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Various fixes (#43) 

* #8172 Enable DL4J MKLDNN batch norm backward pass

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

* #8382 INDArray.toString() rank 1 brackets / ambiguity fix

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

* #8308 Fix handful of broken links (inc. some in errors)

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

* Unused dependencies, round 1

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

* Unused dependencies, round 2

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

* Unused dependencies, round 3

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

* Small fix

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

* Uniform distribution TF import fix

Signed-off-by: AlexDBlack <blacka101@gmail.com>
master
Alex Black 2019-11-14 19:38:20 +11:00 committed by GitHub
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commit 47d19908f4
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87 changed files with 259 additions and 1011 deletions
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2
CONTRIBUTING.md
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/mkldnn/ValidateMKLDNN.java
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@ -138,12 +138,14 @@ 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 +156,7 @@ 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 +188,7 @@ 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() {

2
deeplearning4j/deeplearning4j-graph/src/main/java/org/deeplearning4j/graph/models/deepwalk/DeepWalk.java
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@ -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/exceptions/InvalidKerasConfigurationException.java
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@ -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
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@ -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.";
} }
} }

2
deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings/KerasEmbedding.java
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@ -104,7 +104,7 @@ 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(), Pair<WeightInit, Distribution> init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_EMBEDDING_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion); enforceTrainingConfig, conf, kerasMajorVersion);

65
deeplearning4j/deeplearning4j-nearestneighbors-parent/deeplearning4j-nearestneighbor-server/pom.xml
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@ -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
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@ -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
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@ -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
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@ -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)

4
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/BatchNormalization.java
View File

@ -236,7 +236,7 @@ public class BatchNormalization extends FeedForwardLayer {
/** /**
* Epsilon value for batch normalization; small floating point value added to variance (algorithm 1 in <a * Epsilon value for batch normalization; small floating point value added to variance (algorithm 1 in <a
* href="http://arxiv.org/pdf/1502.03167v3.pdf">http://arxiv.org/pdf/1502.03167v3.pdf</a>) to reduce/avoid * href="https://arxiv.org/pdf/1502.03167v3.pdf">https://arxiv.org/pdf/1502.03167v3.pdf</a>) to reduce/avoid
* underflow issues.<br> Default: 1e-5 * underflow issues.<br> Default: 1e-5
*/ */
protected double eps = 1e-5; protected double eps = 1e-5;
@ -365,7 +365,7 @@ public class BatchNormalization extends FeedForwardLayer {
/** /**
* Epsilon value for batch normalization; small floating point value added to variance (algorithm 1 in <a * Epsilon value for batch normalization; small floating point value added to variance (algorithm 1 in <a
* href="http://arxiv.org/pdf/1502.03167v3.pdf">http://arxiv.org/pdf/1502.03167v3.pdf</a>) to reduce/avoid * href="https://arxiv.org/pdf/1502.03167v3.pdf">https://arxiv.org/pdf/1502.03167v3.pdf</a>) to reduce/avoid
* underflow issues.<br> Default: 1e-5 * underflow issues.<br> Default: 1e-5
* *
* @param eps Epsilon values to use * @param eps Epsilon values to use

4
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/serde/BaseNetConfigDeserializer.java
View File

@ -53,8 +53,8 @@ import java.util.Map;
* We deserialize the config using the default deserializer, then handle the new IUpdater (which will be null for * We deserialize the config using the default deserializer, then handle the new IUpdater (which will be null for
* 0.8.0 and earlier configs) if necessary * 0.8.0 and earlier configs) if necessary
* *
* Overall design: <a href="http://stackoverflow.com/questions/18313323/how-do-i-call-the-default-deserializer-from-a-custom-deserializer-in-jackson"> * Overall design: <a href="https://stackoverflow.com/questions/18313323/how-do-i-call-the-default-deserializer-from-a-custom-deserializer-in-jackson">
* http://stackoverflow.com/questions/18313323/how-do-i-call-the-default-deserializer-from-a-custom-deserializer-in-jackson</a> * https://stackoverflow.com/questions/18313323/how-do-i-call-the-default-deserializer-from-a-custom-deserializer-in-jackson</a>
* *
* @author Alex Black * @author Alex Black
*/ */

6
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/mkldnn/MKLDNNBatchNormHelper.java
View File

@ -67,17 +67,17 @@ public class MKLDNNBatchNormHelper implements BatchNormalizationHelper {
INDArray beta, INDArray dGammaView, INDArray dBetaView, double eps, LayerWorkspaceMgr workspaceMgr) { INDArray beta, INDArray dGammaView, INDArray dBetaView, double eps, LayerWorkspaceMgr workspaceMgr) {
if(input.dataType() != DataType.FLOAT) if(input.dataType() != DataType.FLOAT)
return null; //MKL-DNN only supports float return null; //MKL-DNN only supports float
/*
//TODO FIXME - AB 2019/11/01 - https://github.com/eclipse/deeplearning4j/issues/8335 //TODO FIXME - AB 2019/11/01 - https://github.com/eclipse/deeplearning4j/issues/8335
List<INDArray> args = new ArrayList<>(); List<INDArray> args = new ArrayList<>();
args.add(input); args.add(input);
args.add(meanCache); args.add(meanCache);
args.add(varCache); args.add(varCache);
args.add(epsilon);
if(gamma != null) if(gamma != null)
args.add(gamma.reshape(gamma.length())); args.add(gamma.reshape(gamma.length()));
if(beta != null) if(beta != null)
args.add(beta.reshape(beta.length())); args.add(beta.reshape(beta.length()));
args.add(epsilon);
DynamicCustomOp op = DynamicCustomOp.builder("batchnorm_bp") DynamicCustomOp op = DynamicCustomOp.builder("batchnorm_bp")
@ -110,8 +110,6 @@ public class MKLDNNBatchNormHelper implements BatchNormalizationHelper {
g.setGradientFor(BatchNormalizationParamInitializer.BETA, dBetaView); g.setGradientFor(BatchNormalizationParamInitializer.BETA, dBetaView);
return new Pair<>(g, epsAtInput); return new Pair<>(g, epsAtInput);
*/
return null;
} }
@Override @Override

10
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/BatchNormalization.java
View File

@ -49,8 +49,8 @@ import java.util.*;
/** /**
* Batch normalization layer.<br> * Batch normalization layer.<br>
* Rerences:<br> * Rerences:<br>
* <a href="http://arxiv.org/pdf/1502.03167v3.pdf">http://arxiv.org/pdf/1502.03167v3.pdf</a><br> * <a href="https://arxiv.org/pdf/1502.03167v3.pdf">https://arxiv.org/pdf/1502.03167v3.pdf</a><br>
* <a href="http://arxiv.org/pdf/1410.7455v8.pdf">http://arxiv.org/pdf/1410.7455v8.pdf</a><br> * <a href="https://arxiv.org/pdf/1410.7455v8.pdf">https://arxiv.org/pdf/1410.7455v8.pdf</a><br>
* <a href="https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html"> * <a href="https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html">
* https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html</a> * https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html</a>
* *
@ -327,7 +327,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
batchMean = input.mean(0, 2, 3); batchMean = input.mean(0, 2, 3);
batchVar = input.var(false, 0, 2, 3); batchVar = input.var(false, 0, 2, 3);
} else { } else {
// TODO setup BatchNorm for RNN http://arxiv.org/pdf/1510.01378v1.pdf // TODO setup BatchNorm for RNN https://arxiv.org/pdf/1510.01378v1.pdf
throw new IllegalStateException( "The layer prior to BatchNorm in the configuration is not currently supported. " + layerId()); throw new IllegalStateException( "The layer prior to BatchNorm in the configuration is not currently supported. " + layerId());
} }
@ -476,7 +476,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
// xHat = (x-xmean) / sqrt(var + epsilon) // xHat = (x-xmean) / sqrt(var + epsilon)
//Note that for CNNs, mean and variance are calculated per feature map (i.e., per activation) rather than per activation //Note that for CNNs, mean and variance are calculated per feature map (i.e., per activation) rather than per activation
//Pg5 of http://arxiv.org/pdf/1502.03167v3.pdf //Pg5 of https://arxiv.org/pdf/1502.03167v3.pdf
// "For convolutional layers, we additionally want the normalization to obey the convolutional property – so that // "For convolutional layers, we additionally want the normalization to obey the convolutional property – so that
// different elements of the same feature map, at different locations, are normalized in the same way. To achieve // different elements of the same feature map, at different locations, are normalized in the same way. To achieve
// this, we jointly normalize all the activations in a minibatch, over all locations." // this, we jointly normalize all the activations in a minibatch, over all locations."
@ -560,7 +560,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
activations = Nd4j.getExecutioner().exec(new BroadcastAddOp(activations, beta, activations, 1)); activations = Nd4j.getExecutioner().exec(new BroadcastAddOp(activations, beta, activations, 1));
} }
} else { } else {
// TODO setup BatchNorm for RNN http://arxiv.org/pdf/1510.01378v1.pdf // TODO setup BatchNorm for RNN https://arxiv.org/pdf/1510.01378v1.pdf
throw new IllegalStateException( throw new IllegalStateException(
"The layer prior to BatchNorm in the configuration is not currently supported. " "The layer prior to BatchNorm in the configuration is not currently supported. "
+ layerId()); + layerId());

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/GravesBidirectionalLSTM.java
View File

@ -40,7 +40,7 @@ import java.util.Map;
* <a href="http://www.cs.toronto.edu/~graves/phd.pdf">http://www.cs.toronto.edu/~graves/phd.pdf</a> * <a href="http://www.cs.toronto.edu/~graves/phd.pdf">http://www.cs.toronto.edu/~graves/phd.pdf</a>
* See also for full/vectorized equations (and a comparison to other LSTM variants): * See also for full/vectorized equations (and a comparison to other LSTM variants):
* Greff et al. 2015, "LSTM: A Search Space Odyssey", pg11. This is the "vanilla" variant in said paper * Greff et al. 2015, "LSTM: A Search Space Odyssey", pg11. This is the "vanilla" variant in said paper
* <a href="http://arxiv.org/pdf/1503.04069.pdf">http://arxiv.org/pdf/1503.04069.pdf</a> * <a href="https://arxiv.org/pdf/1503.04069.pdf">https://arxiv.org/pdf/1503.04069.pdf</a>
* *
* A high level description of bidirectional LSTM can be found from * A high level description of bidirectional LSTM can be found from
* "Hybrid Speech Recognition with Deep Bidirectional LSTM" * "Hybrid Speech Recognition with Deep Bidirectional LSTM"

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/GravesLSTM.java
View File

@ -34,7 +34,7 @@ import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
* <a href="http://www.cs.toronto.edu/~graves/phd.pdf">http://www.cs.toronto.edu/~graves/phd.pdf</a> * <a href="http://www.cs.toronto.edu/~graves/phd.pdf">http://www.cs.toronto.edu/~graves/phd.pdf</a>
* See also for full/vectorized equations (and a comparison to other LSTM variants): * See also for full/vectorized equations (and a comparison to other LSTM variants):
* Greff et al. 2015, "LSTM: A Search Space Odyssey", pg11. This is the "vanilla" variant in said paper * Greff et al. 2015, "LSTM: A Search Space Odyssey", pg11. This is the "vanilla" variant in said paper
* <a href="http://arxiv.org/pdf/1503.04069.pdf">http://arxiv.org/pdf/1503.04069.pdf</a> * <a href="https://arxiv.org/pdf/1503.04069.pdf">https://arxiv.org/pdf/1503.04069.pdf</a>
* *
* @author Alex Black * @author Alex Black
* @see LSTM LSTM class, for the version without peephole connections * @see LSTM LSTM class, for the version without peephole connections

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/LSTM.java
View File

@ -38,7 +38,7 @@ import org.nd4j.util.OneTimeLogger;
* *
* See also for full/vectorized equations (and a comparison to other LSTM variants): * See also for full/vectorized equations (and a comparison to other LSTM variants):
* Greff et al. 2015, "LSTM: A Search Space Odyssey", pg11. This is the "no peephole" variant in said paper * Greff et al. 2015, "LSTM: A Search Space Odyssey", pg11. This is the "no peephole" variant in said paper
* <a href="http://arxiv.org/pdf/1503.04069.pdf">http://arxiv.org/pdf/1503.04069.pdf</a> * <a href="https://arxiv.org/pdf/1503.04069.pdf">https://arxiv.org/pdf/1503.04069.pdf</a>
* *
* @author Alex Black * @author Alex Black
* @see GravesLSTM GravesLSTM class, for the version with peephole connections * @see GravesLSTM GravesLSTM class, for the version with peephole connections

2
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/LSTMHelpers.java
View File

@ -68,7 +68,7 @@ import static org.nd4j.linalg.indexing.NDArrayIndex.*;
* <p> * <p>
* When 'hasPeepholeConnections' is true, this is the "vanilla" variant in said paper<br> * When 'hasPeepholeConnections' is true, this is the "vanilla" variant in said paper<br>
* When 'hasPeepholeConnections' is false, this is the "no peephole" variant<br> * When 'hasPeepholeConnections' is false, this is the "no peephole" variant<br>
* <a href="http://arxiv.org/pdf/1503.04069.pdf">http://arxiv.org/pdf/1503.04069.pdf</a> * <a href="https://arxiv.org/pdf/1503.04069.pdf">https://arxiv.org/pdf/1503.04069.pdf</a>
* *
* *
* @author Alex Black (LSTM implementations) * @author Alex Black (LSTM implementations)

180
deeplearning4j/deeplearning4j-scaleout/deeplearning4j-aws/pom.xml
View File

@ -44,184 +44,48 @@
</properties> </properties>
<dependencies> <dependencies>
<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>commons-logging</groupId>
<artifactId>commons-logging</artifactId>
<version>${commons-logging.version}</version>
</dependency>
<dependency>
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>${jodatime.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> <dependency>
<groupId>com.amazonaws</groupId> <groupId>com.amazonaws</groupId>
<artifactId>aws-java-sdk</artifactId> <artifactId>aws-java-sdk</artifactId>
<version>1.11.24</version> <version>1.11.24</version>
</dependency> </dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-core</artifactId>
<version>${project.parent.version}</version>
</dependency>
<dependency> <dependency>
<groupId>args4j</groupId> <groupId>args4j</groupId>
<artifactId>args4j</artifactId> <artifactId>args4j</artifactId>
<version>2.32</version> <version>2.32</version>
</dependency> </dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-api</artifactId>
<version>${nd4j.version}</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-util</artifactId>
<version>${project.version}</version>
</dependency>
<dependency> <dependency>
<groupId>com.jcraft</groupId> <groupId>com.jcraft</groupId>
<artifactId>jsch</artifactId> <artifactId>jsch</artifactId>
<version>${jsch.version}</version> <version>${jsch.version}</version>
</dependency> </dependency>
<dependency>
<groupId>com.google.inject</groupId>
<artifactId>guice</artifactId>
<version>${guice.version}</version>
</dependency>
<dependency>
<groupId>com.google.protobuf</groupId>
<artifactId>protobuf-java</artifactId>
<version>${google.protobuf.version}</version>
</dependency>
<dependency>
<groupId>commons-codec</groupId>
<artifactId>commons-codec</artifactId>
<version>${commons-codec.version}</version>
</dependency>
<dependency>
<groupId>commons-collections</groupId>
<artifactId>commons-collections</artifactId>
<version>${commons-collections.version}</version>
</dependency>
<dependency>
<groupId>commons-io</groupId>
<artifactId>commons-io</artifactId>
<version>${commons-io.version}</version>
</dependency>
<dependency>
<groupId>commons-lang</groupId>
<artifactId>commons-lang</artifactId>
<version>${commons-lang.version}</version>
</dependency>
<dependency>
<groupId>commons-net</groupId>
<artifactId>commons-net</artifactId>
<version>${commons-net.version}</version>
</dependency>
<dependency>
<groupId>com.sun.xml.bind</groupId>
<artifactId>jaxb-core</artifactId>
<version>${jaxb.version}</version>
</dependency>
<dependency>
<groupId>com.sun.xml.bind</groupId>
<artifactId>jaxb-impl</artifactId>
<version>${jaxb.version}</version>
</dependency>
<dependency>
<groupId>io.netty</groupId>
<artifactId>netty</artifactId>
<version>${netty.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>${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>javax.servlet</groupId>
<artifactId>javax.servlet-api</artifactId>
<version>${servlet.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-compress</artifactId>
<version>${commons-compress.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>${commons-lang3.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-math3</artifactId>
<version>${commons-math3.version}</version>
</dependency>
<dependency>
<groupId>org.apache.curator</groupId>
<artifactId>curator-recipes</artifactId>
<version>${curator.version}</version>
</dependency>
<dependency>
<groupId>com.typesafe</groupId>
<artifactId>config</artifactId>
<version>${typesafe.config.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
<exclusions>
<exclusion>
<groupId>com.google.code.findbugs</groupId>
<artifactId>jsr305</artifactId>
</exclusion>
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>jul-to-slf4j</artifactId>
</exclusion>
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>jcl-over-slf4j</artifactId>
</exclusion>
<exclusion>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
</exclusion>
<exclusion>
<groupId>log4j</groupId>
<artifactId>log4j</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency> <dependency>
<groupId>org.threadly</groupId> <groupId>org.threadly</groupId>
<artifactId>threadly</artifactId> <artifactId>threadly</artifactId>
<version>${threadly.version}</version> <version>${threadly.version}</version>
</dependency> </dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>${commons-lang3.version}</version>
</dependency>
</dependencies> </dependencies>
<profiles> <profiles>

8
deeplearning4j/deeplearning4j-scaleout/deeplearning4j-aws/src/main/java/org/deeplearning4j/aws/emr/SparkEMRClient.java
View File

@ -27,8 +27,8 @@ import lombok.AllArgsConstructor;
import lombok.Data; import lombok.Data;
import lombok.NoArgsConstructor; import lombok.NoArgsConstructor;
import lombok.extern.slf4j.Slf4j; import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang.RandomStringUtils; import org.apache.commons.lang3.RandomStringUtils;
import org.apache.spark.api.java.function.Function; import org.nd4j.linalg.function.Function;
import java.io.File; import java.io.File;
import java.util.*; import java.util.*;
@ -157,7 +157,7 @@ public class SparkEMRClient {
private void submitJob(AmazonElasticMapReduce emr, String mainClass, List<String> args, Map<String, String> sparkConfs, File uberJar) throws Exception { private void submitJob(AmazonElasticMapReduce emr, String mainClass, List<String> args, Map<String, String> sparkConfs, File uberJar) throws Exception {
AmazonS3URI s3Jar = new AmazonS3URI(sparkS3JarFolder + "/" + uberJar.getName()); AmazonS3URI s3Jar = new AmazonS3URI(sparkS3JarFolder + "/" + uberJar.getName());
log.info(String.format("Placing uberJar %s to %s", uberJar.getPath(), s3Jar.toString())); log.info(String.format("Placing uberJar %s to %s", uberJar.getPath(), s3Jar.toString()));
PutObjectRequest putRequest = sparkS3PutObjectDecorator.call( PutObjectRequest putRequest = sparkS3PutObjectDecorator.apply(
new PutObjectRequest(s3Jar.getBucket(), s3Jar.getKey(), uberJar) new PutObjectRequest(s3Jar.getBucket(), s3Jar.getKey(), uberJar)
); );
sparkS3ClientBuilder.build().putObject(putRequest); sparkS3ClientBuilder.build().putObject(putRequest);
@ -289,7 +289,7 @@ public class SparkEMRClient {
// This should allow the user to decorate the put call to add metadata to the jar put command, such as security groups, // This should allow the user to decorate the put call to add metadata to the jar put command, such as security groups,
protected Function<PutObjectRequest, PutObjectRequest> sparkS3PutObjectDecorator = new Function<PutObjectRequest, PutObjectRequest>() { protected Function<PutObjectRequest, PutObjectRequest> sparkS3PutObjectDecorator = new Function<PutObjectRequest, PutObjectRequest>() {
@Override @Override
public PutObjectRequest call(PutObjectRequest putObjectRequest) throws Exception { public PutObjectRequest apply(PutObjectRequest putObjectRequest) {
return putObjectRequest; return putObjectRequest;
} }
}; };

79
deeplearning4j/deeplearning4j-scaleout/spark/pom.xml
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@ -116,7 +116,6 @@
</build> </build>
<dependencies> <dependencies>
<!-- ND4J Shaded Jackson Dependency --> <!-- ND4J Shaded Jackson Dependency -->
<dependency> <dependency>
<groupId>org.nd4j</groupId> <groupId>org.nd4j</groupId>
@ -139,82 +138,6 @@
<artifactId>scala-reflect</artifactId> <artifactId>scala-reflect</artifactId>
<version>${scala.version}</version> <version>${scala.version}</version>
</dependency> </dependency>
<dependency>
<groupId>com.google.inject</groupId>
<artifactId>guice</artifactId>
<version>${guice.version}</version>
</dependency>
<dependency>
<groupId>com.google.protobuf</groupId>
<artifactId>protobuf-java</artifactId>
<version>${google.protobuf.version}</version>
</dependency>
<dependency>
<groupId>commons-codec</groupId>
<artifactId>commons-codec</artifactId>
<version>${commons-codec.version}</version>
</dependency>
<dependency>
<groupId>commons-collections</groupId>
<artifactId>commons-collections</artifactId>
<version>${commons-collections.version}</version>
</dependency>
<dependency>
<groupId>commons-io</groupId>
<artifactId>commons-io</artifactId>
<version>${commons-io.version}</version>
</dependency>
<dependency>
<groupId>commons-lang</groupId>
<artifactId>commons-lang</artifactId>
<version>${commons-lang.version}</version>
</dependency>
<dependency>
<groupId>commons-net</groupId>
<artifactId>commons-net</artifactId>
<version>${commons-net.version}</version>
</dependency>
<dependency>
<groupId>com.sun.xml.bind</groupId>
<artifactId>jaxb-core</artifactId>
<version>${jaxb.version}</version>
</dependency>
<dependency>
<groupId>com.sun.xml.bind</groupId>
<artifactId>jaxb-impl</artifactId>
<version>${jaxb.version}</version>
</dependency>
<dependency>
<groupId>io.netty</groupId>
<artifactId>netty</artifactId>
<version>${netty.version}</version>
</dependency>
<dependency>
<groupId>javax.servlet</groupId>
<artifactId>javax.servlet-api</artifactId>
<version>${servlet.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-compress</artifactId>
<version>${commons-compress.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>${commons-lang3.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-math3</artifactId>
<version>${commons-math3.version}</version>
</dependency>
<dependency>
<groupId>org.apache.curator</groupId>
<artifactId>curator-recipes</artifactId>
<version>${curator.version}</version>
</dependency>
<dependency> <dependency>
<groupId>com.typesafe</groupId> <groupId>com.typesafe</groupId>
<artifactId>config</artifactId> <artifactId>config</artifactId>
@ -250,9 +173,7 @@
<artifactId>log4j</artifactId> <artifactId>log4j</artifactId>
</exclusion> </exclusion>
</exclusions> </exclusions>
</dependency> </dependency>
</dependencies> </dependencies>
<profiles> <profiles>

26
deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-play/pom.xml
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@ -129,32 +129,11 @@
<artifactId>deeplearning4j-ui-model</artifactId> <artifactId>deeplearning4j-ui-model</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </dependency>
<dependency>
<groupId>com.google.protobuf</groupId>
<artifactId>protobuf-java</artifactId>
<version>${google.protobuf.version}</version>
</dependency>
<dependency> <dependency>
<groupId>javax.ws.rs</groupId> <groupId>javax.ws.rs</groupId>
<artifactId>javax.ws.rs-api</artifactId> <artifactId>javax.ws.rs-api</artifactId>
<version>${ws.rs.version}</version> <version>${ws.rs.version}</version>
</dependency> </dependency>
<dependency>
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>${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> <dependency>
<groupId>org.scala-lang</groupId> <groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId> <artifactId>scala-library</artifactId>
@ -165,11 +144,6 @@
<artifactId>scala-reflect</artifactId> <artifactId>scala-reflect</artifactId>
<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>

4
docs/deeplearning4j-nlp/templates/word2vec.md
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@ -447,7 +447,7 @@ Marketers might seek to establish relationships among products to build a recomm
### <a name="patent">Google's Word2vec Patent</a> ### <a name="patent">Google's Word2vec Patent</a>
Word2vec is [a method of computing vector representations of words](http://arxiv.org/pdf/1301.3781.pdf) introduced by a team of researchers at Google led by Tomas Mikolov. Google [hosts an open-source version of Word2vec](https://code.google.com/p/word2vec/) released under an Apache 2.0 license. In 2014, Mikolov left Google for Facebook, and in May 2015, [Google was granted a patent for the method](http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=1&f=G&l=50&co1=AND&d=PTXT&s1=9037464&OS=9037464&RS=9037464), which does not abrogate the Apache license under which it has been released. Word2vec is [a method of computing vector representations of words](https://arxiv.org/pdf/1301.3781.pdf) introduced by a team of researchers at Google led by Tomas Mikolov. Google [hosts an open-source version of Word2vec](https://code.google.com/p/word2vec/) released under an Apache 2.0 license. In 2014, Mikolov left Google for Facebook, and in May 2015, [Google was granted a patent for the method](http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=1&f=G&l=50&co1=AND&d=PTXT&s1=9037464&OS=9037464&RS=9037464), which does not abrogate the Apache license under which it has been released.
### <a name="foreign">Foreign Languages</a> ### <a name="foreign">Foreign Languages</a>
@ -485,7 +485,7 @@ Deeplearning4j has a class called [SequenceVectors](https://github.com/eclipse/d
* [Quora: What Are Some Interesting Word2Vec Results?](http://www.quora.com/Word2vec/What-are-some-interesting-Word2Vec-results/answer/Omer-Levy) * [Quora: What Are Some Interesting Word2Vec Results?](http://www.quora.com/Word2vec/What-are-some-interesting-Word2Vec-results/answer/Omer-Levy)
* [Word2Vec: an introduction](http://www.folgertkarsdorp.nl/word2vec-an-introduction/); Folgert Karsdorp * [Word2Vec: an introduction](http://www.folgertkarsdorp.nl/word2vec-an-introduction/); Folgert Karsdorp
* [Mikolov's Original Word2vec Code @Google](https://code.google.com/p/word2vec/) * [Mikolov's Original Word2vec Code @Google](https://code.google.com/p/word2vec/)
* [word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method](http://arxiv.org/pdf/1402.3722v1.pdf); Yoav Goldberg and Omer Levy * [word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method](https://arxiv.org/pdf/1402.3722v1.pdf); Yoav Goldberg and Omer Levy
* [Advances in Pre-Training Distributed Word Representations - by Mikolov et al](https://arxiv.org/abs/1712.09405) * [Advances in Pre-Training Distributed Word Representations - by Mikolov et al](https://arxiv.org/abs/1712.09405)

6
docs/deeplearning4j-nn/templates/computationgraph.md
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@ -51,10 +51,10 @@ Examples of some architectures that can be built using ComputationGraph include:
- Multi-task learning architectures - Multi-task learning architectures
- Recurrent neural networks with skip connections - Recurrent neural networks with skip connections
- [GoogLeNet](http://arxiv.org/abs/1409.4842), a complex type of convolutional netural network for image classification - [GoogLeNet](https://arxiv.org/abs/1409.4842), a complex type of convolutional netural network for image classification
- [Image caption generation](http://arxiv.org/abs/1411.4555) - [Image caption generation](https://arxiv.org/abs/1411.4555)
- [Convolutional networks for sentence classification](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/sentenceclassification/CnnSentenceClassificationExample.java) - [Convolutional networks for sentence classification](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/sentenceclassification/CnnSentenceClassificationExample.java)
- [Residual learning convolutional neural networks](http://arxiv.org/abs/1512.03385) - [Residual learning convolutional neural networks](https://arxiv.org/abs/1512.03385)
## <a name="config">Configuring a Computation Graph</a> ## <a name="config">Configuring a Computation Graph</a>

2
docs/deeplearning4j-nn/templates/model-persistence.md
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@ -8,7 +8,7 @@ weight: 10
## Saving and Loading a Neural Network ## Saving and Loading a Neural Network
The `ModelSerializer` is a class which handles loading and saving models. There are two methods for saving models shown in the examples through the link. The first example saves a normal multilayer network, the second one saves a [computation graph](https://deeplearning4j.org/compgraph). The `ModelSerializer` is a class which handles loading and saving models. There are two methods for saving models shown in the examples through the link. The first example saves a normal multilayer network, the second one saves a [computation graph](https://deeplearning4j.org/docs/latest/deeplearning4j-nn-computationgraph).
Here is a [basic example](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving) with code to save a computation graph using the `ModelSerializer` class, as well as an example of using ModelSerializer to save a neural net built using MultiLayer configuration. Here is a [basic example](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving) with code to save a computation graph using the `ModelSerializer` class, as well as an example of using ModelSerializer to save a neural net built using MultiLayer configuration.

2
docs/deeplearning4j-nn/templates/recurrent.md
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@ -29,7 +29,7 @@ DL4J currently supports the following types of recurrent neural network
* BaseRecurrent * BaseRecurrent
Java documentation for each is available, [GravesLSTM](https://deeplearning4j.org/api/{{page.version}}/org/deeplearning4j/nn/conf/layers/GravesLSTM.html), Java documentation for each is available, [GravesLSTM](https://deeplearning4j.org/api/{{page.version}}/org/deeplearning4j/nn/conf/layers/GravesLSTM.html),
[BidirectionalGravesLSTM](https://deeplearning4j.org/api/{{page.version}}/org/deeplearning4j/nn/conf/layers/GravesBidirectionalLSTM.html), [BaseRecurrent](https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/layers/BaseRecurrentLayer.html) [BidirectionalGravesLSTM](https://deeplearning4j.org/api/{{page.version}}/org/deeplearning4j/nn/conf/layers/GravesBidirectionalLSTM.html), [BaseRecurrent](https://deeplearning4j.org/api/latest/org/deeplearning4j/nn/conf/layers/BaseRecurrentLayer.html)
#### Data for RNNs #### Data for RNNs
Consider for the moment a standard feed-forward network (a multi-layer perceptron or 'DenseLayer' in DL4J). These networks expect input and output data that is two-dimensional: that is, data with "shape" [numExamples,inputSize]. This means that the data into a feed-forward network has ‘numExamples’ rows/examples, where each row consists of ‘inputSize’ columns. A single example would have shape [1,inputSize], though in practice we generally use multiple examples for computational and optimization efficiency. Similarly, output data for a standard feed-forward network is also two dimensional, with shape [numExamples,outputSize]. Consider for the moment a standard feed-forward network (a multi-layer perceptron or 'DenseLayer' in DL4J). These networks expect input and output data that is two-dimensional: that is, data with "shape" [numExamples,inputSize]. This means that the data into a feed-forward network has ‘numExamples’ rows/examples, where each row consists of ‘inputSize’ columns. A single example would have shape [1,inputSize], though in practice we generally use multiple examples for computational and optimization efficiency. Similarly, output data for a standard feed-forward network is also two dimensional, with shape [numExamples,outputSize].

2
docs/deeplearning4j-nn/templates/tsne-visualization.md
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@ -8,7 +8,7 @@ weight: 10
## t-SNE's Data Visualization ## t-SNE's Data Visualization
[t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html) (t-SNE) is a data-visualization tool created by Laurens van der Maaten at Delft University of Technology. [t-Distributed Stochastic Neighbor Embedding](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) (t-SNE) is a data-visualization tool created by Laurens van der Maaten at Delft University of Technology.
While it can be used for any data, t-SNE (pronounced Tee-Snee) is only really meaningful with labeled data, which clarify how the input is clustering. Below, you can see the kind of graphic you can generate in DL4J with t-SNE working on MNIST data. While it can be used for any data, t-SNE (pronounced Tee-Snee) is only really meaningful with labeled data, which clarify how the input is clustering. Below, you can see the kind of graphic you can generate in DL4J with t-SNE working on MNIST data.

4
docs/deeplearning4j-scaleout/templates/howto.md
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@ -627,7 +627,7 @@ To use the system clock time source, add the following to Spark submit:
## <a name="ubuntu16">Failed training on Ubuntu 16.04 (Ubuntu bug that may affect DL4J users)</a> ## <a name="ubuntu16">Failed training on Ubuntu 16.04 (Ubuntu bug that may affect DL4J users)</a>
When running a Spark on YARN cluster on Ubuntu 16.04 machines, chances are that after finishing a job, all processes owned by the user running Hadoop/YARN are killed. This is related to a bug in Ubuntu, which is documented at https://bugs.launchpad.net/ubuntu/+source/procps/+bug/1610499. There's also a Stackoverflow discussion about it at http://stackoverflow.com/questions/38419078/logouts-while-running-hadoop-under-ubuntu-16-04. When running a Spark on YARN cluster on Ubuntu 16.04 machines, chances are that after finishing a job, all processes owned by the user running Hadoop/YARN are killed. This is related to a bug in Ubuntu, which is documented at https://bugs.launchpad.net/ubuntu/+source/procps/+bug/1610499. There's also a Stackoverflow discussion about it at https://stackoverflow.com/questions/38419078/logouts-while-running-hadoop-under-ubuntu-16-04.
Some workarounds are suggested. Some workarounds are suggested.
@ -695,7 +695,7 @@ To use the system clock time source, add the following to Spark submit:
## <a href="ubuntu16">Failed training on Ubuntu 16.04 (Ubuntu bug that may affect DL4J users)</a> ## <a href="ubuntu16">Failed training on Ubuntu 16.04 (Ubuntu bug that may affect DL4J users)</a>
When running a Spark on YARN cluster on Ubuntu 16.04 machines, chances are that after finishing a job, all processes owned by the user running Hadoop/YARN are killed. This is related to a bug in Ubuntu, which is documented at https://bugs.launchpad.net/ubuntu/+source/procps/+bug/1610499. There's also a Stackoverflow discussion about it at http://stackoverflow.com/questions/38419078/logouts-while-running-hadoop-under-ubuntu-16-04. When running a Spark on YARN cluster on Ubuntu 16.04 machines, chances are that after finishing a job, all processes owned by the user running Hadoop/YARN are killed. This is related to a bug in Ubuntu, which is documented at https://bugs.launchpad.net/ubuntu/+source/procps/+bug/1610499. There's also a Stackoverflow discussion about it at https://stackoverflow.com/questions/38419078/logouts-while-running-hadoop-under-ubuntu-16-04.
Some workarounds are suggested. Some workarounds are suggested.

2
docs/deeplearning4j/templates/beginners.md
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@ -99,4 +99,4 @@ You can also download a [free version of the Skymind Intelligence Layer](https:/
Most of what we know about deep learning is contained in academic papers. You can find some of the major research groups [here](https://skymind.ai/wiki/machine-learning-research-groups-labs). Most of what we know about deep learning is contained in academic papers. You can find some of the major research groups [here](https://skymind.ai/wiki/machine-learning-research-groups-labs).
While individual courses have limits on what they can teach, the Internet does not. Most math and programming questions can be answered by Googling and searching sites like [Stackoverflow](http://stackoverflow.com) and [Math Stackexchange](https://math.stackexchange.com/). While individual courses have limits on what they can teach, the Internet does not. Most math and programming questions can be answered by Googling and searching sites like [Stackoverflow](https://stackoverflow.com) and [Math Stackexchange](https://math.stackexchange.com/).

4
docs/deeplearning4j/templates/cheat-sheet.md
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@ -220,7 +220,7 @@ List of supported activation functions:
* **LEAKYRELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationLReLU.java)) - leaky rectified linear unit. ```f(x) = max(0, x) + alpha * min(0, x)``` with ```alpha=0.01``` by default. * **LEAKYRELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationLReLU.java)) - leaky rectified linear unit. ```f(x) = max(0, x) + alpha * min(0, x)``` with ```alpha=0.01``` by default.
* **RATIONALTANH** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRationalTanh.java)) - ```tanh(y) ~ sgn(y) * { 1 - 1/(1+|y|+y^2+1.41645*y^4)}``` which approximates ```f(x) = 1.7159 * tanh(2x/3)```, but should be faster to execute. ([Reference](https://arxiv.org/abs/1508.01292)) * **RATIONALTANH** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRationalTanh.java)) - ```tanh(y) ~ sgn(y) * { 1 - 1/(1+|y|+y^2+1.41645*y^4)}``` which approximates ```f(x) = 1.7159 * tanh(2x/3)```, but should be faster to execute. ([Reference](https://arxiv.org/abs/1508.01292))
* **RELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationReLU.java)) - standard rectified linear unit: ```f(x) = x``` if ```x>0``` or ```f(x) = 0``` otherwise * **RELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationReLU.java)) - standard rectified linear unit: ```f(x) = x``` if ```x>0``` or ```f(x) = 0``` otherwise
* **RRELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRReLU.java)) - randomized rectified linear unit. Deterministic during test time. ([Reference](http://arxiv.org/abs/1505.00853)) * **RRELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRReLU.java)) - randomized rectified linear unit. Deterministic during test time. ([Reference](https://arxiv.org/abs/1505.00853))
* **SIGMOID** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSigmoid.java)) - standard sigmoid activation function, ```f(x) = 1 / (1 + exp(-x))``` * **SIGMOID** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSigmoid.java)) - standard sigmoid activation function, ```f(x) = 1 / (1 + exp(-x))```
* **SOFTMAX** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftmax.java)) - standard softmax activation function * **SOFTMAX** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftmax.java)) - standard softmax activation function
* **SOFTPLUS** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftPlus.java)) - ```f(x) = log(1+e^x)``` - shape is similar to a smooth version of the RELU activation function * **SOFTPLUS** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftPlus.java)) - ```f(x) = log(1+e^x)``` - shape is similar to a smooth version of the RELU activation function
@ -269,7 +269,7 @@ The [CS231n course notes](http://cs231n.github.io/neural-networks-3/#ada) have a
Supported updaters in Deeplearning4j: Supported updaters in Deeplearning4j:
* **AdaDelta** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaDelta.java)) - [Reference](https://arxiv.org/abs/1212.5701) * **AdaDelta** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaDelta.java)) - [Reference](https://arxiv.org/abs/1212.5701)
* **AdaGrad** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaGrad.java)) - [Reference](http://jmlr.org/papers/v12/duchi11a.html) * **AdaGrad** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaGrad.java)) - [Reference](http://jmlr.org/papers/v12/duchi11a.html)
* **AdaMax** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaMax.java)) - A variant of the Adam updater - [Reference](http://arxiv.org/abs/1412.6980) * **AdaMax** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaMax.java)) - A variant of the Adam updater - [Reference](https://arxiv.org/abs/1412.6980)
* **Adam** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Adam.java)) * **Adam** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Adam.java))
* **Nadam** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Nadam.java)) - A variant of the Adam updater, using the Nesterov mementum update rule - [Reference](https://arxiv.org/abs/1609.04747) * **Nadam** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Nadam.java)) - A variant of the Adam updater, using the Nesterov mementum update rule - [Reference](https://arxiv.org/abs/1609.04747)
* **Nesterovs** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Nesterovs.java)) - Nesterov momentum updater * **Nesterovs** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Nesterovs.java)) - Nesterov momentum updater

2
docs/deeplearning4j/templates/config-performance-debugging.md
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@ -84,7 +84,7 @@ Not all DL4J layer types are supported in cuDNN. DL4J layers with cuDNN support
To check if cuDNN is being used, the simplest approach is to look at the log output when running inference or training: To check if cuDNN is being used, the simplest approach is to look at the log output when running inference or training:
If cuDNN is NOT available when you are using a layer that supports it, you will see a message such as: If cuDNN is NOT available when you are using a layer that supports it, you will see a message such as:
``` ```
o.d.n.l.c.ConvolutionLayer - cuDNN not found: use cuDNN for better GPU performance by including the deeplearning4j-cuda module. For more information, please refer to: https://deeplearning4j.org/cudnn o.d.n.l.c.ConvolutionLayer - cuDNN not found: use cuDNN for better GPU performance by including the deeplearning4j-cuda module. For more information, please refer to: https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn
java.lang.ClassNotFoundException: org.deeplearning4j.nn.layers.convolution.CudnnConvolutionHelper java.lang.ClassNotFoundException: org.deeplearning4j.nn.layers.convolution.CudnnConvolutionHelper
at java.net.URLClassLoader.findClass(URLClassLoader.java:381) at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424) at java.lang.ClassLoader.loadClass(ClassLoader.java:424)

52
docs/deeplearning4j/templates/examples-tour.md
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@ -18,31 +18,31 @@ Most of the examples make use of DataVec, a toolkit for preprocessing and clearn
This example takes the canonical Iris dataset of the flower species of the same name, whose relevant measurements are sepal length, sepal width, petal length and petal width. It builds a Spark RDD from the relatively small dataset and runs an analysis against it. This example takes the canonical Iris dataset of the flower species of the same name, whose relevant measurements are sepal length, sepal width, petal length and petal width. It builds a Spark RDD from the relatively small dataset and runs an analysis against it.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/analysis/IrisAnalysis.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/analysis/IrisAnalysis.java)
### BasicDataVecExample.java ### BasicDataVecExample.java
This example loads data into a Spark RDD. All DataVec transform operations use Spark RDDs. Here, we use DataVec to filter data, apply time transformations and remove columns. This example loads data into a Spark RDD. All DataVec transform operations use Spark RDDs. Here, we use DataVec to filter data, apply time transformations and remove columns.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/basic/BasicDataVecExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/basic/BasicDataVecExample.java)
### PrintSchemasAtEachStep.java ### PrintSchemasAtEachStep.java
This example shows the print Schema tools that are useful to visualize and to ensure that the code for the transform is behaving as expected. This example shows the print Schema tools that are useful to visualize and to ensure that the code for the transform is behaving as expected.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/debugging/PrintSchemasAtEachStep.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/debugging/PrintSchemasAtEachStep.java)
### JoinExample.java ### JoinExample.java
You may need to join datasets before passing to a neural network. You can do that in DataVec, and this example shows you how. You may need to join datasets before passing to a neural network. You can do that in DataVec, and this example shows you how.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/join/JoinExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/join/JoinExample.java)
### LogDataExample.java ### LogDataExample.java
This is an example of parsing log data using DataVec. The obvious use cases are cybersecurity and customer relationship management. This is an example of parsing log data using DataVec. The obvious use cases are cybersecurity and customer relationship management.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/logdata/LogDataExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/logdata/LogDataExample.java)
### MnistImagePipelineExample.java ### MnistImagePipelineExample.java
@ -50,7 +50,7 @@ This example is from the video below, which demonstrates the ParentPathLabelGene
<iframe width="560" height="315" src="http://www.youtube.com/embed/GLC8CIoHDnI" frameborder="0" allowfullscreen></iframe> <iframe width="560" height="315" src="http://www.youtube.com/embed/GLC8CIoHDnI" frameborder="0" allowfullscreen></iframe>
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataExamples/MnistImagePipelineExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataExamples/MnistImagePipelineExample.java)
### PreprocessNormalizerExample.java ### PreprocessNormalizerExample.java
@ -78,13 +78,13 @@ MNIST is the "Hello World" of deep learning. Simple, straightforward, and focuss
This is a Single Layer Perceptron for recognizing digits. Note that this pulls the images from a binary package containing the dataset, a rather special case for data ingestion. This is a Single Layer Perceptron for recognizing digits. Note that this pulls the images from a binary package containing the dataset, a rather special case for data ingestion.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistSingleLayerExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistSingleLayerExample.java)
### MLPMnistTwoLayerExample.java ### MLPMnistTwoLayerExample.java
A two-layer perceptron for MNIST, showing there is more than one useful network for a given dataset. A two-layer perceptron for MNIST, showing there is more than one useful network for a given dataset.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistTwoLayerExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistTwoLayerExample.java)
### Feedforward Examples ### Feedforward Examples
@ -92,7 +92,7 @@ Data flows through feed-forward neural networks in a single pass from input via
These networks can be used for a wide range of tasks depending on they are configured. Along with image classification over MNIST data, this directory has examples demonstrating regression, classification, and anomoly detection. These networks can be used for a wide range of tasks depending on they are configured. Along with image classification over MNIST data, this directory has examples demonstrating regression, classification, and anomoly detection.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward)
### Convolutional Neural Networks ### Convolutional Neural Networks
@ -102,7 +102,7 @@ Convolutional Neural Networks are mainly used for image recognition, although th
This example can be run using either LeNet or AlexNet. This example can be run using either LeNet or AlexNet.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/AnimalsClassification.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/AnimalsClassification.java)
--- ---
@ -115,7 +115,7 @@ load the model for later training or inference.
This demonstrates saving and loading a network build using the class ComputationGraph. This demonstrates saving and loading a network build using the class ComputationGraph.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving/SaveLoadComputationGraph.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving/SaveLoadComputationGraph.java)
### SaveLoadMultiLayerNetwork.java ### SaveLoadMultiLayerNetwork.java
@ -135,11 +135,11 @@ Do you need to add a Loss Function that is not available or prebuilt yet? Check
### CustomLossExample.java ### CustomLossExample.java
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossExample.java)
### CustomLossL1L2.java ### CustomLossL1L2.java
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossL1L2.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossL1L2.java)
### Custom Layer ### Custom Layer
@ -147,7 +147,7 @@ Do you need to add a layer with features that aren't available in DeepLearning4J
### CustomLayerExample.java ### CustomLayerExample.java
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/customlayers/CustomLayerExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/customlayers/CustomLayerExample.java)
--- ---
@ -159,25 +159,25 @@ Neural Networks for NLP? We have those, too.
Global Vectors for Word Representation are useful for detecting relationships between words. Global Vectors for Word Representation are useful for detecting relationships between words.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/glove/GloVeExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/glove/GloVeExample.java)
### Paragraph Vectors ### Paragraph Vectors
A vectorized representation of words. Described [here](https://cs.stanford.edu/~quocle/paragraph_vector.pdf) A vectorized representation of words. Described [here](https://cs.stanford.edu/~quocle/paragraph_vector.pdf)
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java)
### Sequence Vectors ### Sequence Vectors
One way to represent sentences is as a sequence of words. One way to represent sentences is as a sequence of words.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/sequencevectors/SequenceVectorsTextExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/sequencevectors/SequenceVectorsTextExample.java)
### Word2Vec ### Word2Vec
Described [here](https://deeplearning4j.org/word2vec.html) Described [here](https://deeplearning4j.org/word2vec.html)
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java)
--- ---
@ -185,7 +185,7 @@ Described [here](https://deeplearning4j.org/word2vec.html)
t-Distributed Stochastic Neighbor Embedding (t-SNE) is useful for data visualization. We include an example in the NLP section since word similarity visualization is a common use. t-Distributed Stochastic Neighbor Embedding (t-SNE) is useful for data visualization. We include an example in the NLP section since word similarity visualization is a common use.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/tsne/TSNEStandardExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/tsne/TSNEStandardExample.java)
--- ---
@ -199,19 +199,19 @@ The examples folder for Recurrent Neural Networks has the following:
An RNN learns a string of characters. An RNN learns a string of characters.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/basic/BasicRNNExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/basic/BasicRNNExample.java)
### GravesLSTMCharModellingExample.java ### GravesLSTMCharModellingExample.java
Takes the complete works of Shakespeare as a sequence of characters and Trains a Neural Net to generate "Shakespeare" one character at a time. Takes the complete works of Shakespeare as a sequence of characters and Trains a Neural Net to generate "Shakespeare" one character at a time.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/character/GravesLSTMCharModellingExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/character/GravesLSTMCharModellingExample.java)
### SingleTimestepRegressionExample.java ### SingleTimestepRegressionExample.java
Regression with an LSTM (Long Short Term Memory) Recurrent Neural Network. Regression with an LSTM (Long Short Term Memory) Recurrent Neural Network.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/regression/SingleTimestepRegressionExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/regression/SingleTimestepRegressionExample.java)
### AdditionRNN.java ### AdditionRNN.java
@ -254,13 +254,13 @@ DeepLearning4j supports using a Spark Cluster for network training. Here are the
### MnistMLPExample.java ### MnistMLPExample.java
This is an example of a Multi-Layer Perceptron training on the Mnist data set of handwritten digits. This is an example of a Multi-Layer Perceptron training on the Mnist data set of handwritten digits.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/mlp/MnistMLPExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/mlp/MnistMLPExample.java)
### SparkLSTMCharacterExample.java ### SparkLSTMCharacterExample.java
An LSTM recurrent Network in Spark. An LSTM recurrent Network in Spark.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/rnn/SparkLSTMCharacterExample.java) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/rnn/SparkLSTMCharacterExample.java)
--- ---
@ -274,7 +274,7 @@ The learning algorithms and loss functions are executed as ND4J operations.
This is a directory with examples for creating and manipulating NDArrays. This is a directory with examples for creating and manipulating NDArrays.
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/tree/master/nd4j-examples/src/main/java/org/nd4j/examples) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/tree/master/nd4j-examples/src/main/java/org/nd4j/examples)
--- ---
@ -282,4 +282,4 @@ This is a directory with examples for creating and manipulating NDArrays.
Deep learning algorithms have learned to play Space Invaders and Doom using reinforcement learning. DeepLearning4J/RL4J examples of Reinforcement Learning are available here: Deep learning algorithms have learned to play Space Invaders and Doom using reinforcement learning. DeepLearning4J/RL4J examples of Reinforcement Learning are available here:
[Show me the code](http://github.com/eclipse/deeplearning4j-examples/tree/master/rl4j-examples) [Show me the code](https://github.com/eclipse/deeplearning4j-examples/tree/master/rl4j-examples)

2
docs/deeplearning4j/templates/quickstart.md
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@ -179,7 +179,7 @@ Congratulations! You just trained your first neural network with Deeplearning4j.
**Q:** **SPARK ISSUES** I am running the examples and having issues with the Spark based examples such as distributed training or datavec transform options. **Q:** **SPARK ISSUES** I am running the examples and having issues with the Spark based examples such as distributed training or datavec transform options.
**A:** You may be missing some dependencies that Spark requires. See this [Stack Overflow discussion](http://stackoverflow.com/a/38735202/3892515) for a discussion of potential dependency issues. Windows users may need the winutils.exe from Hadoop. **A:** You may be missing some dependencies that Spark requires. See this [Stack Overflow discussion](https://stackoverflow.com/a/38735202/3892515) for a discussion of potential dependency issues. Windows users may need the winutils.exe from Hadoop.
Download winutils.exe from https://github.com/steveloughran/winutils and put it into the null/bin/winutils.exe (or create a hadoop folder and add that to HADOOP_HOME) Download winutils.exe from https://github.com/steveloughran/winutils and put it into the null/bin/winutils.exe (or create a hadoop folder and add that to HADOOP_HOME)

2
libnd4j/include/helpers/files.h
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@ -16,7 +16,7 @@
// //
// Methods to lookup files in $PATH // Methods to lookup files in $PATH
// adopted from http://stackoverflow.com/questions/2718915/check-if-file-exists-including-on-path // adopted from https://stackoverflow.com/questions/2718915/check-if-file-exists-including-on-path
// //
#ifndef LIBND4J_FILES_H #ifndef LIBND4J_FILES_H

2
libnd4j/include/ops/declarable/headers/nn.h
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@ -137,7 +137,7 @@ namespace nd4j {
#endif #endif
/** /**
* This operation performs batch normalization of layer, it is based on following article http://arxiv.org/abs/1502.03167. * This operation performs batch normalization of layer, it is based on following article https://arxiv.org/abs/1502.03167.
* Expected arguments: * Expected arguments:
* x: input 4D array of shape [bS,iH,iW,iD] (data format = NHWC) or [bS,iD,iH,iW] (data format = NCHW), where * x: input 4D array of shape [bS,iH,iW,iD] (data format = NHWC) or [bS,iD,iH,iW] (data format = NCHW), where
* bS - batch size * bS - batch size

2
libnd4j/include/ops/declarable/helpers/cpu/gru.cpp
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@ -19,7 +19,7 @@
// //
// implementation of gated Recurrent Unit cell // implementation of gated Recurrent Unit cell
// (cf. http://arxiv.org/abs/1406.1078). // (cf. https://arxiv.org/abs/1406.1078).
// Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio // Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
// "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation" // "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation"

2
libnd4j/include/ops/declarable/helpers/cuda/gru.cu
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@ -19,7 +19,7 @@
// //
// implementation of gated Recurrent Unit cell // implementation of gated Recurrent Unit cell
// (cf. http://arxiv.org/abs/1406.1078). // (cf. https://arxiv.org/abs/1406.1078).
// Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio // Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, Yoshua Bengio
// "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation" // "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation"

2
libnd4j/tests_cpu/layers_tests/testinclude.h
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@ -24,7 +24,7 @@
#include <string> #include <string>
#include <op_boilerplate.h> #include <op_boilerplate.h>
//http://stackoverflow.com/questions/228005/alternative-to-itoa-for-converting-integer-to-string-c //https://stackoverflow.com/questions/228005/alternative-to-itoa-for-converting-integer-to-string-c
FORCEINLINE std::string int_array_to_string(Nd4jLong int_array[], Nd4jLong size_of_array) { FORCEINLINE std::string int_array_to_string(Nd4jLong int_array[], Nd4jLong size_of_array) {
std::string returnstring = "["; std::string returnstring = "[";
for (int temp = 0; temp < size_of_array; temp++) { for (int temp = 0; temp < size_of_array; temp++) {

4
nd4j/README.md
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@ -41,12 +41,12 @@ To install ND4J, there are a couple of approaches, and more information can be f
#### Install from Maven Central #### Install from Maven Central
1. Search for nd4j in the [Maven Central Repository](http://mvnrepository.com/search?q=nd4j) to find the available nd4j jars. 1. Search for nd4j in the [Maven Central Repository](https://search.maven.org/search?q=nd4j) to find the available nd4j jars.
2. Include the appropriate dependency in your pom.xml. 2. Include the appropriate dependency in your pom.xml.
#### Clone from the GitHub Repo #### Clone from the GitHub Repo
https://deeplearning4j.org/buildinglocally https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source
## Contribute ## Contribute
1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug. 1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.

22
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/pom.xml
View File

@ -192,12 +192,6 @@
</dependency> </dependency>
<dependency>
<groupId>org.objenesis</groupId>
<artifactId>objenesis</artifactId>
<version>${objenesis.version}</version>
</dependency>
<!-- oshi: Used for collecting system information for system info reporting --> <!-- oshi: Used for collecting system information for system info reporting -->
<dependency> <dependency>
@ -206,22 +200,6 @@
<version>${oshi.version}</version> <version>${oshi.version}</version>
</dependency> </dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<scope>test</scope>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
<scope>test</scope>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-core</artifactId>
<scope>test</scope>
</dependency>
<dependency> <dependency>
<groupId>org.slf4j</groupId> <groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId> <artifactId>slf4j-api</artifactId>

6
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDNN.java
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@ -69,7 +69,7 @@ public class SDNN extends SDOps {
/** /**
* Neural network batch normalization operation.<br> * Neural network batch normalization operation.<br>
* For details, see <a href="http://arxiv.org/abs/1502.03167">http://arxiv.org/abs/1502.03167</a> * For details, see <a href="https://arxiv.org/abs/1502.03167">https://arxiv.org/abs/1502.03167</a>
* *
* @param name Name of the output variable * @param name Name of the output variable
* @param input Input variable. * @param input Input variable.
@ -139,7 +139,7 @@ public class SDNN extends SDOps {
* out = a * (exp(x) - 1) if x <= 0<br> * out = a * (exp(x) - 1) if x <= 0<br>
* with constant a = 1.0 * with constant a = 1.0
* <p> * <p>
* See: <a href="http://arxiv.org/abs/1511.07289">http://arxiv.org/abs/1511.07289</a> * See: <a href="https://arxiv.org/abs/1511.07289">https://arxiv.org/abs/1511.07289</a>
* *
* @param x Input variable * @param x Input variable
* @return Output variable * @return Output variable
@ -154,7 +154,7 @@ public class SDNN extends SDOps {
* out = a * (exp(x) - 1) if x <= 0<br> * out = a * (exp(x) - 1) if x <= 0<br>
* with constant a = 1.0 * with constant a = 1.0
* <p> * <p>
* See: <a href="http://arxiv.org/abs/1511.07289">http://arxiv.org/abs/1511.07289</a> * See: <a href="https://arxiv.org/abs/1511.07289">https://arxiv.org/abs/1511.07289</a>
* *
* @param name Output variable name * @param name Output variable name
* @param x Input variable * @param x Input variable

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRReLU.java
View File

@ -34,7 +34,7 @@ import org.nd4j.shade.jackson.annotation.JsonIgnoreProperties;
* alpha is drawn from uniform(l,u) during training and is set to l+u/2 during test * alpha is drawn from uniform(l,u) during training and is set to l+u/2 during test
* l and u default to 1/8 and 1/3 respectively * l and u default to 1/8 and 1/3 respectively
* *
* <a href="http://arxiv.org/abs/1505.00853"> * <a href="https://arxiv.org/abs/1505.00853">
* Empirical Evaluation of Rectified Activations in Convolutional Network</a> * Empirical Evaluation of Rectified Activations in Convolutional Network</a>
*/ */
@EqualsAndHashCode(callSuper = false) @EqualsAndHashCode(callSuper = false)

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/scalar/LeakyReLU.java
View File

@ -34,7 +34,7 @@ import org.tensorflow.framework.NodeDef;
* Out(x) = x if x >= 0<br> * Out(x) = x if x >= 0<br>
* Leaky ReLU may avoid zero gradient "dying ReLU" problem by having non-zero * Leaky ReLU may avoid zero gradient "dying ReLU" problem by having non-zero
* gradient below 0.<br> * gradient below 0.<br>
* See for example http://arxiv.org/abs/1505.00853 for a comparison of * See for example https://arxiv.org/abs/1505.00853 for a comparison of
* ReLU variants. * ReLU variants.
* *
* @author Alex Black * @author Alex Black

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/MaxOut.java
View File

@ -33,7 +33,7 @@ import java.util.List;
/** /**
* Max out activation: * Max out activation:
* http://arxiv.org/pdf/1302.4389.pdf * https://arxiv.org/pdf/1302.4389.pdf
* *
* @author Adam Gibson * @author Adam Gibson
*/ */

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/strict/ELU.java
View File

@ -32,7 +32,7 @@ import java.util.List;
* Introduced in paper:<br> * Introduced in paper:<br>
* Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)<br> * Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)<br>
* Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter (2015)<br> * Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter (2015)<br>
* <a href="http://arxiv.org/abs/1511.07289">http://arxiv.org/abs/1511.07289</a> * <a href="https://arxiv.org/abs/1511.07289">https://arxiv.org/abs/1511.07289</a>
* *
* @author Alex Black * @author Alex Black
*/ */

1
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/random/custom/DistributionUniform.java
View File

@ -74,6 +74,7 @@ public class DistributionUniform extends DynamicCustomOp {
AttrValue v = attributesForNode.get("dtype"); AttrValue v = attributesForNode.get("dtype");
dataType = TFGraphMapper.convertType(v.getType()); dataType = TFGraphMapper.convertType(v.getType());
addIArgument(dataType.toInt()); addIArgument(dataType.toInt());
addTArgument(0.0, 1.0); //TF version is hardcoded 0 to 1
} }
protected void addArgs() { protected void addArgs() {

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/AdaMaxUpdater.java
View File

@ -32,7 +32,7 @@ import java.util.Map;
/** /**
* The AdaMax updater, a variant of Adam. * The AdaMax updater, a variant of Adam.
* http://arxiv.org/abs/1412.6980 * https://arxiv.org/abs/1412.6980
* *
* @author Justin Long * @author Justin Long
*/ */

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/AdamUpdater.java
View File

@ -30,7 +30,7 @@ import java.util.Map;
/** /**
* The Adam updater. * The Adam updater.
* http://arxiv.org/abs/1412.6980 * https://arxiv.org/abs/1412.6980
* *
* @author Adam Gibson * @author Adam Gibson
*/ */

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaMax.java
View File

@ -28,7 +28,7 @@ import java.util.Map;
/** /**
* The AdaMax updater, a variant of Adam. * The AdaMax updater, a variant of Adam.
* http://arxiv.org/abs/1412.6980 * https://arxiv.org/abs/1412.6980
* *
* @author Justin Long * @author Justin Long
*/ */

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Adam.java
View File

@ -29,7 +29,7 @@ import java.util.Map;
/** /**
* The Adam updater. * The Adam updater.
* http://arxiv.org/abs/1412.6980 * https://arxiv.org/abs/1412.6980
* *
* @author Adam Gibson * @author Adam Gibson
*/ */

24
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/string/NDArrayStrings.java
View File

@ -46,6 +46,9 @@ public class NDArrayStrings {
public static final String EMPTY_ARRAY_STR = "[]"; public static final String EMPTY_ARRAY_STR = "[]";
private static final String[] OPEN_BRACKETS = new String[]{"", "[", "[[", "[[[", "[[[[", "[[[[[", "[[[[[[", "[[[[[[[", "[[[[[[[["};
private static final String[] CLOSE_BRACKETS = new String[]{"", "]", "]]", "]]]", "]]]]", "]]]]]", "]]]]]]", "]]]]]]]", "]]]]]]]]"};
/** /**
* The default number of elements for printing INDArrays (via NDArrayStrings or INDArray.toString) * The default number of elements for printing INDArrays (via NDArrayStrings or INDArray.toString)
*/ */
@ -190,29 +193,29 @@ public class NDArrayStrings {
private String format(INDArray arr, int offset, boolean summarize) { private String format(INDArray arr, int offset, boolean summarize) {
int rank = arr.rank(); int rank = arr.rank();
if (arr.isScalar()) { if (arr.isScalar() || arr.length() == 1) {
int fRank = Math.min(rank, OPEN_BRACKETS.length-1);
if (arr.isR()) { if (arr.isR()) {
//true scalar i.e shape = [] not legacy which is [1,1]
double arrElement = arr.getDouble(0); double arrElement = arr.getDouble(0);
if (!dontOverrideFormat && ((Math.abs(arrElement) < this.minToPrintWithoutSwitching && arrElement != 0) || (Math.abs(arrElement) >= this.maxToPrintWithoutSwitching))) { if (!dontOverrideFormat && ((Math.abs(arrElement) < this.minToPrintWithoutSwitching && arrElement != 0) || (Math.abs(arrElement) >= this.maxToPrintWithoutSwitching))) {
//switch to scientific notation //switch to scientific notation
String asString = localeIndifferentDecimalFormat(scientificFormat).format(arrElement); String asString = localeIndifferentDecimalFormat(scientificFormat).format(arrElement);
//from E to small e //from E to small e
asString = asString.replace('E', 'e'); asString = asString.replace('E', 'e');
return asString; return OPEN_BRACKETS[fRank] + asString + CLOSE_BRACKETS[fRank];
} else { } else {
if (arr.getDouble(0) == 0) return "0"; if (arr.getDouble(0) == 0) return OPEN_BRACKETS[fRank] + "0" + CLOSE_BRACKETS[fRank];
return decimalFormat.format(arr.getDouble(0)); return OPEN_BRACKETS[fRank] + decimalFormat.format(arr.getDouble(0)) + CLOSE_BRACKETS[fRank];
} }
} else if (arr.isZ()) { } else if (arr.isZ()) {
long arrElement = arr.getLong(0); long arrElement = arr.getLong(0);
return String.valueOf(arrElement); return OPEN_BRACKETS[fRank] + arrElement + CLOSE_BRACKETS[fRank];
} else if (arr.isB()) { } else if (arr.isB()) {
long arrElement = arr.getLong(0); long arrElement = arr.getLong(0);
return arrElement == 0 ? "false" : "true"; return OPEN_BRACKETS[fRank] + (arrElement == 0 ? "false" : "true") + CLOSE_BRACKETS[fRank];
} else if (arr.dataType() == DataType.UTF8){ } else if (arr.dataType() == DataType.UTF8){
String s = arr.getString(0); String s = arr.getString(0);
return "\"" + s.replaceAll("\n","\\n") + "\""; return OPEN_BRACKETS[fRank] + "\"" + s.replaceAll("\n","\\n") + "\"" + CLOSE_BRACKETS[fRank];
} else } else
throw new ND4JIllegalStateException(); throw new ND4JIllegalStateException();
} else if (rank == 1) { } else if (rank == 1) {
@ -246,9 +249,10 @@ public class NDArrayStrings {
//hack fix for slice issue with 'f' order //hack fix for slice issue with 'f' order
if (arr.ordering() == 'f' && arr.rank() > 2 && arr.size(arr.rank() - 1) == 1) { if (arr.ordering() == 'f' && arr.rank() > 2 && arr.size(arr.rank() - 1) == 1) {
sb.append(format(arr.dup('c').slice(i), offset, summarize)); sb.append(format(arr.dup('c').slice(i), offset, summarize));
} else if(arr.rank() <= 1 || arr.length() == 1) {
sb.append(format(Nd4j.scalar(arr.getDouble(0)),offset,summarize));
} }
// else if(arr.rank() <= 1 || arr.length() == 1) {
// sb.append(format(Nd4j.scalar(arr.getDouble(0)),offset,summarize));
// }
else { else {
sb.append(format(arr.slice(i), offset, summarize)); sb.append(format(arr.slice(i), offset, summarize));
} }

70
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/pom.xml
View File

@ -34,13 +34,6 @@
<classifier>${dependency.classifier}</classifier> <classifier>${dependency.classifier}</classifier>
</dependency> </dependency>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-core</artifactId>
<version>5.0.2.RELEASE</version>
<scope>test</scope>
</dependency>
<dependency> <dependency>
<groupId>org.bytedeco</groupId> <groupId>org.bytedeco</groupId>
<artifactId>javacpp</artifactId> <artifactId>javacpp</artifactId>
@ -87,73 +80,10 @@
<artifactId>nd4j-api</artifactId> <artifactId>nd4j-api</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-jackson</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
<version>${logback.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-tensorflow</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
<!-- Reflections: required in one of the tests -->
<dependency>
<groupId>org.reflections</groupId>
<artifactId>reflections</artifactId>
<version>${reflections.version}</version>
<scope>test</scope>
<exclusions>
<exclusion>
<groupId>com.google.code.findbugs</groupId>
<artifactId>*</artifactId>
</exclusion>
</exclusions>
</dependency>
</dependencies> </dependencies>
<build> <build>
<plugins> <plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<configuration>
<environmentVariables>
<LD_LIBRARY_PATH>${env.LD_LIBRARY_PATH}:${user.dir}:${libnd4jhome}/blasbuild/cpu/blas/</LD_LIBRARY_PATH>
</environmentVariables>
<includes>
<include>*.java</include>
<include>**/*.java</include>
<include>**/Test*.java</include>
<include>**/*Test.java</include>
<include>**/*TestCase.java</include>
</includes>
<junitArtifactName>junit:junit</junitArtifactName>
<systemPropertyVariables>
<org.nd4j.linalg.defaultbackend>org.nd4j.linalg.cpu.nativecpu.CpuBackend</org.nd4j.linalg.defaultbackend>
<org.nd4j.linalg.tests.backendstorun>org.nd4j.linalg.cpu.nativecpu.CpuBackend</org.nd4j.linalg.tests.backendstorun>
</systemPropertyVariables>
<!--
Maximum heap size was set to 8g, as a minimum required value for tests run.
Depending on a build machine, default value is not always enough.
-->
<argLine>-Ddtype=float -Xmx8g</argLine>
</configuration>
</plugin>
<plugin> <plugin>
<artifactId>maven-compiler-plugin</artifactId> <artifactId>maven-compiler-plugin</artifactId>
<executions> <executions>

2
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/nativeblas/Nd4jCpu.java
View File

@ -21680,7 +21680,7 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
// #endif // #endif
/** /**
* This operation performs batch normalization of layer, it is based on following article http://arxiv.org/abs/1502.03167. * This operation performs batch normalization of layer, it is based on following article https://arxiv.org/abs/1502.03167.
* Expected arguments: * Expected arguments:
* x: input 4D array of shape [bS,iH,iW,iD] (data format = NHWC) or [bS,iD,iH,iW] (data format = NCHW), where * x: input 4D array of shape [bS,iH,iW,iD] (data format = NHWC) or [bS,iD,iH,iW] (data format = NCHW), where
* bS - batch size * bS - batch size

15
nd4j/nd4j-backends/nd4j-tests/pom.xml
View File

@ -57,12 +57,7 @@
</plugins> </plugins>
</build> </build>
<dependencies> <dependencies>
<dependency>
<groupId>org.springframework</groupId>
<artifactId>spring-core</artifactId>
<version>5.0.2.RELEASE</version>
<scope>test</scope>
</dependency>
<dependency> <dependency>
<groupId>junit</groupId> <groupId>junit</groupId>
@ -105,10 +100,12 @@
<artifactId>logback-core</artifactId> <artifactId>logback-core</artifactId>
<version>${logback.version}</version> <version>${logback.version}</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.nd4j</groupId> <groupId>org.springframework</groupId>
<artifactId>nd4j-kafka_${scala.binary.version}</artifactId> <artifactId>spring-core</artifactId>
<version>${project.version}</version> <version>5.0.2.RELEASE</version>
<scope>test</scope>
</dependency> </dependency>
<dependency> <dependency>

4
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/BaseNd4jTest.java
View File

@ -20,16 +20,13 @@ package org.nd4j.linalg;
import lombok.val; import lombok.val;
import org.bytedeco.javacpp.Pointer; import org.bytedeco.javacpp.Pointer;
import org.junit.After; import org.junit.After;
import org.junit.AfterClass;
import org.junit.Before; import org.junit.Before;
import org.junit.Rule; import org.junit.Rule;
import org.junit.rules.TestName; import org.junit.rules.TestName;
import org.junit.runner.RunWith; import org.junit.runner.RunWith;
import org.junit.runners.Parameterized; import org.junit.runners.Parameterized;
import org.nd4j.config.ND4JEnvironmentVars;
import org.nd4j.config.ND4JSystemProperties; import org.nd4j.config.ND4JSystemProperties;
import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.environment.Nd4jEnvironment;
import org.nd4j.linalg.api.memory.MemoryWorkspace; import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.factory.Nd4jBackend; import org.nd4j.linalg.factory.Nd4jBackend;
@ -38,7 +35,6 @@ import org.nd4j.linalg.util.ArrayUtil;
import org.nd4j.nativeblas.NativeOpsHolder; import org.nd4j.nativeblas.NativeOpsHolder;
import org.slf4j.Logger; import org.slf4j.Logger;
import org.slf4j.LoggerFactory; import org.slf4j.LoggerFactory;
import scala.collection.mutable.StringBuilder;
import java.lang.management.ManagementFactory; import java.lang.management.ManagementFactory;
import java.util.*; import java.util.*;

25
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/ToStringTest.java
View File

@ -31,6 +31,7 @@ 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.factory.Nd4jBackend; import org.nd4j.linalg.factory.Nd4jBackend;
import org.nd4j.linalg.util.ArrayUtil;
@RunWith(Parameterized.class) @RunWith(Parameterized.class)
@Slf4j @Slf4j
@ -57,6 +58,30 @@ public class ToStringTest extends BaseNd4jTest {
Nd4j.createFromArray(1, 2, 3, 4, 5, 6, 7, 8).toString(6, true, 1)); Nd4j.createFromArray(1, 2, 3, 4, 5, 6, 7, 8).toString(6, true, 1));
} }
@Test
public void testToStringScalars(){
DataType[] dataTypes = new DataType[]{DataType.FLOAT, DataType.DOUBLE, DataType.BOOL, DataType.INT, DataType.UINT32};
String[] strs = new String[]{"1.0000", "1.0000", "true", "1", "1"};
for(int dt=0; dt<5; dt++ ) {
for (int i = 0; i < 5; i++) {
long[] shape = ArrayUtil.nTimes(i, 1L);
INDArray scalar = Nd4j.scalar(1.0f).castTo(dataTypes[dt]).reshape(shape);
String str = scalar.toString();
StringBuilder sb = new StringBuilder();
for (int j = 0; j < i; j++) {
sb.append("[");
}
sb.append(strs[dt]);
for (int j = 0; j < i; j++) {
sb.append("]");
}
String exp = sb.toString();
assertEquals("Rank: " + i + ", DT: " + dataTypes[dt], exp, str);
}
}
}
@Override @Override
public char ordering() { public char ordering() {
return 'c'; return 'c';

2
nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/ArrayUtil.java
View File

@ -1495,7 +1495,7 @@ public class ArrayUtil {
} }
//Credit: http://stackoverflow.com/questions/15533854/converting-byte-array-to-double-array //Credit: https://stackoverflow.com/questions/15533854/converting-byte-array-to-double-array
/** /**
* *

2
nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/MathUtils.java
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@ -107,7 +107,7 @@ public class MathUtils {
} }
/** /**
* See: <a href="http://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2">http://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2</a> * See: <a href="https://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2">https://stackoverflow.com/questions/466204/rounding-off-to-nearest-power-of-2</a>
* *
* @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

34
nd4j/nd4j-parameter-server-parent/nd4j-parameter-server-client/pom.xml
View File

@ -29,29 +29,6 @@
<name>nd4j-parameter-server-client</name> <name>nd4j-parameter-server-client</name>
<dependencies> <dependencies>
<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>
@ -72,11 +49,6 @@
<artifactId>nd4j-aeron</artifactId> <artifactId>nd4j-aeron</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </dependency>
<dependency>
<groupId>commons-io</groupId>
<artifactId>commons-io</artifactId>
<version>${commons-io.version}</version>
</dependency>
<dependency> <dependency>
<groupId>org.zeroturnaround</groupId> <groupId>org.zeroturnaround</groupId>
<artifactId>zt-exec</artifactId> <artifactId>zt-exec</artifactId>
@ -89,12 +61,6 @@
<version>${project.version}</version> <version>${project.version}</version>
<scope>test</scope> <scope>test</scope>
</dependency> </dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-parameter-server-status_2.11</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
<dependency> <dependency>
<groupId>ch.qos.logback</groupId> <groupId>ch.qos.logback</groupId>

1
nd4j/nd4j-parameter-server-parent/nd4j-parameter-server-client/src/test/java/org/nd4j/parameterserver/BaseNd4jTest.java
View File

@ -29,7 +29,6 @@ import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace; import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.profiler.ProfilerConfig; import org.nd4j.linalg.profiler.ProfilerConfig;
import scala.collection.mutable.StringBuilder;
import java.lang.management.ManagementFactory; import java.lang.management.ManagementFactory;
import java.util.List; import java.util.List;

2
nd4j/nd4j-parameter-server-parent/nd4j-parameter-server-client/src/test/java/org/nd4j/parameterserver/background/BackgroundDaemonStarter.java
View File

@ -31,7 +31,7 @@ import java.util.concurrent.TimeoutException;
/** /**
* Start background daemons for tests * Start background daemons for tests
* Credit to: * Credit to:
* http://stackoverflow.com/questions/636367/executing-a-java-application-in-a-separate-process * https://stackoverflow.com/questions/636367/executing-a-java-application-in-a-separate-process
* @author Adam Gibson * @author Adam Gibson
*/ */
@Slf4j @Slf4j

83
nd4j/nd4j-parameter-server-parent/nd4j-parameter-server-status/pom.xml
View File

@ -47,66 +47,19 @@
<artifactId>nd4j-parameter-server</artifactId> <artifactId>nd4j-parameter-server</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>joda-time</groupId> <groupId>junit</groupId>
<artifactId>joda-time</artifactId> <artifactId>junit</artifactId>
<version>${jodatime.version}</version> <scope>test</scope>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.apache.commons</groupId> <groupId>com.typesafe.play</groupId>
<artifactId>commons-lang3</artifactId> <artifactId>play-netty-server_2.11</artifactId>
<version>${commons-lang3.version}</version> <version>${playframework.version}</version>
</dependency>
<dependency>
<groupId>org.hibernate</groupId>
<artifactId>hibernate-validator</artifactId>
<version>${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>${typesafe.config.version}</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>com.typesafe.play</groupId> <groupId>com.typesafe.play</groupId>
<artifactId>play-java_2.11</artifactId> <artifactId>play-java_2.11</artifactId>
@ -142,24 +95,6 @@
</exclusion> </exclusion>
</exclusions> </exclusions>
</dependency> </dependency>
<dependency>
<groupId>net.jodah</groupId>
<artifactId>typetools</artifactId>
<version>${jodah.typetools.version}</version>
</dependency>
<dependency>
<groupId>com.typesafe.play</groupId>
<artifactId>play-netty-server_2.11</artifactId>
<version>${playframework.version}</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<scope>test</scope>
</dependency>
</dependencies> </dependencies>
<profiles> <profiles>

45
nd4j/nd4j-parameter-server-parent/nd4j-parameter-server/pom.xml
View File

@ -34,39 +34,6 @@
<artifactId>nd4j-parameter-server-model</artifactId> <artifactId>nd4j-parameter-server-model</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</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>
<groupId>com.mashape.unirest</groupId>
<artifactId>unirest-java</artifactId>
<version>${unirest.version}</version>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-jackson</artifactId>
<version>${project.version}</version>
</dependency>
<dependency> <dependency>
<groupId>org.slf4j</groupId> <groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId> <artifactId>slf4j-log4j12</artifactId>
@ -76,16 +43,20 @@
<groupId>junit</groupId> <groupId>junit</groupId>
<artifactId>junit</artifactId> <artifactId>junit</artifactId>
</dependency> </dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-aeron</artifactId>
<version>${project.version}</version>
</dependency>
<dependency> <dependency>
<groupId>com.beust</groupId> <groupId>com.beust</groupId>
<artifactId>jcommander</artifactId> <artifactId>jcommander</artifactId>
<version>${jcommander.version}</version> <version>${jcommander.version}</version>
</dependency> </dependency>
<dependency> <dependency>
<groupId>org.nd4j</groupId> <groupId>com.mashape.unirest</groupId>
<artifactId>nd4j-aeron</artifactId> <artifactId>unirest-java</artifactId>
<version>${project.version}</version> <version>${unirest.version}</version>
</dependency> </dependency>
</dependencies> </dependencies>

2
nd4j/nd4j-parameter-server-parent/nd4j-parameter-server/src/main/java/org/nd4j/parameterserver/util/CheckSocket.java
View File

@ -20,7 +20,7 @@ import java.io.IOException;
import java.net.*; import java.net.*;
/** /**
* Credit: http://stackoverflow.com/questions/5226905/test-if-remote-port-is-in-use * Credit: https://stackoverflow.com/questions/5226905/test-if-remote-port-is-in-use
* *
* *
*/ */

2
nd4j/nd4j-remote/nd4j-grpc-client/pom.xml
View File

@ -74,12 +74,14 @@
<groupId>ch.qos.logback</groupId> <groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId> <artifactId>logback-classic</artifactId>
<version>${logback.version}</version> <version>${logback.version}</version>
<scope>test</scope>
</dependency> </dependency>
<dependency> <dependency>
<groupId>ch.qos.logback</groupId> <groupId>ch.qos.logback</groupId>
<artifactId>logback-core</artifactId> <artifactId>logback-core</artifactId>
<version>${logback.version}</version> <version>${logback.version}</version>
<scope>test</scope>
</dependency> </dependency>
</dependencies> </dependencies>

10
nd4j/nd4j-serde/nd4j-arrow/pom.xml
View File

@ -39,16 +39,6 @@
<artifactId>nd4j-api</artifactId> <artifactId>nd4j-api</artifactId>
<version>${project.version}</version> <version>${project.version}</version>
</dependency> </dependency>
<dependency>
<groupId>com.carrotsearch</groupId>
<artifactId>hppc</artifactId>
<version>${hppc.version}</version>
</dependency>
<dependency>
<groupId>joda-time</groupId>
<artifactId>joda-time</artifactId>
<version>${jodatime.version}</version>
</dependency>
<dependency> <dependency>
<groupId>org.apache.arrow</groupId> <groupId>org.apache.arrow</groupId>
<artifactId>arrow-vector</artifactId> <artifactId>arrow-vector</artifactId>

21
nd4j/nd4j-serde/nd4j-kryo/pom.xml
View File

@ -94,26 +94,7 @@
<version>${jkserializers.version}</version> <version>${jkserializers.version}</version>
</dependency> </dependency>
<dependency>
<groupId>commons-codec</groupId>
<artifactId>commons-codec</artifactId>
<version>${commons-codec.version}</version>
</dependency>
<dependency>
<groupId>commons-io</groupId>
<artifactId>commons-io</artifactId>
<version>${commons-io.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>${commons-lang3.version}</version>
</dependency>
<dependency>
<groupId>org.ow2.asm</groupId>
<artifactId>asm</artifactId>
<version>${asm.version}</version>
</dependency>
<dependency> <dependency>
<groupId>org.apache.spark</groupId> <groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId> <artifactId>spark-core_2.11</artifactId>

2
rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/sync/qlearning/discrete/QLearningDiscrete.java
View File

@ -44,7 +44,7 @@ import java.util.ArrayList;
* *
* DQN or Deep Q-Learning in the Discrete domain * DQN or Deep Q-Learning in the Discrete domain
* *
* http://arxiv.org/abs/1312.5602 * https://arxiv.org/abs/1312.5602
* *
*/ */
public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O, Integer, DiscreteSpace> { public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O, Integer, DiscreteSpace> {