiguration conf,
Map<String, Object> initConfig,
int kerasMajorVersion)
@ -50,68 +49,63 @@ public class KerasInitilizationUtils {
// TODO: Identity and VarianceScaling need "scale" factor
WeightInit init = null;
Distribution dist = null;
if (kerasInit != null) {
if (kerasInit.equals(conf.getINIT_GLOROT_NORMAL()) ||
kerasInit.equals(conf.getINIT_GLOROT_NORMAL_ALIAS())) {
init = WeightInit.XAVIER;
return WeightInit.XAVIER.getWeightInitFunction();
} else if (kerasInit.equals(conf.getINIT_GLOROT_UNIFORM()) ||
kerasInit.equals(conf.getINIT_GLOROT_UNIFORM_ALIAS())) {
init = WeightInit.XAVIER_UNIFORM;
return WeightInit.XAVIER_UNIFORM.getWeightInitFunction();
} else if (kerasInit.equals(conf.getINIT_LECUN_NORMAL()) ||
kerasInit.equals(conf.getINIT_LECUN_NORMAL_ALIAS())) {
init = WeightInit.LECUN_NORMAL;
return WeightInit.LECUN_NORMAL.getWeightInitFunction();
} else if (kerasInit.equals(conf.getINIT_LECUN_UNIFORM()) ||
kerasInit.equals(conf.getINIT_LECUN_UNIFORM_ALIAS())) {
init = WeightInit.LECUN_UNIFORM;
return WeightInit.LECUN_UNIFORM.getWeightInitFunction();
} else if (kerasInit.equals(conf.getINIT_HE_NORMAL()) ||
kerasInit.equals(conf.getINIT_HE_NORMAL_ALIAS())) {
init = WeightInit.RELU;
return WeightInit.RELU.getWeightInitFunction();
} else if (kerasInit.equals(conf.getINIT_HE_UNIFORM()) ||
kerasInit.equals(conf.getINIT_HE_UNIFORM_ALIAS())) {
init = WeightInit.RELU_UNIFORM;
return WeightInit.RELU_UNIFORM.getWeightInitFunction();
} else if (kerasInit.equals(conf.getINIT_ONE()) ||
kerasInit.equals(conf.getINIT_ONES()) ||
kerasInit.equals(conf.getINIT_ONES_ALIAS())) {
init = WeightInit.ONES;
return WeightInit.ONES.getWeightInitFunction();
} else if (kerasInit.equals(conf.getINIT_ZERO()) ||
kerasInit.equals(conf.getINIT_ZEROS()) ||
kerasInit.equals(conf.getINIT_ZEROS_ALIAS())) {
init = WeightInit.ZERO;
return WeightInit.ZERO.getWeightInitFunction();
} else if (kerasInit.equals(conf.getINIT_UNIFORM()) ||
kerasInit.equals(conf.getINIT_RANDOM_UNIFORM()) ||
kerasInit.equals(conf.getINIT_RANDOM_UNIFORM_ALIAS())) {
if (kerasMajorVersion == 2) {
double minVal = (double) initConfig.get(conf.getLAYER_FIELD_INIT_MINVAL());
double maxVal = (double) initConfig.get(conf.getLAYER_FIELD_INIT_MAXVAL());
dist = new UniformDistribution(minVal, maxVal);
return new WeightInitDistribution(new UniformDistribution(minVal, maxVal));
} else {
double scale = 0.05;
if (initConfig.containsKey(conf.getLAYER_FIELD_INIT_SCALE()))
scale = (double) initConfig.get(conf.getLAYER_FIELD_INIT_SCALE());
dist = new UniformDistribution(-scale, scale);
return new WeightInitDistribution(new UniformDistribution(-scale, scale));
}
init = WeightInit.DISTRIBUTION;
} else if (kerasInit.equals(conf.getINIT_NORMAL()) ||
kerasInit.equals(conf.getINIT_RANDOM_NORMAL()) ||
kerasInit.equals(conf.getINIT_RANDOM_NORMAL_ALIAS())) {
if (kerasMajorVersion == 2) {
double mean = (double) initConfig.get(conf.getLAYER_FIELD_INIT_MEAN());
double stdDev = (double) initConfig.get(conf.getLAYER_FIELD_INIT_STDDEV());
dist = new NormalDistribution(mean, stdDev);
return new WeightInitDistribution(new NormalDistribution(mean, stdDev));
} else {
double scale = 0.05;
if (initConfig.containsKey(conf.getLAYER_FIELD_INIT_SCALE()))
scale = (double) initConfig.get(conf.getLAYER_FIELD_INIT_SCALE());
dist = new NormalDistribution(0, scale);
return new WeightInitDistribution(new NormalDistribution(0, scale));
}
init = WeightInit.DISTRIBUTION;
} else if (kerasInit.equals(conf.getINIT_CONSTANT()) ||
kerasInit.equals(conf.getINIT_CONSTANT_ALIAS())) {
double value = (double) initConfig.get(conf.getLAYER_FIELD_INIT_VALUE());
dist = new ConstantDistribution(value);
init = WeightInit.DISTRIBUTION;
return new WeightInitDistribution(new ConstantDistribution(value));
} else if (kerasInit.equals(conf.getINIT_ORTHOGONAL()) ||
kerasInit.equals(conf.getINIT_ORTHOGONAL_ALIAS())) {
if (kerasMajorVersion == 2) {
@ -121,34 +115,38 @@ public class KerasInitilizationUtils {
} catch (Exception e) {
gain = (int) initConfig.get(conf.getLAYER_FIELD_INIT_GAIN());
}
dist = new OrthogonalDistribution(gain);
return new WeightInitDistribution(new OrthogonalDistribution(gain));
} else {
double scale = 1.1;
if (initConfig.containsKey(conf.getLAYER_FIELD_INIT_SCALE()))
scale = (double) initConfig.get(conf.getLAYER_FIELD_INIT_SCALE());
dist = new OrthogonalDistribution(scale);
return new WeightInitDistribution(new OrthogonalDistribution(scale));
}
init = WeightInit.DISTRIBUTION;
} else if (kerasInit.equals(conf.getINIT_TRUNCATED_NORMAL()) ||
kerasInit.equals(conf.getINIT_TRUNCATED_NORMAL_ALIAS())) {
double mean = (double) initConfig.get(conf.getLAYER_FIELD_INIT_MEAN());
double stdDev = (double) initConfig.get(conf.getLAYER_FIELD_INIT_STDDEV());
dist = new TruncatedNormalDistribution(mean, stdDev);
init = WeightInit.DISTRIBUTION;
return new WeightInitDistribution(new TruncatedNormalDistribution(mean, stdDev));
} else if (kerasInit.equals(conf.getINIT_IDENTITY()) ||
kerasInit.equals(conf.getINIT_IDENTITY_ALIAS())) {
if (kerasMajorVersion == 2) {
double gain = (double) initConfig.get(conf.getLAYER_FIELD_INIT_GAIN());
if (gain != 1.)
log.warn("Scaled identity weight init not supported, setting gain=1");
if (gain != 1.0)
if (gain != 1.0) {
return new WeightInitIdentity(gain);
} else {
return new WeightInitIdentity();
}
} else {
double scale = 1.;
if (initConfig.containsKey(conf.getLAYER_FIELD_INIT_SCALE()))
scale = (double) initConfig.get(conf.getLAYER_FIELD_INIT_SCALE());
if (scale != 1.)
log.warn("Scaled identity weight init not supported, setting scale=1");
if (scale != 1.0) {
return new WeightInitIdentity(scale);
} else {
return new WeightInitIdentity();
}
}
init = WeightInit.IDENTITY;
} else if (kerasInit.equals(conf.getINIT_VARIANCE_SCALING())) {
double scale;
try {
@ -156,32 +154,27 @@ public class KerasInitilizationUtils {
} catch (Exception e) {
scale = (int) initConfig.get(conf.getLAYER_FIELD_INIT_SCALE());
}
if (scale != 1.)
log.warn("Scaled identity weight init not supported, setting scale=1");
String mode = (String) initConfig.get(conf.getLAYER_FIELD_INIT_MODE());
String distribution = (String) initConfig.get(conf.getLAYER_FIELD_INIT_DISTRIBUTION());
switch (mode) {
case "fan_in":
if (distribution.equals("normal")) {
init = WeightInit.VAR_SCALING_NORMAL_FAN_IN;
return new WeightInitVarScalingNormalFanIn(scale);
} else {
init = WeightInit.VAR_SCALING_UNIFORM_FAN_IN;
return new WeightInitVarScalingUniformFanIn(scale);
}
break;
case "fan_out":
if (distribution.equals("normal")) {
init = WeightInit.VAR_SCALING_NORMAL_FAN_OUT;
return new WeightInitVarScalingNormalFanOut(scale);
} else {
init = WeightInit.VAR_SCALING_UNIFORM_FAN_OUT;
return new WeightInitVarScalingUniformFanOut(scale);
}
break;
case "fan_avg":
if (distribution.equals("normal")) {
init = WeightInit.VAR_SCALING_NORMAL_FAN_AVG;
return new WeightInitVarScalingNormalFanAvg(scale);
} else {
init = WeightInit.VAR_SCALING_UNIFORM_FAN_AVG;
return new WeightInitVarScalingUniformFanAvg(scale);
}
break;
default:
throw new InvalidKerasConfigurationException("Initialization argument 'mode' has to be either " +
"fan_in, fan_out or fan_avg");
@ -190,7 +183,7 @@ public class KerasInitilizationUtils {
throw new UnsupportedKerasConfigurationException("Unknown keras weight initializer " + kerasInit);
}
}
return new Pair<>(init, dist);
throw new IllegalStateException("Error getting Keras weight initialization");
}
/**
@ -202,7 +195,7 @@ public class KerasInitilizationUtils {
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Unsupported Keras config
*/
public static Pair<WeightInit, Distribution> getWeightInitFromConfig(Map<String, Object> layerConfig, String initField,
public static IWeightInit getWeightInitFromConfig(Map<String, Object> layerConfig, String initField,
boolean enforceTrainingConfig,
KerasLayerConfiguration conf,
int kerasMajorVersion)
@ -225,14 +218,14 @@ public class KerasInitilizationUtils {
throw new UnsupportedKerasConfigurationException("Incomplete initialization class");
}
}
Pair<WeightInit, Distribution> init;
IWeightInit init;
try {
init = mapWeightInitialization(kerasInit, conf, initMap, kerasMajorVersion);
} catch (UnsupportedKerasConfigurationException e) {
if (enforceTrainingConfig)
throw e;
else {
init = new Pair<>(WeightInit.XAVIER, null);
init = new WeightInitXavier();
log.warn("Unknown weight initializer " + kerasInit + " (Using XAVIER instead).");
}
}

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasModelUtils.java
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@ -21,7 +21,6 @@ import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.StringUtils;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/KerasTestUtils.java
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@ -16,7 +16,6 @@
package org.deeplearning4j.nn.modelimport.keras;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.layers.BaseLayer;
import org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer;
import org.nd4j.linalg.learning.regularization.L1Regularization;
@ -25,7 +24,6 @@ import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.List;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
public class KerasTestUtils {

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/MiscTests.java
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@ -22,8 +22,6 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.linalg.util.Nd4jValidator;
import org.nd4j.resources.Resources;
import org.nd4j.validation.ValidationResult;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/FullModelComparisons.java
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@ -21,7 +21,6 @@ import org.datavec.api.records.reader.SequenceRecordReader;
import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader;
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;
@ -30,7 +29,6 @@ import org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Assert;
import org.junit.Ignore;
import org.junit.Rule;
import org.junit.Test;

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/Keras1ModelConfigurationTest.java
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@ -24,7 +24,6 @@ import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Test;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.InputStream;

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

@ -30,11 +30,9 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStream;
import java.util.Arrays;

6
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/KerasInitilizationTest.java
View File

@ -25,6 +25,8 @@ import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.layers.core.KerasDense;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.WeightInitIdentity;
import org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanIn;
import org.junit.Test;
import java.util.HashMap;
@ -94,11 +96,11 @@ public class KerasInitilizationTest extends BaseDL4JTest {
WeightInit.RELU_UNIFORM.getWeightInitFunction(),
WeightInit.ONES.getWeightInitFunction(),
WeightInit.ZERO.getWeightInitFunction(),
WeightInit.IDENTITY.getWeightInitFunction(),
new WeightInitIdentity(0.2),
WeightInit.DISTRIBUTION.getWeightInitFunction(new NormalDistribution(mean, stdDev)),
WeightInit.DISTRIBUTION.getWeightInitFunction(new OrthogonalDistribution(gain)),
WeightInit.DISTRIBUTION.getWeightInitFunction(new ConstantDistribution(value)),
WeightInit.VAR_SCALING_NORMAL_FAN_IN.getWeightInitFunction()};
new WeightInitVarScalingNormalFanIn(0.2)};
}
private Distribution[] dl4jDistributions() {

6
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/KerasModelImportTest.java
View File

@ -17,22 +17,16 @@
package org.deeplearning4j.nn.modelimport.keras.configurations;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.File;
import java.io.IOException;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertNotNull;
/**

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasLambdaTest.java
View File

@ -31,7 +31,6 @@ import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.File;

23
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java
View File

@ -24,22 +24,19 @@ import org.deeplearning4j.eval.ROCMultiClass;
import org.deeplearning4j.gradientcheck.GradientCheckUtil;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.layers.IOutputLayer;
import org.deeplearning4j.nn.conf.layers.CnnLossLayer;
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.layers.recurrent.LSTM;
import org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer;
import org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer;
import org.deeplearning4j.nn.modelimport.keras.*;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelUtils;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration;
import org.deeplearning4j.nn.transferlearning.TransferLearning;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.junit.Ignore;
import org.junit.Rule;
import org.junit.Test;
@ -47,27 +44,25 @@ import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.*;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.resources.Resources;
import java.io.File;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStream;
import java.net.URL;
import java.nio.file.Files;
import java.nio.file.StandardCopyOption;
import java.util.*;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Random;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
import static org.junit.Assert.*;
/**
* Unit tests for end-to-end Keras model import.

4
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasYolo9000PredictTest.java
View File

@ -21,7 +21,6 @@ import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasSpaceToDepth;
import org.deeplearning4j.nn.transferlearning.TransferLearning;
@ -31,11 +30,8 @@ import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.StandardCopyOption;
/**
* Import previously stored YOLO9000 Keras net from https://github.com/allanzelener/YAD2K.

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasYolo9000Test.java
View File

@ -26,7 +26,6 @@ import org.junit.Ignore;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.File;

5
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasAtrousConvolution1DTest.java
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@ -27,16 +27,11 @@ import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasAtrousC
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Test;
import org.nd4j.linalg.learning.regularization.L1Regularization;
import org.nd4j.linalg.learning.regularization.L2Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/**
* @author Max Pumperla

4
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasConvolution3DTest.java
View File

@ -28,9 +28,6 @@ import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasConvolu
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Test;
import org.nd4j.linalg.learning.regularization.L1Regularization;
import org.nd4j.linalg.learning.regularization.L2Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.ArrayList;
import java.util.HashMap;
@ -39,7 +36,6 @@ import java.util.Map;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/**
* @author Max Pumperla

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasCropping1DTest.java
View File

@ -24,7 +24,6 @@ import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasCropping1D;
import org.junit.Test;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasCropping3DTest.java
View File

@ -16,13 +16,11 @@
package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D;
import org.deeplearning4j.nn.conf.layers.convolutional.Cropping3D;
import org.deeplearning4j.nn.modelimport.keras.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;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasCropping2D;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasCropping3D;
import org.junit.Test;

4
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasDepthwiseConvolution2DTest.java
View File

@ -30,15 +30,11 @@ import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Test;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.learning.regularization.L1Regularization;
import org.nd4j.linalg.learning.regularization.L2Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.*;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/**
* @author Max Pumperla

4
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasUpsampling1DTest.java
View File

@ -17,18 +17,14 @@
package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.layers.Upsampling1D;
import org.deeplearning4j.nn.conf.layers.Upsampling2D;
import org.deeplearning4j.nn.modelimport.keras.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;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasUpsampling1D;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasUpsampling2D;
import org.junit.Test;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertEquals;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasUpsampling2DTest.java
View File

@ -17,13 +17,11 @@
package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.layers.Upsampling2D;
import org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer;
import org.deeplearning4j.nn.modelimport.keras.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;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasUpsampling2D;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasZeroPadding1D;
import org.junit.Test;
import java.util.ArrayList;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasZeroPadding3DTest.java
View File

@ -17,12 +17,10 @@
package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer;
import org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer;
import org.deeplearning4j.nn.modelimport.keras.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;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasZeroPadding2D;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasZeroPadding3D;
import org.junit.Test;

5
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDenseTest.java
View File

@ -26,16 +26,11 @@ import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Test;
import org.nd4j.linalg.learning.regularization.L1Regularization;
import org.nd4j.linalg.learning.regularization.L2Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/**
* @author Max Pumperla

6
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasPermuteTest.java
View File

@ -24,10 +24,12 @@ 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.preprocessors.PermutePreprocessor;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor;
import org.junit.Test;
import java.util.*;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertEquals;

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasReshapeTest.java
View File

@ -24,11 +24,11 @@ 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.preprocessors.ReshapePreprocessor;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.junit.Assert;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import java.util.*;

6
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings/KerasEmbeddingTest.java
View File

@ -26,11 +26,7 @@ import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.*;
import static org.junit.Assert.assertEquals;

3
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/local/KerasLocallyConnected1DTest.java
View File

@ -20,7 +20,6 @@ import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.dropout.Dropout;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.LocallyConnected1D;
import org.deeplearning4j.nn.conf.layers.LocallyConnected2D;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasTestUtils;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
@ -31,10 +30,8 @@ import org.junit.Test;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
/**

9
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/local/KerasLocallyConnected2DTest.java
View File

@ -27,15 +27,14 @@ import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration;
import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.Test;
import org.nd4j.linalg.learning.regularization.L1Regularization;
import org.nd4j.linalg.learning.regularization.L2Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.*;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/**
* @author Max Pumperla

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling3DTest.java
View File

@ -19,7 +19,6 @@ package org.deeplearning4j.nn.modelimport.keras.layers.pooling;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.PoolingType;
import org.deeplearning4j.nn.conf.layers.Subsampling3DLayer;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;

9
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasLSTMTest.java
View File

@ -33,14 +33,13 @@ import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Assert;
import org.junit.Test;
import org.nd4j.linalg.learning.regularization.L1Regularization;
import org.nd4j.linalg.learning.regularization.L2Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import java.util.*;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/**
* @author Max Pumperla

5
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/optimizers/OptimizerImport.java
View File

@ -16,15 +16,12 @@
package org.deeplearning4j.nn.modelimport.keras.optimizers;
import org.deeplearning4j.config.DL4JSystemProperties;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel;
import org.deeplearning4j.nn.modelimport.keras.e2e.KerasModelEndToEndTest;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder;
import org.deeplearning4j.util.DL4JFileUtils;
import org.junit.Test;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.File;
@ -32,8 +29,6 @@ import java.io.InputStream;
import java.nio.file.Files;
import java.nio.file.StandardCopyOption;
import static java.io.File.createTempFile;
public class OptimizerImport extends BaseDL4JTest {
@Test

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/preprocessing/sequence/TimeSeriesGeneratorImportTest.java
View File

@ -18,9 +18,7 @@ package org.deeplearning4j.nn.modelimport.keras.preprocessing.sequence;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.preprocessing.text.KerasTokenizer;
import org.junit.Test;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.IOException;

6
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/preprocessing/text/TokenizerImportTest.java
View File

@ -19,15 +19,11 @@ package org.deeplearning4j.nn.modelimport.keras.preprocessing.text;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.junit.Test;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.IOException;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNull;
import static org.junit.Assert.assertTrue;
import static org.junit.Assert.*;
/**
* Import Keras Tokenizer

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/preprocessing/text/TokenizerTest.java
View File

@ -20,7 +20,6 @@ import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;

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

@ -29,7 +29,6 @@ import org.junit.Test;
import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources;
import java.io.File;

65
deeplearning4j/deeplearning4j-nearestneighbors-parent/deeplearning4j-nearestneighbor-server/pom.xml
View File

@ -77,71 +77,6 @@
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>com.google.protobuf</groupId>
<artifactId>protobuf-java</artifactId>
<version>${google.protobuf.version}</version>
</dependency>
<dependency>
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>${jodatime.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>${commons-lang3.version}</version>
</dependency>
<dependency>
<groupId>org.hibernate</groupId>
<artifactId>hibernate-validator</artifactId>
<version>${hibernate.version}</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-reflect</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.yaml</groupId>
<artifactId>snakeyaml</artifactId>
<version>${snakeyaml.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-annotations</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.datatype</groupId>
<artifactId>jackson-datatype-jdk8</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.datatype</groupId>
<artifactId>jackson-datatype-jsr310</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.typesafe</groupId>
<artifactId>config</artifactId>
<version>${typesafe.config.version}</version>
</dependency>
<dependency>
<groupId>com.typesafe.play</groupId>
<artifactId>play-java_2.11</artifactId>

15
deeplearning4j/deeplearning4j-nearestneighbors-parent/deeplearning4j-nearestneighbors-client/pom.xml
View File

@ -31,21 +31,6 @@
<dependencies>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpclient</artifactId>
<version>${httpclient.version}</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpcore</artifactId>
<version>${httpcore.version}</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpmime</artifactId>
<version>${httpmime.version}</version>
</dependency>
<dependency>
<groupId>com.mashape.unirest</groupId>
<artifactId>unirest-java</artifactId>

2
deeplearning4j/deeplearning4j-nearestneighbors-parent/nearestneighbor-core/src/main/java/org/deeplearning4j/clustering/quadtree/QuadTree.java
View File

@ -29,7 +29,7 @@ import static java.lang.Math.max;
* QuadTree: <a href="http://en.wikipedia.org/wiki/Quadtree">http://en.wikipedia.org/wiki/Quadtree</a>
*
* Reference impl based on the paper by:
* <a href="http://arxiv.org/pdf/1301.3342v2.pdf">http://arxiv.org/pdf/1301.3342v2.pdf</a>
* <a href="https://arxiv.org/pdf/1301.3342v2.pdf">https://arxiv.org/pdf/1301.3342v2.pdf</a>
*
* Primarily focused on 2 dimensions, may expand later if there's a reason.
*

2
deeplearning4j/deeplearning4j-nearestneighbors-parent/nearestneighbor-core/src/main/java/org/deeplearning4j/clustering/util/MathUtils.java
View File

@ -86,7 +86,7 @@ public class MathUtils {
/**
* See: http://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2
* See: https://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2
* @param v the number to getFromOrigin the next power of 2 for
* @return the next power of 2 for the passed in value
*/

6
deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-chinese/pom.xml
View File

@ -52,12 +52,6 @@
<artifactId>deeplearning4j-nlp</artifactId>
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>org.nutz</groupId>
<artifactId>nutz</artifactId>
<version>1.r.58</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.nlpcn</groupId>
<artifactId>nlp-lang</artifactId>

20
deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/pom.xml
View File

@ -33,26 +33,6 @@
</properties>
<dependencies>
<dependency>
<groupId>commons-logging</groupId>
<artifactId>commons-logging</artifactId>
<version>${commons-logging.version}</version>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-core</artifactId>
<version>${spring.version}</version>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-context</artifactId>
<version>${spring.version}</version>
</dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-beans</artifactId>
<version>${spring.version}</version>
</dependency>
<dependency>
<groupId>org.cleartk</groupId>
<artifactId>cleartk-snowball</artifactId>

15
deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/pom.xml
View File

@ -54,11 +54,6 @@
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.objenesis</groupId>
<artifactId>objenesis</artifactId>
<version>${objenesis.version}</version>
</dependency>
<dependency>
<groupId>org.mockito</groupId>
<artifactId>mockito-core</artifactId>
@ -66,16 +61,6 @@
<scope>test</scope>
</dependency>
<!-- TSNE -->
<!-- (Previously: dropwizard deps) -->
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-jackson</artifactId>
<version>${nd4j.version}</version>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>

2
deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/graph/walkers/impl/PopularityWalker.java
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@ -42,7 +42,7 @@ import java.util.*;
* Instead of rand walks, this walker produces walks based on number of edges coming into each node.
* This allows you to build walks filtering too rare nodes, or too popular nodes, depending on your demands.
*
* Original DeepWalk paper: <a href="http://arxiv.org/pdf/1403.6652v2">http://arxiv.org/pdf/1403.6652v2</a>
* Original DeepWalk paper: <a href="https://arxiv.org/pdf/1403.6652v2">https://arxiv.org/pdf/1403.6652v2</a>
* @author raver119@gmail.com
*/
public class PopularityWalker<T extends SequenceElement> extends RandomWalker<T> implements GraphWalker<T> {

2
deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/graph/walkers/impl/RandomWalker.java
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@ -37,7 +37,7 @@ import java.util.concurrent.atomic.AtomicInteger;
/**
* This is Random-based walker for SequenceVectors-based DeepWalk implementation
*
* Original DeepWalk paper: <a href="http://arxiv.org/pdf/1403.6652v2">http://arxiv.org/pdf/1403.6652v2</a>
* Original DeepWalk paper: <a href="https://arxiv.org/pdf/1403.6652v2">https://arxiv.org/pdf/1403.6652v2</a>
*
* @author AlexDBlack
* @author raver119@gmail.com

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/GradientNormalization.java
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@ -52,7 +52,7 @@ package org.deeplearning4j.nn.conf;
* </ul>
* Thus, the l2 norm of the scaled gradients will not exceed the specified threshold, though may be smaller than it<br>
* See: Pascanu, Mikolov, Bengio (2012), <i>On the difficulty of training Recurrent Neural Networks</i>,
* <a href="http://arxiv.org/abs/1211.5063">http://arxiv.org/abs/1211.5063</a><br>
* <a href="https://arxiv.org/abs/1211.5063">https://arxiv.org/abs/1211.5063</a><br>
* Threshold for clipping can be set in Layer configuration, using gradientNormalizationThreshold(double threshold)
* </p>
*

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/OrthogonalDistribution.java
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@ -23,7 +23,7 @@ import org.nd4j.shade.jackson.annotation.JsonProperty;
/**
* Orthogonal distribution, with gain parameter.<br>
* See <a href="http://arxiv.org/abs/1312.6120">http://arxiv.org/abs/1312.6120</a> for details
* See <a href="https://arxiv.org/abs/1312.6120">https://arxiv.org/abs/1312.6120</a> for details
*
*/
@EqualsAndHashCode(callSuper = false)

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