Brutex
/
cavis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

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
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

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) 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 ## Contributions

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

@ -61,7 +61,7 @@
<outputDirectory>examples</outputDirectory> <outputDirectory>examples</outputDirectory>
<!-- <!--
<lineEnding>unix</lineEnding> <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> </fileSet>

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

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

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

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

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

@ -44,26 +44,6 @@
<artifactId>datavec-api</artifactId> <artifactId>datavec-api</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </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> <dependency>
<groupId>org.cleartk</groupId> <groupId>org.cleartk</groupId>
<artifactId>cleartk-snowball</artifactId> <artifactId>cleartk-snowball</artifactId>

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

@ -31,36 +31,6 @@
<artifactId>datavec-api</artifactId> <artifactId>datavec-api</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </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> <dependency>
<groupId>com.maxmind.geoip2</groupId> <groupId>com.maxmind.geoip2</groupId>
<artifactId>geoip2</artifactId> <artifactId>geoip2</artifactId>

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

@ -35,41 +35,11 @@
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </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> <dependency>
<groupId>io.netty</groupId> <groupId>io.netty</groupId>
<artifactId>netty</artifactId> <artifactId>netty</artifactId>
<version>${netty.version}</version> <version>${netty.version}</version>
</dependency> </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> <dependency>
<groupId>org.apache.hadoop</groupId> <groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId> <artifactId>hadoop-common</artifactId>

37
datavec/datavec-local/pom.xml
View File

@ -73,42 +73,7 @@
</dependency> </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> <dependency>
<groupId>org.datavec</groupId> <groupId>org.datavec</groupId>
<artifactId>datavec-python</artifactId> <artifactId>datavec-python</artifactId>

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

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

20
datavec/datavec-spark-inference-parent/datavec-spark-inference-client/pom.xml
View File

@ -41,26 +41,6 @@
<version>1.0.0-SNAPSHOT</version> <version>1.0.0-SNAPSHOT</version>
<scope>test</scope> <scope>test</scope>
</dependency> </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> <dependency>
<groupId>com.mashape.unirest</groupId> <groupId>com.mashape.unirest</groupId>
<artifactId>unirest-java</artifactId> <artifactId>unirest-java</artifactId>

6
datavec/datavec-spark-inference-parent/datavec-spark-inference-server/pom.xml
View File

@ -94,12 +94,6 @@
<version>${scala.version}</version> <version>${scala.version}</version>
</dependency> </dependency>
<dependency>
<groupId>org.yaml</groupId>
<artifactId>snakeyaml</artifactId>
<version>${snakeyaml.version}</version>
</dependency>
<dependency> <dependency>
<groupId>com.typesafe.play</groupId> <groupId>com.typesafe.play</groupId>
<artifactId>play-java_2.11</artifactId> <artifactId>play-java_2.11</artifactId>

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

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

10
deeplearning4j/deeplearning4j-common/src/main/java/org/deeplearning4j/common/resources/DL4JResources.java
View File

@ -64,7 +64,7 @@ public class DL4JResources {
/** /**
* Set the base download URL for (most) DL4J datasets and models.<br> * 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 * 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){ public static void setBaseDownloadURL(@NonNull String baseDownloadURL){
baseURL = baseDownloadURL; baseURL = baseDownloadURL;
@ -79,8 +79,8 @@ public class DL4JResources {
/** /**
* Get the URL relative to the base URL.<br> * Get the URL relative to the base URL.<br>
* For example, if baseURL is "http://blob.deeplearning4j.org/", and relativeToBase is "/datasets/iris.dat" * For example, if baseURL is "https://dl4jdata.blob.core.windows.net/", and relativeToBase is "/datasets/iris.dat"
* this simply returns "http://blob.deeplearning4j.org/datasets/iris.dat" * this simply returns "https://dl4jdata.blob.core.windows.net/datasets/iris.dat"
* *
* @param relativeToBase Relative URL * @param relativeToBase Relative URL
* @return URL * @return URL
@ -92,8 +92,8 @@ public class DL4JResources {
/** /**
* Get the URL relative to the base URL as a String.<br> * 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" * For example, if baseURL is "https://dl4jdata.blob.core.windows.net/", and relativeToBase is "/datasets/iris.dat"
* this simply returns "http://blob.deeplearning4j.org/datasets/iris.dat" * this simply returns "https://dl4jdata.blob.core.windows.net/datasets/iris.dat"
* *
* @param relativeToBase Relative URL * @param relativeToBase Relative URL
* @return URL * @return URL

54
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/LayerHelperValidationUtil.java
View File

@ -35,6 +35,7 @@ import org.nd4j.linalg.indexing.conditions.Conditions;
import org.nd4j.linalg.ops.transforms.Transforms; import org.nd4j.linalg.ops.transforms.Transforms;
import java.lang.reflect.Field; import java.lang.reflect.Field;
import java.lang.reflect.Method;
import java.util.*; import java.util.*;
import static org.junit.Assert.*; import static org.junit.Assert.*;
@ -63,6 +64,30 @@ public class LayerHelperValidationUtil {
private DataSetIterator data; 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){ public static void validateMLN(MultiLayerNetwork netOrig, TestCase t){
assertNotNull(t.getAllowHelpersForClasses()); assertNotNull(t.getAllowHelpersForClasses());
assertFalse(t.getAllowHelpersForClasses().isEmpty()); assertFalse(t.getAllowHelpersForClasses().isEmpty());
@ -95,7 +120,13 @@ public class LayerHelperValidationUtil {
for (boolean train : new boolean[]{false, true}) { for (boolean train : new boolean[]{false, true}) {
assertEquals(net1NoHelper.params(), net2With.params()); assertEquals(net1NoHelper.params(), net2With.params());
String s = "Feed forward test - " + t.getTestName() + " - " + (train ? "Train: " : "Test: "); 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<INDArray> ff2 = net2With.feedForward(t.getFeatures(), train);
List<String> paramKeys = new ArrayList<>(net1NoHelper.paramTable().keySet()); List<String> paramKeys = new ArrayList<>(net1NoHelper.paramTable().keySet());
Collections.sort(paramKeys); Collections.sort(paramKeys);
@ -131,7 +162,13 @@ public class LayerHelperValidationUtil {
log.info("Forward pass, max relative error: " + layerName + " - " + maxRE); 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 out2 = net2With.output(t.getFeatures(), train);
INDArray relError = relError(out1, out2, t.getMinAbsError()); INDArray relError = relError(out1, out2, t.getMinAbsError());
double maxRE = relError.maxNumber().doubleValue(); double maxRE = relError.maxNumber().doubleValue();
@ -148,7 +185,13 @@ public class LayerHelperValidationUtil {
Preconditions.checkNotNull(t.getLabels(), "Labels are not set (null)"); Preconditions.checkNotNull(t.getLabels(), "Labels are not set (null)");
log.info("Validation - checking scores"); 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 s2 = net2With.score(new DataSet(t.getFeatures(), t.getLabels()));
double re = relError(s1, s2); double re = relError(s1, s2);
@ -168,7 +211,12 @@ public class LayerHelperValidationUtil {
net2With.setInput(t.getFeatures()); net2With.setInput(t.getFeatures());
net2With.setLabels(t.getLabels()); net2With.setLabels(t.getLabels());
try {
disableCppHelpers();
net1NoHelper.computeGradientAndScore(); net1NoHelper.computeGradientAndScore();
} finally {
enableCppHelpers();
}
net2With.computeGradientAndScore(); net2With.computeGradientAndScore();
List<String> paramKeys = new ArrayList<>(net1NoHelper.paramTable().keySet()); 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 in = Nd4j.create(DataType.FLOAT, 1, 3, 16, 16, 16);
INDArray out = cg.outputSingle(in); 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 @Override
public void initializeParameters(Map<String, INDArray> params) { 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); 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)); initWeights(nIn, nOut, weightInit, params.get(DefaultParamInitializer.WEIGHT_KEY));
} }
}
//OPTIONAL methods: //OPTIONAL methods:
// public void setNIn(InputType inputType, boolean override) // 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 @Override
public void initializeParameters(Map<String, INDArray> params) { public void initializeParameters(Map<String, INDArray> params) {
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) { 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()) { 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())) { if (ConvolutionParamInitializer.BIAS_KEY.equals(e.getKey())) {
e.getValue().assign(0); e.getValue().assign(0);
} else { } 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()); WeightInitUtil.initWeights(fanIn, fanOut, e.getValue().shape(), weightInit, null, 'c', e.getValue());
} }
} }
} }
} }
}
@Override @Override
public SDVariable defineLayer(SameDiff sameDiff, SDVariable layerInput, Map<String, SDVariable> paramTable, SDVariable mask) { 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 @Override
public void initializeParameters(Map<String,INDArray> params){ public void initializeParameters(Map<String,INDArray> params){
for(Map.Entry<String,INDArray> e : params.entrySet()){ 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())){ if(DefaultParamInitializer.BIAS_KEY.equals(e.getKey())){
e.getValue().assign(0.0); e.getValue().assign(0.0);
} else { } else {
@ -96,6 +99,7 @@ public class SameDiffDense extends SameDiffLayer {
} }
} }
} }
}
@Override @Override
public SDVariable defineLayer(SameDiff sd, SDVariable layerInput, Map<String, SDVariable> paramTable, SDVariable mask) { 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 { public class ValidateMKLDNN extends BaseDL4JTest {
@Test @Test
public void validateConvSubsampling() throws Exception { public void validateConvSubsampling() throws Exception {
//Only run test if using nd4j-native backend //Only run test if using nd4j-native backend
@ -138,12 +139,14 @@ public class ValidateMKLDNN extends BaseDL4JTest {
ConvolutionMode cm = ConvolutionMode.Truncate; ConvolutionMode cm = ConvolutionMode.Truncate;
for (int minibatch : new int[]{1, 3}) { for (int minibatch : new int[]{1, 3}) {
for (boolean b : new boolean[]{true, false}) {
inputSize[0] = minibatch; inputSize[0] = minibatch;
INDArray f = Nd4j.rand(Nd4j.defaultFloatingPointType(), inputSize); INDArray f = Nd4j.rand(Nd4j.defaultFloatingPointType(), inputSize);
INDArray l = TestUtils.randomOneHot(minibatch, 10); INDArray l = TestUtils.randomOneHot(minibatch, 10);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.FLOAT)
.updater(new Adam(0.01)) .updater(new Adam(0.01))
.convolutionMode(cm) .convolutionMode(cm)
.seed(12345) .seed(12345)
@ -154,7 +157,7 @@ public class ValidateMKLDNN extends BaseDL4JTest {
.padding(0, 0) .padding(0, 0)
.nOut(3) .nOut(3)
.build()) .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) .layer(new ConvolutionLayer.Builder().activation(Activation.TANH)
.kernelSize(kernel) .kernelSize(kernel)
.stride(stride) .stride(stride)
@ -186,6 +189,7 @@ public class ValidateMKLDNN extends BaseDL4JTest {
LayerHelperValidationUtil.validateMLN(netWith, tc); LayerHelperValidationUtil.validateMLN(netWith, tc);
} }
} }
}
@Test @Ignore //https://github.com/deeplearning4j/deeplearning4j/issues/7272 @Test @Ignore //https://github.com/deeplearning4j/deeplearning4j/issues/7272
public void validateLRN() { public void validateLRN() {
@ -265,6 +269,7 @@ public class ValidateMKLDNN extends BaseDL4JTest {
@Test @Test
public void compareBatchNormBackward() throws Exception { public void compareBatchNormBackward() throws Exception {
assumeTrue(Nd4j.getBackend().getClass().getName().toLowerCase().contains("native"));
Nd4j.getRandom().setSeed(12345); Nd4j.getRandom().setSeed(12345);
INDArray in = Nd4j.rand(DataType.FLOAT, 1, 3, 15, 15); 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); 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); assertEquals(outExp, outAct);
} else {
boolean eq = outExp.equalsWithEps(outAct, 0.1);
assertTrue(eq);
}
} }
@Test @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; import java.util.List;
/** /**
* A wrapper for a dataset to sample from. * @deprecated Use {@link org.nd4j.linalg.dataset.api.iterator.SamplingDataSetIterator}
* 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
public class SamplingDataSetIterator extends org.nd4j.linalg.dataset.api.iterator.SamplingDataSetIterator {
public SamplingDataSetIterator(DataSet sampleFrom, int batchSize, int totalNumberSamples) { public SamplingDataSetIterator(DataSet sampleFrom, int batchSize, int totalNumberSamples) {
super(); super(sampleFrom, batchSize, totalNumberSamples);
this.sampleFrom = sampleFrom;
this.batchSize = batchSize;
this.totalNumberSamples = 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 /**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), * <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 * 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 * of each vertex in a graph. Vector representations are learned using walks (usually random walks) on the vertices in
* the graph.<br> * 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; package org.deeplearning4j.nn.modelimport.keras;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.bytedeco.hdf5.*;
import org.bytedeco.javacpp.BytePointer; import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.FloatPointer; import org.bytedeco.javacpp.FloatPointer;
import org.bytedeco.javacpp.Loader; import org.bytedeco.javacpp.Loader;
@ -32,7 +33,6 @@ import java.lang.Exception;
import java.util.ArrayList; import java.util.ArrayList;
import java.util.List; import java.util.List;
import org.bytedeco.hdf5.*;
import static org.bytedeco.hdf5.global.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; package org.deeplearning4j.nn.modelimport.keras;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.IOutputLayer;
import org.deeplearning4j.nn.conf.BackpropType; import org.deeplearning4j.nn.conf.BackpropType;
import org.deeplearning4j.nn.conf.InputPreProcessor; import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration; 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) { 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. * is not currently supported.
* *
* See <a href="https://deeplearning4j.org/docs/latest/keras-import-overview">https://deeplearning4j.org/docs/latest/keras-import-overview</a> * 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 * @author dave@skymind.io
*/ */
@ -41,6 +41,6 @@ public class UnsupportedKerasConfigurationException extends Exception {
} }
private static String appendDocumentationURL(String message) { 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 lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.PReLULayer; import org.deeplearning4j.nn.conf.layers.PReLULayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; 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.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.PReLUParamInitializer; 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.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.linalg.util.ArrayUtil; import org.nd4j.linalg.util.ArrayUtil;
import java.util.HashMap; import java.util.HashMap;
@ -79,14 +77,12 @@ public class KerasPReLU extends KerasLayer {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, ALPHA_CONSTRAINT, conf, kerasMajorVersion); layerConfig, ALPHA_CONSTRAINT, conf, kerasMajorVersion);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, ALPHA_INIT, IWeightInit init = getWeightInitFromConfig(layerConfig, ALPHA_INIT,
enforceTrainingConfig, conf, kerasMajorVersion); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
long[] axes = getSharedAxes(layerConfig); long[] axes = getSharedAxes(layerConfig);
PReLULayer.Builder builder = new PReLULayer.Builder().sharedAxes(axes) PReLULayer.Builder builder = new PReLULayer.Builder().sharedAxes(axes)
.weightInit(weightInit.getWeightInitFunction(distribution)).name(layerName); .weightInit(init).name(layerName);
if (weightConstraint != null){ if (weightConstraint != null){
builder.constrainWeights(weightConstraint); 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; package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
import org.deeplearning4j.nn.api.layers.LayerConstraint; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer; import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.primitives.Pair;
import java.util.Map; import java.util.Map;
@ -83,15 +81,13 @@ public class KerasAtrousConvolution1D extends KerasConvolution {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
Convolution1DLayer.Builder builder = new Convolution1DLayer.Builder().name(this.layerName) Convolution1DLayer.Builder builder = new Convolution1DLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.dilation(getDilationRate(layerConfig, 1, conf, true)[0]) .dilation(getDilationRate(layerConfig, 1, conf, true)[0])
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .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; package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
import org.deeplearning4j.nn.api.layers.LayerConstraint; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer; import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.primitives.Pair;
import java.util.Map; import java.util.Map;
@ -84,14 +82,13 @@ public class KerasAtrousConvolution2D extends KerasConvolution {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
ConvolutionLayer.Builder builder = new ConvolutionLayer.Builder().name(this.layerName) ConvolutionLayer.Builder builder = new ConvolutionLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction()) .weightInit(init)
.dilation(getDilationRate(layerConfig, 2, conf, true)) .dilation(getDilationRate(layerConfig, 2, conf, true))
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .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 lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.ArrayUtils; import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; 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.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer; import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
@ -30,7 +29,6 @@ import org.nd4j.linalg.factory.Nd4j;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; import java.util.Map;
import java.util.Set;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.removeDefaultWeights; 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.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution1DLayer; import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil; 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.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer; 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.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; import java.util.Map;
@ -94,15 +92,13 @@ public class KerasConvolution1D extends KerasConvolution {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
Convolution1DLayer.Builder builder = new Convolution1DLayer.Builder().name(this.layerName) Convolution1DLayer.Builder builder = new Convolution1DLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 1, conf, kerasMajorVersion)[0]) .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 lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer; import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.primitives.Pair;
import java.util.Map; import java.util.Map;
@ -87,10 +85,8 @@ public class KerasConvolution2D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1; numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 2, conf, false); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion); 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) ConvolutionLayer.Builder builder = new ConvolutionLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 2, conf, kerasMajorVersion)) .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 lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Convolution3D; import org.deeplearning4j.nn.conf.layers.Convolution3D;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer; import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.primitives.Pair;
import java.util.Map; import java.util.Map;
@ -88,10 +86,8 @@ public class KerasConvolution3D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1; numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 3, conf, false); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion); 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) Convolution3D.Builder builder = new Convolution3D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 3, conf, kerasMajorVersion)) .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.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.Deconvolution2D; import org.deeplearning4j.nn.conf.layers.Deconvolution2D;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.primitives.Pair;
import java.util.Map; import java.util.Map;
@ -86,10 +84,8 @@ public class KerasDeconvolution2D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1; numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 2, conf, false); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion); 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) Deconvolution2D.Builder builder = new Deconvolution2D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 2, conf, kerasMajorVersion)) .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.extern.slf4j.Slf4j;
import lombok.val; import lombok.val;
import org.deeplearning4j.nn.api.layers.LayerConstraint; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D; import org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; 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.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasRegularizerUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasRegularizerUtils;
import org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer; 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.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.Collections; import java.util.Collections;
import java.util.HashMap; import java.util.HashMap;
@ -126,10 +124,8 @@ public class KerasDepthwiseConvolution2D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1; numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 2, conf, false); 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); conf.getLAYER_FIELD_DEPTH_WISE_INIT(), enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit depthWeightInit = depthWiseInit.getFirst();
Distribution depthDistribution = depthWiseInit.getSecond();
val nIn = getNInFromConfig(previousLayers); val nIn = getNInFromConfig(previousLayers);
@ -152,7 +148,7 @@ public class KerasDepthwiseConvolution2D extends KerasConvolution {
.nIn(nIn) .nIn(nIn)
.nOut(nIn * depthMultiplier) .nOut(nIn * depthMultiplier)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(depthWeightInit.getWeightInitFunction(depthDistribution)) .weightInit(depthWiseInit)
.depthMultiplier(depthMultiplier) .depthMultiplier(depthMultiplier)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .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.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.SeparableConvolution2D; import org.deeplearning4j.nn.conf.layers.SeparableConvolution2D;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; 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.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasRegularizerUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasRegularizerUtils;
import org.deeplearning4j.nn.params.SeparableConvolutionParamInitializer; 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.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; import java.util.Map;
@ -93,17 +91,13 @@ public class KerasSeparableConvolution2D extends KerasConvolution {
int depthMultiplier = getDepthMultiplier(layerConfig, conf); int depthMultiplier = getDepthMultiplier(layerConfig, conf);
Pair<WeightInit, Distribution> depthWiseInit = getWeightInitFromConfig(layerConfig, IWeightInit depthWiseInit = getWeightInitFromConfig(layerConfig,
conf.getLAYER_FIELD_DEPTH_WISE_INIT(), enforceTrainingConfig, conf, kerasMajorVersion); 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); 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) if (enforceTrainingConfig)
throw new UnsupportedKerasConfigurationException( throw new UnsupportedKerasConfigurationException(
"Specifying different initialization for depth- and point-wise weights not supported."); "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) SeparableConvolution2D.Builder builder = new SeparableConvolution2D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(depthWeightInit.getWeightInitFunction(depthDistribution)) .weightInit(depthWiseInit)
.depthMultiplier(depthMultiplier) .depthMultiplier(depthMultiplier)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .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; package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
import org.deeplearning4j.nn.conf.inputs.InputType; 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.conf.layers.Upsampling3D;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;

1
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasZeroPadding3D.java
View File

@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer; 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.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;

10
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDense.java
View File

@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.DenseLayer; import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; 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.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.params.DefaultParamInitializer; 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.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; import java.util.Map;
@ -95,15 +93,13 @@ public class KerasDense extends KerasLayer {
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
DenseLayer.Builder builder = new DenseLayer.Builder().name(this.layerName) DenseLayer.Builder builder = new DenseLayer.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)) .nOut(getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout).activation(getIActivationFromConfig(layerConfig, conf)) .dropOut(this.dropout).activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.biasInit(0.0) .biasInit(0.0)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.hasBias(hasBias); .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;
import org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional; import org.deeplearning4j.nn.conf.inputs.InputType.InputTypeConvolutional;
import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor; 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.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; 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 lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.inputs.InputType; 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.conf.layers.misc.RepeatVector;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration; 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 lombok.val;
import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.nn.conf.InputPreProcessor; import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; 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.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor; import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.nd4j.linalg.util.ArrayUtil;
import java.util.List; import java.util.List;
import java.util.Map; import java.util.Map;

12
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings/KerasEmbedding.java
View File

@ -21,7 +21,6 @@ import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer; import org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; 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.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.DefaultParamInitializer; 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.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; 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." "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 " + + " If you want to have this behaviour for your imported model " +
"in DL4J, apply masking as a pre-processing step to your input." + "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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
Distribution distribution = init.getSecond();
LayerConstraint embeddingConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint embeddingConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_EMBEDDINGS_CONSTRAINT(), conf, kerasMajorVersion); layerConfig, conf.getLAYER_FIELD_EMBEDDINGS_CONSTRAINT(), conf, kerasMajorVersion);
@ -121,7 +117,7 @@ public class KerasEmbedding extends KerasLayer {
.inferInputLength(inferInputLength) .inferInputLength(inferInputLength)
.nOut(getNOutFromConfig(layerConfig, conf)) .nOut(getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout).activation(Activation.IDENTITY) .dropOut(this.dropout).activation(Activation.IDENTITY)
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.biasInit(0.0) .biasInit(0.0)
.l1(this.weightL1Regularization) .l1(this.weightL1Regularization)
.l2(this.weightL2Regularization) .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 lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.LocallyConnected1D; import org.deeplearning4j.nn.conf.layers.LocallyConnected1D;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; 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.layers.convolutional.KerasConvolution;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer; 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.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; import java.util.Map;
@ -90,11 +88,8 @@ public class KerasLocallyConnected1D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1; numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 1, conf, false); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst();
// TODO: take care of distribution and bias init
//Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion); 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) LocallyConnected1D.Builder builder = new LocallyConnected1D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getActivationFromConfig(layerConfig, conf)) .activation(getActivationFromConfig(layerConfig, conf))
.weightInit(weightInit) .weightInit(conf.getKERAS_PARAM_NAME_W(), init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 1, conf, kerasMajorVersion)[0]) .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 lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.LocallyConnected2D; import org.deeplearning4j.nn.conf.layers.LocallyConnected2D;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; 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.layers.convolutional.KerasConvolution;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.params.ConvolutionParamInitializer; 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.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; 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.layers.convolutional.KerasConvolutionUtils.*;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasActivationUtils.getActivationFromConfig; 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.KerasInitilizationUtils.getWeightInitFromConfig;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.getHasBiasFromConfig; import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.*;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.getNOutFromConfig;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.removeDefaultWeights;
/** /**
@ -92,11 +88,9 @@ public class KerasLocallyConnected2D extends KerasConvolution {
numTrainableParams = hasBias ? 2 : 1; numTrainableParams = hasBias ? 2 : 1;
int[] dilationRate = getDilationRate(layerConfig, 2, conf, false); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit weightInit = init.getFirst(); // TODO: take care of bias init
// TODO: take care of distribution and bias init
//Distribution distribution = init.getSecond();
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig( LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion); 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) LocallyConnected2D.Builder builder = new LocallyConnected2D.Builder().name(this.layerName)
.nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout) .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
.activation(getActivationFromConfig(layerConfig, conf)) .activation(getActivationFromConfig(layerConfig, conf))
.weightInit(weightInit) .weightInit(conf.getKERAS_PARAM_NAME_W(), init)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization) .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.convolutionMode(getConvolutionModeFromConfig(layerConfig, conf)) .convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
.kernelSize(getKernelSizeFromConfig(layerConfig, 2, conf, kerasMajorVersion)) .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.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; import java.util.Map;
import java.util.Set; 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 lombok.val;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil; import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
import org.deeplearning4j.nn.conf.layers.LSTM; 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.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.LSTMParamInitializer; 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.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
@ -151,15 +150,11 @@ public class KerasLSTM extends KerasLayer {
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException { throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig); super(layerConfig, enforceTrainingConfig);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(), IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit recurrentWeightInit = recurrentInit.getFirst();
Distribution recurrentDistribution = recurrentInit.getSecond();
boolean hasBias = getHasBiasFromConfig(layerConfig, conf); boolean hasBias = getHasBiasFromConfig(layerConfig, conf);
@ -186,8 +181,8 @@ public class KerasLSTM extends KerasLayer {
.nOut(getNOutFromConfig(layerConfig, conf)) .nOut(getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout) .dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.weightInitRecurrent(recurrentWeightInit.getWeightInitFunction(recurrentDistribution)) .weightInitRecurrent(recurrentInit)
.biasInit(0.0) // TODO: this is incorrect .biasInit(0.0) // TODO: this is incorrect
.l1(this.weightL1Regularization) .l1(this.weightL1Regularization)
.l2(this.weightL2Regularization); .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 lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.api.layers.LayerConstraint; import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor; 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.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil; import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
import org.deeplearning4j.nn.conf.layers.Layer; 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.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.SimpleRnnParamInitializer; 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.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair; import org.nd4j.linalg.primitives.Pair;
@ -124,15 +123,11 @@ public class KerasSimpleRnn extends KerasLayer {
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException { throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig); super(layerConfig, enforceTrainingConfig);
Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(), IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion); 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); enforceTrainingConfig, conf, kerasMajorVersion);
WeightInit recurrentWeightInit = recurrentInit.getFirst();
Distribution recurrentDistribution = recurrentInit.getSecond();
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf); Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
this.returnSequences = (Boolean) innerConfig.get(conf.getLAYER_FIELD_RETURN_SEQUENCES()); this.returnSequences = (Boolean) innerConfig.get(conf.getLAYER_FIELD_RETURN_SEQUENCES());
@ -154,8 +149,8 @@ public class KerasSimpleRnn extends KerasLayer {
.nOut(getNOutFromConfig(layerConfig, conf)) .nOut(getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout) .dropOut(this.dropout)
.activation(getIActivationFromConfig(layerConfig, conf)) .activation(getIActivationFromConfig(layerConfig, conf))
.weightInit(weightInit.getWeightInitFunction(distribution)) .weightInit(init)
.weightInitRecurrent(recurrentWeightInit.getWeightInitFunction(recurrentDistribution)) .weightInitRecurrent(recurrentInit)
.biasInit(0.0) .biasInit(0.0)
.l1(this.weightL1Regularization) .l1(this.weightL1Regularization)
.l2(this.weightL2Regularization); .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 com.google.gson.reflect.TypeToken;
import lombok.Data; import lombok.Data;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; 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.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.INDArrayIndex; import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex; import org.nd4j.linalg.indexing.NDArrayIndex;
@ -31,7 +29,6 @@ import org.nd4j.linalg.primitives.Pair;
import java.io.IOException; import java.io.IOException;
import java.nio.file.Files; import java.nio.file.Files;
import java.nio.file.Paths; import java.nio.file.Paths;
import java.util.HashMap;
import java.util.List; import java.util.List;
import java.util.Map; 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.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor; import org.deeplearning4j.nn.conf.preprocessor.BaseInputPreProcessor;
import org.deeplearning4j.nn.workspace.ArrayType; import org.deeplearning4j.nn.workspace.ArrayType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr; 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; import org.nd4j.shade.jackson.annotation.JsonProperty;
/** /**

4
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/preprocessors/ReshapePreprocessor.java
View File

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

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

@ -20,9 +20,9 @@ import lombok.extern.slf4j.Slf4j;
import lombok.val; import lombok.val;
import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor; import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;
import org.deeplearning4j.nn.workspace.ArrayType; import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape; 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.JsonCreator;
import org.nd4j.shade.jackson.annotation.JsonProperty; import org.nd4j.shade.jackson.annotation.JsonProperty;

13
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/DL4JKerasModelValidator.java
View File

@ -1,28 +1,15 @@
package org.deeplearning4j.nn.modelimport.keras.utils; package org.deeplearning4j.nn.modelimport.keras.utils;
import lombok.NonNull; 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.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive; 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.modelimport.keras.config.KerasModelConfiguration;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.validation.Nd4jCommonValidator; import org.nd4j.validation.Nd4jCommonValidator;
import org.nd4j.validation.ValidationResult; import org.nd4j.validation.ValidationResult;
import java.io.BufferedReader;
import java.io.File; 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.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 * 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
View File

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

85
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasInitilizationUtils.java
View File

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

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

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

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

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

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

@ -22,8 +22,6 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Rule; import org.junit.Rule;
import org.junit.Test; import org.junit.Test;
import org.junit.rules.TemporaryFolder; 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.resources.Resources;
import org.nd4j.validation.ValidationResult; import org.nd4j.validation.ValidationResult;

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

@ -21,7 +21,6 @@ import org.datavec.api.records.reader.SequenceRecordReader;
import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader; import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader;
import org.datavec.api.split.NumberedFileInputSplit; import org.datavec.api.split.NumberedFileInputSplit;
import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator; import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator;
import org.deeplearning4j.nn.layers.recurrent.LSTM; import org.deeplearning4j.nn.layers.recurrent.LSTM;
import org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer; import org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest; 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.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Assert;
import org.junit.Ignore; import org.junit.Ignore;
import org.junit.Rule; import org.junit.Rule;
import org.junit.Test; import org.junit.Test;

1
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/Keras1ModelConfigurationTest.java
View File

@ -24,7 +24,6 @@ import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModel; import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Test; import org.junit.Test;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.InputStream; 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.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.File; import java.io.File;
import java.io.FileNotFoundException;
import java.io.IOException; import java.io.IOException;
import java.io.InputStream; import java.io.InputStream;
import java.util.Arrays; 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.modelimport.keras.layers.core.KerasDense;
import org.deeplearning4j.nn.weights.IWeightInit; import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.nn.weights.WeightInitIdentity;
import org.deeplearning4j.nn.weights.WeightInitVarScalingNormalFanIn;
import org.junit.Test; import org.junit.Test;
import java.util.HashMap; import java.util.HashMap;
@ -94,11 +96,11 @@ public class KerasInitilizationTest extends BaseDL4JTest {
WeightInit.RELU_UNIFORM.getWeightInitFunction(), WeightInit.RELU_UNIFORM.getWeightInitFunction(),
WeightInit.ONES.getWeightInitFunction(), WeightInit.ONES.getWeightInitFunction(),
WeightInit.ZERO.getWeightInitFunction(), WeightInit.ZERO.getWeightInitFunction(),
WeightInit.IDENTITY.getWeightInitFunction(), new WeightInitIdentity(0.2),
WeightInit.DISTRIBUTION.getWeightInitFunction(new NormalDistribution(mean, stdDev)), WeightInit.DISTRIBUTION.getWeightInitFunction(new NormalDistribution(mean, stdDev)),
WeightInit.DISTRIBUTION.getWeightInitFunction(new OrthogonalDistribution(gain)), WeightInit.DISTRIBUTION.getWeightInitFunction(new OrthogonalDistribution(gain)),
WeightInit.DISTRIBUTION.getWeightInitFunction(new ConstantDistribution(value)), WeightInit.DISTRIBUTION.getWeightInitFunction(new ConstantDistribution(value)),
WeightInit.VAR_SCALING_NORMAL_FAN_IN.getWeightInitFunction()}; new WeightInitVarScalingNormalFanIn(0.2)};
} }
private Distribution[] dl4jDistributions() { 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; package org.deeplearning4j.nn.modelimport.keras.configurations;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest; import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModelImport; import org.deeplearning4j.nn.modelimport.keras.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.Test; 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 org.nd4j.resources.Resources;
import java.io.File;
import java.io.IOException; import java.io.IOException;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertNotNull; 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.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.File; 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.gradientcheck.GradientCheckUtil;
import org.deeplearning4j.nn.api.Layer; import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.layers.IOutputLayer; 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.FeedForwardLayer;
import org.deeplearning4j.nn.conf.layers.LossLayer; import org.deeplearning4j.nn.conf.layers.LossLayer;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer; import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.graph.ComputationGraph; import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.recurrent.LSTM; import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.layers.recurrent.LastTimeStepLayer; import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive;
import org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer; import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.*; import org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder; import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelUtils; import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelUtils;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration; import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration;
import org.deeplearning4j.nn.transferlearning.TransferLearning; 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.Ignore;
import org.junit.Rule; import org.junit.Rule;
import org.junit.Test; import org.junit.Test;
@ -47,27 +44,25 @@ import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.activations.Activation; import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation; import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.*; 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.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.linalg.learning.config.NoOp; import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions; import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.File; import java.io.File;
import java.io.FileNotFoundException;
import java.io.IOException; import java.io.IOException;
import java.io.InputStream; import java.io.InputStream;
import java.net.URL; import java.net.URL;
import java.nio.file.Files; import java.nio.file.Files;
import java.nio.file.StandardCopyOption; 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.*;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
/** /**
* Unit tests for end-to-end Keras model import. * 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.graph.ComputationGraph;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest; import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer; 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.KerasModelImport;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasSpaceToDepth; import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasSpaceToDepth;
import org.deeplearning4j.nn.transferlearning.TransferLearning; 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.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler; import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import java.io.File; 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. * 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.Rule;
import org.junit.Test; import org.junit.Test;
import org.junit.rules.TemporaryFolder; import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.File; import java.io.File;

5
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasAtrousConvolution1DTest.java
View File

@ -27,16 +27,11 @@ import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasAtrousC
import org.deeplearning4j.nn.weights.IWeightInit; import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitXavier; import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Test; 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.HashMap;
import java.util.List;
import java.util.Map; import java.util.Map;
import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/** /**
* @author Max Pumperla * @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.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitXavier; import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Test; 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.ArrayList;
import java.util.HashMap; import java.util.HashMap;
@ -39,7 +36,6 @@ import java.util.Map;
import static org.junit.Assert.assertArrayEquals; import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/** /**
* @author Max Pumperla * @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.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasCropping1D;
import org.junit.Test; import org.junit.Test;
import java.util.ArrayList;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; 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; 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.conf.layers.convolutional.Cropping3D;
import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest; import org.deeplearning4j.nn.modelimport.keras.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration; 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.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasCropping3D;
import org.junit.Test; 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.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Test; import org.junit.Test;
import org.nd4j.base.Preconditions; 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 java.util.*;
import static org.junit.Assert.assertArrayEquals; import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/** /**
* @author Max Pumperla * @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; package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.layers.Upsampling1D; 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.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration; 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.KerasUpsampling1D;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasUpsampling2D;
import org.junit.Test; import org.junit.Test;
import java.util.ArrayList;
import java.util.HashMap; import java.util.HashMap;
import java.util.List;
import java.util.Map; import java.util.Map;
import static org.junit.Assert.assertEquals; 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; package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.layers.Upsampling2D; 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.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration; 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.KerasUpsampling2D;
import org.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasZeroPadding1D;
import org.junit.Test; import org.junit.Test;
import java.util.ArrayList; 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; package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
import org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer; 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.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.KerasLayerConfiguration; 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.deeplearning4j.nn.modelimport.keras.layers.convolutional.KerasZeroPadding3D;
import org.junit.Test; 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.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInitXavier; import org.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Test; 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.HashMap;
import java.util.List;
import java.util.Map; import java.util.Map;
import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/** /**
* @author Max Pumperla * @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.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.PermutePreprocessor; import org.deeplearning4j.nn.modelimport.keras.preprocessors.PermutePreprocessor;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor;
import org.junit.Test; 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; 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.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor; import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.junit.Assert; import org.junit.Assert;
import org.junit.Test; import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import java.util.*; 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.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import java.util.ArrayList; import java.util.*;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertEquals; 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.dropout.Dropout;
import org.deeplearning4j.nn.conf.inputs.InputType; import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.LocallyConnected1D; 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.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasTestUtils; import org.deeplearning4j.nn.modelimport.keras.KerasTestUtils;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
@ -31,10 +30,8 @@ import org.junit.Test;
import java.util.ArrayList; import java.util.ArrayList;
import java.util.HashMap; import java.util.HashMap;
import java.util.List;
import java.util.Map; import java.util.Map;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals; 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.modelimport.keras.config.KerasLayerConfiguration;
import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.Test; 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.assertArrayEquals;
import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
/** /**
* @author Max Pumperla * @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.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.PoolingType; import org.deeplearning4j.nn.conf.layers.PoolingType;
import org.deeplearning4j.nn.conf.layers.Subsampling3DLayer; 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.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration; import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
import org.deeplearning4j.nn.modelimport.keras.config.Keras2LayerConfiguration; 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.deeplearning4j.nn.weights.WeightInitXavier;
import org.junit.Assert; import org.junit.Assert;
import org.junit.Test; 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.assertEquals;
import static org.junit.Assert.assertNotNull;
/** /**
* @author Max Pumperla * @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; 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.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.KerasModel; import org.deeplearning4j.nn.modelimport.keras.KerasModel;
import org.deeplearning4j.nn.modelimport.keras.KerasSequentialModel; 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.nn.modelimport.keras.utils.KerasModelBuilder;
import org.deeplearning4j.util.DL4JFileUtils; import org.deeplearning4j.util.DL4JFileUtils;
import org.junit.Test; import org.junit.Test;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.File; import java.io.File;
@ -32,8 +29,6 @@ import java.io.InputStream;
import java.nio.file.Files; import java.nio.file.Files;
import java.nio.file.StandardCopyOption; import java.nio.file.StandardCopyOption;
import static java.io.File.createTempFile;
public class OptimizerImport extends BaseDL4JTest { public class OptimizerImport extends BaseDL4JTest {
@Test @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.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.preprocessing.text.KerasTokenizer;
import org.junit.Test; import org.junit.Test;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.IOException; 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.BaseDL4JTest;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException; import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.junit.Test; import org.junit.Test;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.IOException; import java.io.IOException;
import static org.junit.Assert.assertEquals; import static org.junit.Assert.*;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNull;
import static org.junit.Assert.assertTrue;
/** /**
* Import Keras Tokenizer * 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.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Arrays;
import java.util.HashMap; import java.util.HashMap;
import java.util.Map; 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.junit.rules.TemporaryFolder;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.resources.Resources; import org.nd4j.resources.Resources;
import java.io.File; import java.io.File;

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

@ -77,71 +77,6 @@
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </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> <dependency>
<groupId>com.typesafe.play</groupId> <groupId>com.typesafe.play</groupId>
<artifactId>play-java_2.11</artifactId> <artifactId>play-java_2.11</artifactId>

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

@ -31,21 +31,6 @@
<dependencies> <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> <dependency>
<groupId>com.mashape.unirest</groupId> <groupId>com.mashape.unirest</groupId>
<artifactId>unirest-java</artifactId> <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> * QuadTree: <a href="http://en.wikipedia.org/wiki/Quadtree">http://en.wikipedia.org/wiki/Quadtree</a>
* *
* Reference impl based on the paper by: * 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. * 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 * @param v the number to getFromOrigin the next power of 2 for
* @return the next power of 2 for the passed in value * @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> <artifactId>deeplearning4j-nlp</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </dependency>
<dependency>
<groupId>org.nutz</groupId>
<artifactId>nutz</artifactId>
<version>1.r.58</version>
<scope>provided</scope>
</dependency>
<dependency> <dependency>
<groupId>org.nlpcn</groupId> <groupId>org.nlpcn</groupId>
<artifactId>nlp-lang</artifactId> <artifactId>nlp-lang</artifactId>

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

@ -33,26 +33,6 @@
</properties> </properties>
<dependencies> <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> <dependency>
<groupId>org.cleartk</groupId> <groupId>org.cleartk</groupId>
<artifactId>cleartk-snowball</artifactId> <artifactId>cleartk-snowball</artifactId>

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

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

2
deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/graph/walkers/impl/PopularityWalker.java
View File

@ -42,7 +42,7 @@ import java.util.*;
* Instead of rand walks, this walker produces walks based on number of edges coming into each node. * 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. * 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 * @author raver119@gmail.com
*/ */
public class PopularityWalker<T extends SequenceElement> extends RandomWalker<T> implements GraphWalker<T> { 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
View File

@ -37,7 +37,7 @@ import java.util.concurrent.atomic.AtomicInteger;
/** /**
* This is Random-based walker for SequenceVectors-based DeepWalk implementation * 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 AlexDBlack
* @author raver119@gmail.com * @author raver119@gmail.com

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/GradientNormalization.java
View File

@ -52,7 +52,7 @@ package org.deeplearning4j.nn.conf;
* </ul> * </ul>
* Thus, the l2 norm of the scaled gradients will not exceed the specified threshold, though may be smaller than it<br> * 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>, * 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) * Threshold for clipping can be set in Layer configuration, using gradientNormalizationThreshold(double threshold)
* </p> * </p>
* *

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/distribution/OrthogonalDistribution.java
View File

@ -23,7 +23,7 @@ import org.nd4j.shade.jackson.annotation.JsonProperty;
/** /**
* Orthogonal distribution, with gain parameter.<br> * 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) @EqualsAndHashCode(callSuper = false)

Some files were not shown because too many files have changed in this diff Show More