From adc586c566c65f256bb9d84c25416f01c4d73e9c Mon Sep 17 00:00:00 2001 From: helga_sh Date: Tue, 21 Jul 2020 16:24:31 +0300 Subject: [PATCH 1/5] CNN example with Deeplearning4j in Java --- deeplearning4j/pom.xml | 10 ++ .../deeplearning4j/cnn/CnnExample.java | 21 +++ .../cnn/domain/network/CnnModel.java | 120 ++++++++++++++++++ .../domain/network/CnnModelProperties.java | 13 ++ .../service/dataset/CifarDataSetService.java | 46 +++++++ .../cnn/service/dataset/IDataSetService.java | 16 +++ 6 files changed, 226 insertions(+) create mode 100644 deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnExample.java create mode 100644 deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModel.java create mode 100644 deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModelProperties.java create mode 100644 deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/CifarDataSetService.java create mode 100644 deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/IDataSetService.java diff --git a/deeplearning4j/pom.xml b/deeplearning4j/pom.xml index c8fa18cbd4..d88c877aa4 100644 --- a/deeplearning4j/pom.xml +++ b/deeplearning4j/pom.xml @@ -37,6 +37,16 @@ deeplearning4j-nn ${dl4j.version} + + org.slf4j + slf4j-api + 1.7.5 + + + org.slf4j + slf4j-log4j12 + 1.7.5 + org.datavec diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnExample.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnExample.java new file mode 100644 index 0000000000..2e2d4392b8 --- /dev/null +++ b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnExample.java @@ -0,0 +1,21 @@ +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 { + + public static void main(String... args) { + CnnModel network = new CnnModel(new CifarDataSetService(), new CnnModelProperties()); + + network.train(); + Evaluation evaluation = network.evaluate(); + + log.info(evaluation.stats()); + } +} diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModel.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModel.java new file mode 100644 index 0000000000..037d14529c --- /dev/null +++ b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModel.java @@ -0,0 +1,120 @@ +package com.baeldung.deeplearning4j.cnn.domain.network; + +import com.baeldung.deeplearning4j.cnn.service.dataset.IDataSetService; +import lombok.extern.slf4j.Slf4j; +import org.deeplearning4j.eval.Evaluation; +import org.deeplearning4j.nn.api.OptimizationAlgorithm; +import org.deeplearning4j.nn.conf.MultiLayerConfiguration; +import org.deeplearning4j.nn.conf.NeuralNetConfiguration; +import org.deeplearning4j.nn.conf.layers.ConvolutionLayer; +import org.deeplearning4j.nn.conf.layers.OutputLayer; +import org.deeplearning4j.nn.conf.layers.SubsamplingLayer; +import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; +import org.deeplearning4j.nn.weights.WeightInit; +import org.nd4j.linalg.activations.Activation; +import org.nd4j.linalg.lossfunctions.LossFunctions; + +import java.util.stream.IntStream; + +@Slf4j +public class CnnModel { + + private final IDataSetService dataSetService; + + private MultiLayerNetwork network; + + private final CnnModelProperties properties; + + public CnnModel(IDataSetService dataSetService, CnnModelProperties properties) { + + this.dataSetService = dataSetService; + this.properties = properties; + + MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder() + .seed(1611) + .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) + .learningRate(properties.getLearningRate()) + .regularization(true) + .updater(properties.getOptimizer()) + .list() + .layer(0, conv5x5()) + .layer(1, pooling2x2Stride2()) + .layer(2, conv3x3Stride1Padding2()) + .layer(3, pooling2x2Stride1()) + .layer(4, conv3x3Stride1Padding1()) + .layer(5, pooling2x2Stride1()) + .layer(6, dense()) + .pretrain(false) + .backprop(true) + .setInputType(dataSetService.inputType()) + .build(); + + network = new MultiLayerNetwork(configuration); + } + + public void train() { + network.init(); + int epochsNum = properties.getEpochsNum(); + IntStream.range(1, epochsNum + 1).forEach(epoch -> { + log.info(String.format("Epoch %d?%d", epoch, epochsNum)); + network.fit(dataSetService.trainIterator()); + }); + } + + public Evaluation evaluate() { + return network.evaluate(dataSetService.testIterator()); + } + + private ConvolutionLayer conv5x5() { + return new ConvolutionLayer.Builder(5, 5) + .nIn(3) + .nOut(16) + .stride(1, 1) + .padding(1, 1) + .weightInit(WeightInit.XAVIER_UNIFORM) + .activation(Activation.RELU) + .build(); + } + + private SubsamplingLayer pooling2x2Stride2() { + return new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) + .kernelSize(2, 2) + .stride(2, 2) + .build(); + } + + private ConvolutionLayer conv3x3Stride1Padding2() { + return new ConvolutionLayer.Builder(3, 3) + .nOut(32) + .stride(1, 1) + .padding(2, 2) + .weightInit(WeightInit.XAVIER_UNIFORM) + .activation(Activation.RELU) + .build(); + } + + private SubsamplingLayer pooling2x2Stride1() { + return new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) + .kernelSize(2,2) + .stride(1, 1) + .build(); + } + + private ConvolutionLayer conv3x3Stride1Padding1() { + return new ConvolutionLayer.Builder(3, 3) + .nOut(64) + .stride(1, 1) + .padding(1, 1) + .weightInit(WeightInit.XAVIER_UNIFORM) + .activation(Activation.RELU) + .build(); + } + + private OutputLayer dense() { + return new OutputLayer.Builder(LossFunctions.LossFunction.MEAN_SQUARED_LOGARITHMIC_ERROR) + .activation(Activation.SOFTMAX) + .weightInit(WeightInit.XAVIER_UNIFORM) + .nOut(dataSetService.labels().size() - 1) + .build(); + } +} diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModelProperties.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModelProperties.java new file mode 100644 index 0000000000..7ea3a71363 --- /dev/null +++ b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModelProperties.java @@ -0,0 +1,13 @@ +package com.baeldung.deeplearning4j.cnn.domain.network; + +import lombok.Value; +import org.deeplearning4j.nn.conf.Updater; + +@Value +public class CnnModelProperties { + private final int epochsNum = 512; + + private final double learningRate = 0.001; + + private final Updater optimizer = Updater.ADAM; +} diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/CifarDataSetService.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/CifarDataSetService.java new file mode 100644 index 0000000000..cb69d0c818 --- /dev/null +++ b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/CifarDataSetService.java @@ -0,0 +1,46 @@ +package com.baeldung.deeplearning4j.cnn.service.dataset; + +import lombok.Getter; +import org.deeplearning4j.datasets.iterator.impl.CifarDataSetIterator; +import org.deeplearning4j.nn.conf.inputs.InputType; +import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; + +import java.util.List; + +@Getter +public class CifarDataSetService implements IDataSetService { + + private CifarDataSetIterator trainIterator; + 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() { + trainIterator = new CifarDataSetIterator(trainBatch, trainImagesNum, true); + testIterator = new CifarDataSetIterator(testBatch, testImagesNum, false); + } + + @Override + public DataSetIterator trainIterator() { + return trainIterator; + } + + @Override + public DataSetIterator testIterator() { + return testIterator; + } + + @Override + public InputType inputType() { + return inputType; + } + + @Override + public List labels() { + return trainIterator.getLabels(); + } +} diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/IDataSetService.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/IDataSetService.java new file mode 100644 index 0000000000..c27e566076 --- /dev/null +++ b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/IDataSetService.java @@ -0,0 +1,16 @@ +package com.baeldung.deeplearning4j.cnn.service.dataset; + +import org.deeplearning4j.nn.conf.inputs.InputType; +import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; + +import java.util.List; + +public interface IDataSetService { + DataSetIterator trainIterator(); + + DataSetIterator testIterator(); + + InputType inputType(); + + List labels(); +} From 51f1fc9b1e07d5bd342b583b750b66bcabf13838 Mon Sep 17 00:00:00 2001 From: helga_sh Date: Thu, 23 Jul 2020 16:17:04 +0300 Subject: [PATCH 2/5] CNN example with Deeplearning4j in Java: refactor --- deeplearning4j/pom.xml | 5 +++-- .../dataset => }/CifarDataSetService.java | 15 ++++++------- .../deeplearning4j/cnn/CnnExample.java | 5 +---- .../cnn/{domain/network => }/CnnModel.java | 21 +++++++++---------- .../network => }/CnnModelProperties.java | 4 ++-- .../dataset => }/IDataSetService.java | 4 ++-- 6 files changed, 26 insertions(+), 28 deletions(-) rename deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/{service/dataset => }/CifarDataSetService.java (79%) rename deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/{domain/network => }/CnnModel.java (86%) rename deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/{domain/network => }/CnnModelProperties.java (70%) rename deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/{service/dataset => }/IDataSetService.java (74%) diff --git a/deeplearning4j/pom.xml b/deeplearning4j/pom.xml index d88c877aa4..e1e4998c98 100644 --- a/deeplearning4j/pom.xml +++ b/deeplearning4j/pom.xml @@ -40,12 +40,12 @@ org.slf4j slf4j-api - 1.7.5 + ${sl4j.version} org.slf4j slf4j-log4j12 - 1.7.5 + ${sl4j.version} @@ -63,6 +63,7 @@ 0.9.1 4.3.5 + 1.7.5 diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/CifarDataSetService.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CifarDataSetService.java similarity index 79% rename from deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/CifarDataSetService.java rename to deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CifarDataSetService.java index cb69d0c818..70348a6d9e 100644 --- a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/CifarDataSetService.java +++ b/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 CifarDataSetIterator testIterator; - - private final InputType inputType = InputType.convolutional(32,32,3); + 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); } diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnExample.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnExample.java index 2e2d4392b8..224062c388 100644 --- a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnExample.java +++ b/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()); diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModel.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModel.java similarity index 86% rename from deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModel.java rename to deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModel.java index 037d14529c..bd87709c0e 100644 --- a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModel.java +++ b/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; @@ -52,17 +51,17 @@ 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() { - return network.evaluate(dataSetService.testIterator()); + Evaluation evaluate() { + return network.evaluate(dataSetService.testIterator()); } private ConvolutionLayer conv5x5() { @@ -84,7 +83,7 @@ public class CnnModel { } private ConvolutionLayer conv3x3Stride1Padding2() { - return new ConvolutionLayer.Builder(3, 3) + return new ConvolutionLayer.Builder(3, 3) .nOut(32) .stride(1, 1) .padding(2, 2) @@ -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(); } diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModelProperties.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModelProperties.java similarity index 70% rename from deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModelProperties.java rename to deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModelProperties.java index 7ea3a71363..d010d789c8 100644 --- a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/domain/network/CnnModelProperties.java +++ b/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; diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/IDataSetService.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/IDataSetService.java similarity index 74% rename from deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/IDataSetService.java rename to deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/IDataSetService.java index c27e566076..ea88bf550c 100644 --- a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/service/dataset/IDataSetService.java +++ b/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(); From 06a10e83536d815e2775944a11780051a8d2dcaf Mon Sep 17 00:00:00 2001 From: helga_sh Date: Thu, 23 Jul 2020 17:33:06 +0300 Subject: [PATCH 3/5] CNN example with Deeplearning4j in Java: add missing letter in pom --- deeplearning4j/pom.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deeplearning4j/pom.xml b/deeplearning4j/pom.xml index e1e4998c98..8ec335b184 100644 --- a/deeplearning4j/pom.xml +++ b/deeplearning4j/pom.xml @@ -63,7 +63,7 @@ 0.9.1 4.3.5 - 1.7.5 + 1.7.5 From c67658322b281f5eff5ff9b7556f0119df607b20 Mon Sep 17 00:00:00 2001 From: helga_sh Date: Thu, 23 Jul 2020 17:33:31 +0300 Subject: [PATCH 4/5] CNN example with Deeplearning4j in Java: add missing letter in pom --- deeplearning4j/pom.xml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deeplearning4j/pom.xml b/deeplearning4j/pom.xml index 8ec335b184..af65aa7e03 100644 --- a/deeplearning4j/pom.xml +++ b/deeplearning4j/pom.xml @@ -40,12 +40,12 @@ org.slf4j slf4j-api - ${sl4j.version} + ${slf4j.version} org.slf4j slf4j-log4j12 - ${sl4j.version} + ${slf4j.version} From 768658cfc8ab0ad9a9df6fca9574f72ac1905576 Mon Sep 17 00:00:00 2001 From: helga_sh Date: Wed, 29 Jul 2020 11:36:09 +0300 Subject: [PATCH 5/5] Fixed builder formatting --- .../baeldung/deeplearning4j/cnn/CnnModel.java | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModel.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModel.java index bd87709c0e..efa7f828ed 100644 --- a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModel.java +++ b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/cnn/CnnModel.java @@ -30,23 +30,23 @@ class CnnModel { this.properties = properties; MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder() - .seed(1611) - .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) - .learningRate(properties.getLearningRate()) - .regularization(true) - .updater(properties.getOptimizer()) - .list() - .layer(0, conv5x5()) - .layer(1, pooling2x2Stride2()) - .layer(2, conv3x3Stride1Padding2()) - .layer(3, pooling2x2Stride1()) - .layer(4, conv3x3Stride1Padding1()) - .layer(5, pooling2x2Stride1()) - .layer(6, dense()) - .pretrain(false) - .backprop(true) - .setInputType(dataSetService.inputType()) - .build(); + .seed(1611) + .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) + .learningRate(properties.getLearningRate()) + .regularization(true) + .updater(properties.getOptimizer()) + .list() + .layer(0, conv5x5()) + .layer(1, pooling2x2Stride2()) + .layer(2, conv3x3Stride1Padding2()) + .layer(3, pooling2x2Stride1()) + .layer(4, conv3x3Stride1Padding1()) + .layer(5, pooling2x2Stride1()) + .layer(6, dense()) + .pretrain(false) + .backprop(true) + .setInputType(dataSetService.inputType()) + .build(); network = new MultiLayerNetwork(configuration); }