CNN example with Deeplearning4j in Java: refactor

deeplearning4j/pom.xml

@@ -40,12 +40,12 @@
         <dependency>
             <groupId>org.slf4j</groupId>
             <artifactId>slf4j-api</artifactId>
-            <version>1.7.5</version>
+            <version>${sl4j.version}</version>
         </dependency>
         <dependency>
             <groupId>org.slf4j</groupId>
             <artifactId>slf4j-log4j12</artifactId>
-            <version>1.7.5</version>
+            <version>${sl4j.version}</version>
         </dependency>
         <!-- https://mvnrepository.com/artifact/org.datavec/datavec-api -->
         <dependency>
@@ -63,6 +63,7 @@
     <properties>
         <dl4j.version>0.9.1</dl4j.version> <!-- Latest non beta version -->
         <httpclient.version>4.3.5</httpclient.version>
+        <sl4j.version>1.7.5</sl4j.version>
     </properties>
 
 </project>

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

@@ -1,4 +1,4 @@
-package com.baeldung.deeplearning4j.cnn.service.dataset;
+package com.baeldung.deeplearning4j.cnn;
 
 import lombok.Getter;
 import org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator;
@@ -8,18 +8,19 @@ import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import java.util.List;
 
 @Getter
-public class CifarDataSetService implements IDataSetService {
+class CifarDataSetService implements IDataSetService {
 
-    private CifarDataSetIterator trainIterator;
+    private final InputType inputType = InputType.convolutional(32, 32, 3);
-    private CifarDataSetIterator testIterator;
-
-    private final InputType inputType = InputType.convolutional(32,32,3);
     private final int trainImagesNum = 512;
     private final int testImagesNum = 128;
     private final int trainBatch = 16;
     private final int testBatch = 8;
 
-    public CifarDataSetService() {
+    private final CifarDataSetIterator trainIterator;
+
+    private final CifarDataSetIterator testIterator;
+
+    CifarDataSetService() {
         trainIterator = new CifarDataSetIterator(trainBatch, trainImagesNum, true);
         testIterator = new CifarDataSetIterator(testBatch, testImagesNum, false);
     }

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

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

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

@@ -1,6 +1,5 @@
-package com.baeldung.deeplearning4j.cnn.domain.network;
+package com.baeldung.deeplearning4j.cnn;
 
-import com.baeldung.deeplearning4j.cnn.service.dataset.IDataSetService;
 import lombok.extern.slf4j.Slf4j;
 import org.deeplearning4j.eval.Evaluation;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
@@ -17,15 +16,15 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 import java.util.stream.IntStream;
 
 @Slf4j
-public class CnnModel {
+class CnnModel {
 
     private final IDataSetService dataSetService;
 
-    private MultiLayerNetwork network;
+    private final MultiLayerNetwork network;
 
     private final CnnModelProperties properties;
 
-    public CnnModel(IDataSetService dataSetService, CnnModelProperties properties) {
+    CnnModel(IDataSetService dataSetService, CnnModelProperties properties) {
 
         this.dataSetService = dataSetService;
         this.properties = properties;
@@ -52,16 +51,16 @@ public class CnnModel {
         network = new MultiLayerNetwork(configuration);
     }
 
-    public void train() {
+    void train() {
         network.init();
         int epochsNum = properties.getEpochsNum();
         IntStream.range(1, epochsNum + 1).forEach(epoch -> {
-            log.info(String.format("Epoch %d?%d", epoch, epochsNum));
+            log.info("Epoch {} / {}", epoch, epochsNum);
             network.fit(dataSetService.trainIterator());
         });
     }
 
-    public Evaluation evaluate() {
+    Evaluation evaluate() {
         return network.evaluate(dataSetService.testIterator());
     }
 
@@ -95,7 +94,7 @@ public class CnnModel {
 
     private SubsamplingLayer pooling2x2Stride1() {
         return new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
-                .kernelSize(2,2)
+                .kernelSize(2, 2)
                 .stride(1, 1)
                 .build();
     }

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

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

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

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