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helga_sh
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CNN example with Deeplearning4j in Java: refactor
1 parent 6e1cab0 commit eeff326

6 files changed

Lines changed: 26 additions & 28 deletions

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‎deeplearning4j/pom.xml‎

Lines changed: 3 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -40,12 +40,12 @@
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<dependency>
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<groupId>org.slf4j</groupId>
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<artifactId>slf4j-api</artifactId>
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<version>1.7.5</version>
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<version>${sl4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.slf4j</groupId>
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<artifactId>slf4j-log4j12</artifactId>
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<version>1.7.5</version>
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<version>${sl4j.version}</version>
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</dependency>
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<!-- https://mvnrepository.com/artifact/org.datavec/datavec-api -->
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<dependency>
@@ -63,6 +63,7 @@
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<properties>
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<dl4j.version>0.9.1</dl4j.version> <!-- Latest non beta version -->
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<httpclient.version>4.3.5</httpclient.version>
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<sl4j.version>1.7.5</sl4j.version>
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</properties>
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</project>

deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/CifarDataSetService.java renamed to deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CifarDataSetService.java

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@@ -1,4 +1,4 @@
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package com.baeldung.deeplearning4j.cnn.service.dataset;
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package com.baeldung.deeplearning4j.cnn;
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import lombok.Getter;
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import org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator;
@@ -8,18 +8,19 @@
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import java.util.List;
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@Getter
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public class CifarDataSetService implements IDataSetService {
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class CifarDataSetService implements IDataSetService {
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private CifarDataSetIterator trainIterator;
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private CifarDataSetIterator testIterator;
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private final InputType inputType = InputType.convolutional(32,32,3);
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private final InputType inputType = InputType.convolutional(32, 32, 3);
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private final int trainImagesNum = 512;
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private final int testImagesNum = 128;
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private final int trainBatch = 16;
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private final int testBatch = 8;
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public CifarDataSetService() {
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private final CifarDataSetIterator trainIterator;
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private final CifarDataSetIterator testIterator;
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CifarDataSetService() {
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trainIterator = new CifarDataSetIterator(trainBatch, trainImagesNum, true);
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testIterator = new CifarDataSetIterator(testBatch, testImagesNum, false);
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}

‎deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnExample.java‎

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Original file line numberDiff line numberDiff line change
@@ -1,14 +1,11 @@
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package com.baeldung.deeplearning4j.cnn;
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import com.baeldung.deeplearning4j.cnn.domain.network.CnnModel;
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import com.baeldung.deeplearning4j.cnn.domain.network.CnnModelProperties;
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import com.baeldung.deeplearning4j.cnn.service.dataset.CifarDataSetService;
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import lombok.extern.slf4j.Slf4j;
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import org.deeplearning4j.eval.Evaluation;
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@Slf4j
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public class CnnExample {
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class CnnExample {
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public static void main(String... args) {
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CnnModel network = new CnnModel(new CifarDataSetService(), new CnnModelProperties());

deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModel.java renamed to deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModel.java

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Original file line numberDiff line numberDiff line change
@@ -1,6 +1,5 @@
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package com.baeldung.deeplearning4j.cnn.domain.network;
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package com.baeldung.deeplearning4j.cnn;
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import com.baeldung.deeplearning4j.cnn.service.dataset.IDataSetService;
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import lombok.extern.slf4j.Slf4j;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.nn.api.OptimizationAlgorithm;
@@ -17,15 +16,15 @@
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import java.util.stream.IntStream;
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@Slf4j
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public class CnnModel {
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class CnnModel {
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private final IDataSetService dataSetService;
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private MultiLayerNetwork network;
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private final MultiLayerNetwork network;
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private final CnnModelProperties properties;
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public CnnModel(IDataSetService dataSetService, CnnModelProperties properties) {
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CnnModel(IDataSetService dataSetService, CnnModelProperties properties) {
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this.dataSetService = dataSetService;
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this.properties = properties;
@@ -52,17 +51,17 @@ public CnnModel(IDataSetService dataSetService, CnnModelProperties properties) {
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network = new MultiLayerNetwork(configuration);
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}
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public void train() {
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void train() {
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network.init();
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int epochsNum = properties.getEpochsNum();
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IntStream.range(1, epochsNum + 1).forEach(epoch -> {
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log.info(String.format("Epoch %d?%d", epoch, epochsNum));
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log.info("Epoch {} / {}", epoch, epochsNum);
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network.fit(dataSetService.trainIterator());
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});
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}
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public Evaluation evaluate() {
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return network.evaluate(dataSetService.testIterator());
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Evaluation evaluate() {
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return network.evaluate(dataSetService.testIterator());
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}
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private ConvolutionLayer conv5x5() {
@@ -84,7 +83,7 @@ private SubsamplingLayer pooling2x2Stride2() {
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}
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private ConvolutionLayer conv3x3Stride1Padding2() {
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return new ConvolutionLayer.Builder(3, 3)
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return new ConvolutionLayer.Builder(3, 3)
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.nOut(32)
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.stride(1, 1)
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.padding(2, 2)
@@ -95,7 +94,7 @@ private ConvolutionLayer conv3x3Stride1Padding2() {
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private SubsamplingLayer pooling2x2Stride1() {
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return new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
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.kernelSize(2,2)
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.kernelSize(2, 2)
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.stride(1, 1)
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.build();
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}

deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModelProperties.java renamed to deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModelProperties.java

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@@ -1,10 +1,10 @@
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package com.baeldung.deeplearning4j.cnn.domain.network;
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package com.baeldung.deeplearning4j.cnn;
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import lombok.Value;
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import org.deeplearning4j.nn.conf.Updater;
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@Value
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public class CnnModelProperties {
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class CnnModelProperties {
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private final int epochsNum = 512;
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private final double learningRate = 0.001;

deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/IDataSetService.java renamed to deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/IDataSetService.java

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@@ -1,11 +1,11 @@
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package com.baeldung.deeplearning4j.cnn.service.dataset;
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package com.baeldung.deeplearning4j.cnn;
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import org.deeplearning4j.nn.conf.inputs.InputType;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import java.util.List;
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8-
public interface IDataSetService {
8+
interface IDataSetService {
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DataSetIterator trainIterator();
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DataSetIterator testIterator();

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