gan example

Signed-off-by: brian <brian@brutex.de>

.docker/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:11.4.0-cudnn8-devel-ubuntu20.04
+FROM nvidia/cuda:11.4.3-cudnn8-devel-ubuntu20.04
 
 RUN apt-get update &&  \
     DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-11-jdk wget build-essential checkinstall zlib1g-dev libssl-dev git

.gitignore

@@ -36,6 +36,8 @@ pom.xml.versionsBackup
 pom.xml.next
 release.properties
 *dependency-reduced-pom.xml
+**/build/*
+.gradle/*
 
 # Specific for Nd4j
 *.md5
@@ -83,3 +85,14 @@ bruai4j-native-common/cmake*
 /bruai4j-native/bruai4j-native-common/blasbuild/
 /bruai4j-native/bruai4j-native-common/build/
 /cavis-native/cavis-native-lib/blasbuild/
+/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/classes/org.deeplearning4j.gradientcheck.AttentionLayerTest.html
+/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/css/base-style.css
+/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/css/style.css
+/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/js/report.js
+/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/packages/org.deeplearning4j.gradientcheck.html
+/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/index.html
+/cavis-dnn/cavis-dnn-core/build/resources/main/iris.dat
+/cavis-dnn/cavis-dnn-core/build/resources/test/junit-platform.properties
+/cavis-dnn/cavis-dnn-core/build/resources/test/logback-test.xml
+/cavis-dnn/cavis-dnn-core/build/test-results/cudaTest/TEST-org.deeplearning4j.gradientcheck.AttentionLayerTest.xml
+/cavis-dnn/cavis-dnn-core/build/tmp/jar/MANIFEST.MF

.jenkins/linux-x86_64-cpu-build.jenkinsfile

@@ -35,7 +35,7 @@ pipeline {
                 }
         stage('build-linux-cpu') {
             environment {
-                        MAVEN = credentials('Internal Archiva')
+                        MAVEN = credentials('Internal_Archiva')
                         OSSRH = credentials('OSSRH')
             }
 

.jenkins/linux-x86_64-cuda-build.jenkinsfile

@@ -21,13 +21,15 @@
 
 pipeline {
     agent {
-        dockerfile {
+      /*  dockerfile {
             filename 'Dockerfile'
             dir '.docker'
             label 'linux && cuda'
             //additionalBuildArgs  '--build-arg version=1.0.2'
             //args '--gpus all' --needed for test only, you can build without GPU
         }
+        */
+       label 'linux && cuda'
     }
 
     stages {
@@ -43,13 +45,13 @@ pipeline {
                 }
         stage('build-linux-cuda') {
             environment {
-                        MAVEN = credentials('Internal Archiva')
+                        MAVEN = credentials('Internal_Archiva')
                         OSSRH = credentials('OSSRH')
             }
 
             steps {
                 withGradle {
-                    sh 'sh ./gradlew build --stacktrace -x test -PCAVIS_CHIP=cuda \
+                    sh 'sh ./gradlew build --stacktrace  -PCAVIS_CHIP=cuda \
                                 -Pmavenuser=$MAVEN_USR -Pmavenpass=$MAVEN_PSW \
                                 -PossrhUsername=$OSSRH_USR -PossrhPassword=$OSSRH_PSW'
                 }

.jenkins/linux-x86_64-docker-all-publish.jenkinsfile

@@ -47,7 +47,7 @@ pipeline {
                 }
                 stage('build-linux-cuda') {
                     environment {
-                        MAVEN = credentials('Internal Archiva')
+                        MAVEN = credentials('Internal_Archiva')
                         OSSRH = credentials('OSSRH')
                     }
 

.jenkins/linux-x86_64-docker-cpu-build.jenkinsfile

@@ -41,7 +41,7 @@ pipeline {
                 }
         stage('build-linux-cpu') {
             environment {
-                        MAVEN = credentials('Internal Archiva')
+                        MAVEN = credentials('Internal_Archiva')
                         OSSRH = credentials('OSSRH')
             }
 

.jenkins/linux-x86_64-docker-cpu-publish.jenkinsfile

@@ -33,7 +33,7 @@ pipeline {
     stages {
         stage('publish-linux-cpu') {
             environment {
-                MAVEN = credentials('Internal Archiva')
+                MAVEN = credentials('Internal_Archiva')
                 OSSRH = credentials('OSSRH')
             }
 

.jenkins/linux-x86_64-docker-cuda-build.jenkinsfile

@@ -43,7 +43,7 @@ pipeline {
                 }
         stage('build-linux-cuda') {
             environment {
-                        MAVEN = credentials('Internal Archiva')
+                        MAVEN = credentials('Internal_Archiva')
                         OSSRH = credentials('OSSRH')
             }
 
@@ -56,5 +56,20 @@ pipeline {
                 //stash includes: '/cavis-native/cavis-native-lib/build/lib/*.jar', name: 'cuda-build'
             }
         }
+        stage('test-linux-cuda') {
+            environment {
+                MAVEN = credentials('Internal_Archiva')
+                OSSRH = credentials('OSSRH')
+            }
+
+            steps {
+                withGradle {
+                    sh 'sh ./gradlew test --stacktrace -PexcludeTests=\'long-running,performance\' -Pskip-native=true -PCAVIS_CHIP=cuda \
+                                -Pmavenuser=$MAVEN_USR -Pmavenpass=$MAVEN_PSW \
+                                -PossrhUsername=$OSSRH_USR -PossrhPassword=$OSSRH_PSW'
+                }
+                //stash includes: '/cavis-native/cavis-native-lib/build/lib/*.jar', name: 'cuda-build'
+            }
+        }
     }
 }

.jenkins/wsl-x86_64-docker-cpu-build.jenkinsfile

@@ -41,7 +41,7 @@ pipeline {
                 }
         stage('build-linux-cpu') {
             environment {
-                        MAVEN = credentials('Internal Archiva')
+                        MAVEN = credentials('Internal_Archiva')
                         OSSRH = credentials('OSSRH')
             }
 

brutex-extended-tests/src/test/java/net/brutex/ai/nd4j/tests/ExploreParamsTest.java

@@ -0,0 +1,167 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * This program and the accompanying materials are made available under the
+ *    * terms of the Apache License, Version 2.0 which is available at
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
+ *    *
+ *    *  See the NOTICE file distributed with this work for additional
+ *    *  information regarding copyright ownership.
+ *    * Unless required by applicable law or agreed to in writing, software
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * License for the specific language governing permissions and limitations
+ *    * under the License.
+ *    *
+ *    * SPDX-License-Identifier: Apache-2.0
+ *    *****************************************************************************
+ *
+ */
+
+package net.brutex.ai.nd4j.tests;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+import lombok.extern.slf4j.Slf4j;
+import org.deeplearning4j.datasets.iterator.INDArrayDataSetIterator;
+import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
+import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.DenseLayer;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.junit.jupiter.api.Test;
+import org.nd4j.common.primitives.Pair;
+import org.nd4j.evaluation.classification.Evaluation;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+@Slf4j
+public class ExploreParamsTest {
+
+  @Test
+  public void testParam() {
+    NeuralNetConfiguration conf =
+        NeuralNetConfiguration.builder()
+            .seed(12345)
+                .dataType(DataType.DOUBLE)
+            .layer(
+                DenseLayer.builder().nIn(4).nOut(30).name("1. Dense").activation(Activation.TANH))
+           .layer(DenseLayer.builder().nIn(30).nOut(10).name("2. Dense"))
+            //  .layer(FrozenLayer.builder(DenseLayer.builder().nOut(6).build()).build())
+
+            .layer(
+                OutputLayer.builder()
+                    .nOut(3)
+                    .lossFunction(LossFunctions.LossFunction.MSE)
+                    .activation(Activation.SOFTMAX))
+            .build();
+    MultiLayerNetwork nn = new MultiLayerNetwork(conf);
+    nn.init();
+    log.info(nn.summary());
+    // INDArray input = Nd4j.rand(10,4);
+    INDArray labels = Nd4j.zeros(9, 3);
+
+    INDArray input =
+        Nd4j.create(
+            new double[][] {
+              {5.15, 3.5, 1.4, 0.21},     // setosa
+              {4.9, 3.2, 1.4, 0.2},       // setosa
+              {4.7, 3.2, 1.23, 0.2},      // setosa
+              {7, 3.25, 4.7, 1.41},       // versicolor
+              {6.4, 3.2, 4.54, 1.5},      // versicolor
+              {6.9, 3.1, 4.92, 1.5},      // versicolor
+              {7.7, 3, 6.1, 2.3},         // virginica
+              {6.3, 3.4, 5.6, 2.45},      // virginica
+              {6.4, 3.12, 5.5, 1.8}       // virginica
+            });
+
+    labels.putScalar(0, 1);
+    labels.putScalar(3, 1);
+    labels.putScalar(6, 1);
+    labels.putScalar(10, 1);
+    labels.putScalar(13, 1);
+    labels.putScalar(16, 1);
+    labels.putScalar(20, 1);
+    labels.putScalar(23, 1);
+    labels.putScalar(26, 1);
+
+    IrisDataSetIterator iter = new IrisDataSetIterator();
+    //Iterable<Pair<INDArray, INDArray>> it = List.of(new Pair<INDArray, INDArray>(input, labels));
+    List l = new ArrayList<>();
+    for (int i=0; i< input.rows(); i++) {
+      l.add(new Pair(input.getRow(i), labels.getRow(i)));
+    }
+    Iterable<Pair<INDArray, INDArray>> it = l;
+    INDArrayDataSetIterator diter = new INDArrayDataSetIterator(it, 1);
+
+    for (int i = 0; i < 100; i++) {
+      // nn.fit(input, labels);
+      // nn.fit( input, labels);
+      nn.fit(diter);
+      // nn.feedForward(input);
+      if(i%20==0) log.info("Score: {}", nn.getScore());
+    }
+
+    Evaluation eval = nn.evaluate(iter, List.of("setosa", "vericolor", "virginica"));
+
+    log.info("\n{}", eval.stats());
+  }
+
+  @Test
+  public void testParam2() throws IOException {
+    NeuralNetConfiguration conf =
+            NeuralNetConfiguration.builder()
+                    .seed(12345)
+                    .layer(
+                            DenseLayer.builder().nIn(784).nOut(20).name("1. Dense"))
+                    .layer(DenseLayer.builder().nIn(20).nOut(10).name("2. Dense"))
+                    .layer(
+                            OutputLayer.builder()
+                                    .nOut(10)
+                                    .lossFunction(LossFunctions.LossFunction.MSE)
+                                    .activation(Activation.SOFTMAX))
+                    .build();
+    MultiLayerNetwork nn = new MultiLayerNetwork(conf);
+    nn.init();
+    log.info(nn.summary());
+
+    NeuralNetConfiguration conf2 =
+            NeuralNetConfiguration.builder()
+                    .seed(12345)
+                    .layer(
+                            DenseLayer.builder().nIn(784).nOut(20).name("1. Dense").dropOut(0.7))
+                    .layer(DenseLayer.builder().nIn(20).nOut(10).name("2. Dense"))
+                    .layer(
+                            OutputLayer.builder()
+                                    .nOut(10)
+                                    .lossFunction(LossFunctions.LossFunction.MSE)
+                                    .activation(Activation.SOFTMAX))
+                    .build();
+    MultiLayerNetwork nn2 = new MultiLayerNetwork(conf2);
+    nn2.init();
+    log.info(nn2.summary());
+
+
+
+    MnistDataSetIterator iter = new MnistDataSetIterator(10, 500);
+    MnistDataSetIterator iter2 = new MnistDataSetIterator(10, 50);
+
+
+    for (int i = 0; i < 200; i++) {
+      nn.fit(iter);
+      nn2.fit(iter);
+      if(i%20==0) log.info("Score: {} vs. {}", nn.getScore(), nn2.getScore());
+    }
+
+    Evaluation eval = nn.evaluate(iter2);
+    Evaluation eval2 = nn2.evaluate(iter2);
+
+    log.info("\n{} \n{}", eval.stats(), eval2.stats());
+  }
+}

brutex-extended-tests/src/test/java/net/brutex/gan/App.java

@@ -1,411 +1,261 @@
-/*
- *
- *    ******************************************************************************
- *    *
- *    * This program and the accompanying materials are made available under the
- *    * terms of the Apache License, Version 2.0 which is available at
- *    * https://www.apache.org/licenses/LICENSE-2.0.
- *    *
- *    *  See the NOTICE file distributed with this work for additional
- *    *  information regarding copyright ownership.
- *    * Unless required by applicable law or agreed to in writing, software
- *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *    * License for the specific language governing permissions and limitations
- *    * under the License.
- *    *
- *    * SPDX-License-Identifier: Apache-2.0
- *    *****************************************************************************
- *
- */
-
 package net.brutex.gan;
 
-import java.awt.BorderLayout;
-import java.awt.Dimension;
-import java.awt.GridLayout;
-import java.awt.Image;
+import static net.brutex.ai.dnn.api.NN.dense;
+
+import java.awt.*;
 import java.awt.image.BufferedImage;
 import java.io.File;
 import java.util.Arrays;
-import java.util.Random;
-import javax.swing.ImageIcon;
-import javax.swing.JFrame;
-import javax.swing.JLabel;
-import javax.swing.JPanel;
-import javax.swing.WindowConstants;
-import lombok.extern.slf4j.Slf4j;
+import javax.swing.*;
 import org.apache.commons.lang3.ArrayUtils;
-import org.datavec.api.split.FileSplit;
-import org.datavec.image.loader.NativeImageLoader;
-import org.datavec.image.recordreader.ImageRecordReader;
-import org.datavec.image.transform.ColorConversionTransform;
-import org.datavec.image.transform.ImageTransform;
-import org.datavec.image.transform.PipelineImageTransform;
-import org.datavec.image.transform.ResizeImageTransform;
-import org.datavec.image.transform.ShowImageTransform;
-import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
+import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.distribution.Distribution;
-import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
-import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.layers.ActivationLayer;
-import org.deeplearning4j.nn.conf.layers.DenseLayer;
-import org.deeplearning4j.nn.conf.layers.DropoutLayer;
-import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
-import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
-import org.deeplearning4j.nn.conf.weightnoise.WeightNoise;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
-import org.deeplearning4j.nn.weights.WeightInitXavier;
 import org.deeplearning4j.optimize.listeners.PerformanceListener;
-import org.deeplearning4j.optimize.listeners.ScoreToChartListener;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.impl.ActivationLReLU;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
-import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.learning.config.IUpdater;
-import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
 
-@Slf4j
 public class App {
-  private static final double LEARNING_RATE = 0.000002;
-  private static final double GRADIENT_THRESHOLD = 100.0;
+    private static final double LEARNING_RATE = 0.002;
+    private static final double GRADIENT_THRESHOLD = 100.0;
+    private static final IUpdater UPDATER = Adam.builder().learningRate(LEARNING_RATE).beta1(0.5).build();
+    private static final int BATCHSIZE = 128;
+    private static JFrame frame;
+    private static JPanel panel;
 
-  private static final int X_DIM = 20 ;
-  private static final int Y_DIM = 20;
-  private static final int CHANNELS = 1;
-  private static final int batchSize = 10;
-  private static final int INPUT = 128;
+    private static LayerConfiguration[] genLayers() {
+        return new LayerConfiguration[] {
+                dense().nIn(100).nOut(256).weightInit(WeightInit.NORMAL).build(),
+                ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+                dense().nIn(256).nOut(512).build(),
+                ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+                dense().nIn(512).nOut(1024).build(),
+                ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+                dense().nIn(1024).nOut(784).activation(Activation.TANH).build()
+        };
+    }
 
-  private static final int OUTPUT_PER_PANEL = 4;
+    /**
+     * Returns a network config that takes in a 10x10 random number and produces a 28x28 grayscale image.
+     *
+     * @return config
+     */
+    private static NeuralNetConfiguration generator() {
+        NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
+                .seed(42)
+                .updater(UPDATER)
+                .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
+                .gradientNormalizationThreshold(GRADIENT_THRESHOLD)
+                .weightInit(WeightInit.XAVIER)
+                .activation(Activation.IDENTITY)
+                .layersFromArray(genLayers())
+                .name("generator")
+                .build();
 
-  private static final int ARRAY_SIZE_PER_SAMPLE = X_DIM*Y_DIM*CHANNELS;
-  private static final IUpdater UPDATER = Adam.builder().learningRate(LEARNING_RATE).beta1(0.5).build();
+        return conf;
+    }
 
-  private  static JFrame frame;
-  private static  JFrame frame2;
-  private static JPanel panel;
-  private static JPanel panel2;
+    private static LayerConfiguration[] disLayers() {
+        return new LayerConfiguration[]{
+                dense().nIn(784).nOut(1024).build(),
+                ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+                DropoutLayer.builder(1 - 0.5).build(),
+                dense().nIn(1024).nOut(512).build(),
+                ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+                DropoutLayer.builder(1 - 0.5).build(),
+                dense().nIn(512).nOut(256).build(),
+                ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+                DropoutLayer.builder(1 - 0.5).build(),
+                OutputLayer.builder(LossFunctions.LossFunction.XENT).nIn(256).nOut(1).activation(Activation.SIGMOID).build()
+        };
+    }
 
-  private static LayerConfiguration[] genLayers() {
-    return new LayerConfiguration[] {
-        DenseLayer.builder().nIn(INPUT).nOut(X_DIM*Y_DIM*CHANNELS).weightInit(WeightInit.NORMAL).build(),
-        ActivationLayer.builder(Activation.LEAKYRELU).build(),
+    private static NeuralNetConfiguration discriminator() {
+        NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
+                .seed(42)
+                .updater(UPDATER)
+                .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
+                .gradientNormalizationThreshold(GRADIENT_THRESHOLD)
+                .weightInit(WeightInit.XAVIER)
+                .activation(Activation.IDENTITY)
+                .layersFromArray(disLayers())
+                .name("discriminator")
+                .build();
 
-        DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
-        ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
-        DenseLayer.builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM).build(),
-        ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+        return conf;
+    }
 
-        DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM*CHANNELS).activation(Activation.TANH).build()
-    };
- }
+    private static NeuralNetConfiguration gan() {
+        LayerConfiguration[] genLayers = genLayers();
+        LayerConfiguration[] disLayers = discriminator().getFlattenedLayerConfigurations().stream()
+                .map((layer) -> {
+                    if (layer instanceof DenseLayer || layer instanceof OutputLayer) {
+                        return FrozenLayerWithBackprop.builder(layer).build();
+                    } else {
+                        return layer;
+                    }
+                }).toArray(LayerConfiguration[]::new);
+        LayerConfiguration[] layers = ArrayUtils.addAll(genLayers, disLayers);
 
-  /**
-   * Returns a network config that takes in a 10x10 random number and produces a 28x28 grayscale image.
-   *
-   * @return config
-   */
-  private static NeuralNetConfiguration generator() {
-    NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
-        .seed(42)
-        .updater(UPDATER)
-        .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
-        .gradientNormalizationThreshold(GRADIENT_THRESHOLD)
-        //.weightInit(WeightInit.XAVIER)
-        .weightInit(WeightInit.XAVIER)
-        .activation(Activation.IDENTITY)
-        .layersFromArray(genLayers())
-        .inputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
-       // .inputPreProcessor("CNN1", new FeedForwardToCnnPreProcessor(Y_DIM, X_DIM, CHANNELS))
-        .build();
-    ((NeuralNetConfiguration) conf).init();
+        NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
+                .seed(42)
+                .updater(UPDATER)
+                .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
+                .gradientNormalizationThreshold(GRADIENT_THRESHOLD)
+                .weightInit(WeightInit.XAVIER)
+                .activation(Activation.IDENTITY)
+                .layersFromArray(layers)
+                .name("GAN")
+                .build();
 
-   return conf;
-  }
+        return conf;
+    }
 
-  private static LayerConfiguration[] disLayers() {
-    return new LayerConfiguration[]{
-        DenseLayer.builder().name("1.Dense").nOut(X_DIM*Y_DIM*CHANNELS).build(), //input is set by setInputType on the network
-        ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
-        DropoutLayer.builder(1 - 0.5).build(),
-        DenseLayer.builder().name("2.Dense").nIn(X_DIM * Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM*CHANNELS*4).build(), //HxBxC
-        ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
-        DropoutLayer.builder(1 - 0.5).build(),
-        DenseLayer.builder().name("3.Dense").nIn(X_DIM*Y_DIM*CHANNELS*4).nOut(X_DIM*Y_DIM*CHANNELS).build(),
-        ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
-        DropoutLayer.builder(1 - 0.5).build(),
-        DenseLayer.builder().name("4.Dense").nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
-        ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
-        DropoutLayer.builder(1 - 0.5).build(),
+    @Test
+    public void runTest() throws Exception {
+        App.main(null);
+    }
+    public static void main(String... args) throws Exception {
+        Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);
 
-        OutputLayer.builder().name("dis-output").lossFunction(LossFunction.XENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build()
-    };
-  }
+        MnistDataSetIterator trainData = new MnistDataSetIterator(BATCHSIZE, true, 42);
 
-  private static NeuralNetConfiguration discriminator() {
+        MultiLayerNetwork gen = new MultiLayerNetwork(generator());
+        MultiLayerNetwork dis = new MultiLayerNetwork(discriminator());
+        MultiLayerNetwork gan = new MultiLayerNetwork(gan());
+        gen.init();
+        dis.init();
+        gan.init();
 
-    NeuralNetConfiguration conf =
-        NeuralNetConfiguration.builder()
-            .seed(42)
-            .updater(UPDATER)
-            .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
-            .gradientNormalizationThreshold(GRADIENT_THRESHOLD)
-            .weightInit(WeightInit.XAVIER)
-            //.weightNoise(new WeightNoise(new NormalDistribution(0.5, 0.5)))
-                .weightNoise(null)
-            // .weightInitFn(new WeightInitXavier())
-            // .activationFn(new ActivationIdentity())
-            .activation(Activation.IDENTITY)
-            .layersFromArray(disLayers())
-            .inputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
-            .build();
-    ((NeuralNetConfiguration) conf).init();
+        copyParams(gen, dis, gan);
 
-    return conf;
-  }
+        gen.addTrainingListeners(new PerformanceListener(10, true));
+        dis.addTrainingListeners(new PerformanceListener(10, true));
+        gan.addTrainingListeners(new PerformanceListener(10, true));
 
-  private static NeuralNetConfiguration gan() {
-    LayerConfiguration[] genLayers = genLayers();
-    LayerConfiguration[] disLayers = Arrays.stream(disLayers())
-        .map((layer) -> {
-         if (layer instanceof DenseLayer || layer instanceof OutputLayer) {
-          return FrozenLayerWithBackprop.builder(layer).name("frozen-for-"+layer.getName()).build();
-          } else {
-            return layer;
-          }
-        }).toArray(LayerConfiguration[]::new);
-    LayerConfiguration[] layers = ArrayUtils.addAll(genLayers, disLayers);
+        trainData.reset();
 
-    NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
-        .seed(42)
-        .updater( Adam.builder().learningRate(0.0002).beta1(0.5).build() )
-        .gradientNormalization( GradientNormalization.RenormalizeL2PerLayer)
-        .gradientNormalizationThreshold( 100 )
-        //.weightInitFn( new WeightInitXavier() ) //this is internal
-            .weightNoise(new WeightNoise(new NormalDistribution(0.5, 0.5)))
-        .weightInit( WeightInit.XAVIER)
-        //.activationFn( new ActivationIdentity()) //this is internal
-        .activation( Activation.IDENTITY )
-        .layersFromArray(  layers  )
-        .inputType( InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
-        .build();
-((NeuralNetConfiguration) conf).init();
-    return conf;
-  }
+        int j = 0;
+        for (int i = 0; i < 50; i++) {
+            while (trainData.hasNext()) {
+                j++;
+
+                // generate data
+                INDArray real = trainData.next().getFeatures().muli(2).subi(1);
+                int batchSize = (int) real.shape()[0];
+
+                INDArray fakeIn = Nd4j.rand(batchSize, 100);
+                INDArray fake = gan.activateSelectedLayers(0, gen.getLayers().length - 1, fakeIn);
+
+                DataSet realSet = new DataSet(real, Nd4j.zeros(batchSize, 1));
+                DataSet fakeSet = new DataSet(fake, Nd4j.ones(batchSize, 1));
+
+                DataSet data = DataSet.merge(Arrays.asList(realSet, fakeSet));
+
+                dis.fit(data);
+                dis.fit(data);
+
+                // Update the discriminator in the GAN network
+                updateGan(gen, dis, gan);
+
+                gan.fit(new DataSet(Nd4j.rand(batchSize, 100), Nd4j.zeros(batchSize, 1)));
 
 
-  @Test
-  public void runTest() throws Exception {
-    main();
-  }
+                if (j % 10 == 1) {
+                    System.out.println("Epoch " + i +" Iteration " + j + " Visualizing...");
+                    INDArray[] samples = new INDArray[9];
+                    DataSet fakeSet2 = new DataSet(fakeIn, Nd4j.ones(batchSize, 1));
 
-  public static void main(String... args) throws Exception {
+                    for (int k = 0; k < 9; k++) {
+                        INDArray input = fakeSet2.get(k).getFeatures();
+                        //samples[k] = gen.output(input, false);
+                        samples[k] = gan.activateSelectedLayers(0, gen.getLayers().length - 1, input);
 
-    log.info("\u001B[32m  Some \u001B[1m green \u001B[22m text \u001B[0m \u001B[7m Inverted\u001B[0m   ");
-    Nd4j.getMemoryManager().setAutoGcWindow(500);
-
-//    MnistDataSetIterator trainData = new MnistDataSetIterator(128, true, 45);
-  //  FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/flowers"), NativeImageLoader.getALLOWED_FORMATS());
-    FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/humans"), NativeImageLoader.getALLOWED_FORMATS());
-
-
-    ImageTransform transform = new ColorConversionTransform(new Random(42), 7 );
-
-    ImageTransform transform2 = new ShowImageTransform("Tester", 30);
-    ImageTransform transform3 = new ResizeImageTransform(X_DIM, Y_DIM);
-
-    ImageTransform tr = new PipelineImageTransform.Builder()
-        .addImageTransform(transform) //convert to GREY SCALE
-        .addImageTransform(transform3)
-        //.addImageTransform(transform2)
-        .build();
-
-    ImageRecordReader imageRecordReader = new ImageRecordReader(X_DIM, Y_DIM, CHANNELS);
-    imageRecordReader.initialize(fileSplit, tr);
-    DataSetIterator trainData = new RecordReaderDataSetIterator(imageRecordReader, batchSize );
-
-    MultiLayerNetwork gen = new MultiLayerNetwork(generator());
-    MultiLayerNetwork dis = new MultiLayerNetwork(discriminator());
-    MultiLayerNetwork gan = new MultiLayerNetwork(gan());
-    gen.init(); log.debug("Generator network: {}", gen);
-    dis.init(); log.debug("Discriminator network: {}", dis);
-    gan.init(); log.debug("Complete GAN network: {}", gan);
-
-
-    copyParams(gen, dis, gan);
-
-    gen.addTrainingListeners(new PerformanceListener(15, true));
-    //dis.addTrainingListeners(new PerformanceListener(10, true));
-    //gan.addTrainingListeners(new PerformanceListener(10, true));
-    //gan.addTrainingListeners(new ScoreToChartListener("gan"));
-    //dis.setListeners(new ScoreToChartListener("dis"));
-
-    System.out.println(gan.toString());
-    gan.fit(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1));
-
-    //gan.fit(new DataSet(trainData.next().getFeatures(), Nd4j.zeros(batchSize, 1)));
-    //trainData.reset();
-
-    int j = 0;
-    for (int i = 0; i < 201; i++) { //epoch
-      while (trainData.hasNext()) {
-        j++;
-
-        DataSet next = trainData.next();
-        // generate data
-        INDArray real = next.getFeatures();//.div(255f);
-
-        //start next round if there are not enough images left to have a full batchsize dataset
-        if(real.length() < ARRAY_SIZE_PER_SAMPLE*batchSize) {
-          log.warn("Your total number of input images is not a multiple of {}, "
-              + "thus skipping {} images to make it fit", batchSize, real.length()/ARRAY_SIZE_PER_SAMPLE);
-        break;
+                    }
+                    visualize(samples);
+                }
+            }
+            trainData.reset();
+            // Copy the GANs generator to gen.
+            //updateGen(gen, gan);
         }
 
-        if(i%20 == 0) {
-         // frame2 = visualize(new INDArray[]{real}, batchSize,
-         //     frame2 == null ? new JFrame() : frame2, true); //real has batchsize number of images
+        // Copy the GANs generator to gen.
+        updateGen(gen, gan);
+
+        gen.save(new File("mnist-mlp-generator.dlj"));
+    }
+
+    private static void copyParams(MultiLayerNetwork gen, MultiLayerNetwork dis, MultiLayerNetwork gan) {
+        int genLayerCount = gen.getLayers().length;
+        for (int i = 0; i < gan.getLayers().length; i++) {
+            if (i < genLayerCount) {
+                gen.getLayer(i).setParams(gan.getLayer(i).getParams());
+            } else {
+                dis.getLayer(i - genLayerCount).setParams(gan.getLayer(i).getParams());
+            }
         }
-       real.divi(255f);
+    }
 
-//        int batchSize = (int) real.shape()[0];
-
-        INDArray fakeIn = Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM);
-        INDArray fake = gan.activateSelectedLayers(0, gen.getLayers().length - 1, fakeIn);
-        fake = fake.reshape(batchSize, CHANNELS, X_DIM, Y_DIM);
-
-        //log.info("real has {} items.", real.length());
-        DataSet realSet = new DataSet(real, Nd4j.zeros(batchSize, 1));
-        DataSet fakeSet = new DataSet(fake, Nd4j.ones(batchSize, 1));
-
-
-        DataSet data = DataSet.merge(Arrays.asList(realSet, fakeSet));
-
-        dis.fit(data);
-        dis.fit(data);
-
-        // Update the discriminator in the GAN network
-        updateGan(gen, dis, gan);
-
-        //gan.fit(new DataSet(Nd4j.rand(batchSize, INPUT), Nd4j.zeros(batchSize, 1)));
-        gan.fit(new DataSet(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1)));
-
-
-        if (j % 10 == 1) {
-          System.out.println("Iteration " + j + " Visualizing...");
-          INDArray[] samples = batchSize > OUTPUT_PER_PANEL ? new INDArray[OUTPUT_PER_PANEL] : new INDArray[batchSize];
-
-
-          for (int k = 0; k < samples.length; k++) {
-            //INDArray input = fakeSet2.get(k).getFeatures();
-            DataSet fakeSet2 = new DataSet(fakeIn, Nd4j.ones(batchSize, 1));
-            INDArray input = fakeSet2.get(k).getFeatures();
-            input = input.reshape(1,CHANNELS, X_DIM, Y_DIM); //batch size will be 1 here
-
-            //samples[k] = gen.output(input, false);
-            samples[k] = gan.activateSelectedLayers(0, gen.getLayers().length - 1, input);
-            samples[k] = samples[k].reshape(1, CHANNELS, X_DIM, Y_DIM);
-            //samples[k] =
-            samples[k].addi(1f).divi(2f).muli(255f);
-
-          }
-          frame = visualize(samples, 1, frame == null ? new JFrame() : frame, false); //each samples only has 1 image, thus batchElements=1
+    private static void updateGen(MultiLayerNetwork gen, MultiLayerNetwork gan) {
+        for (int i = 0; i < gen.getLayers().length; i++) {
+            gen.getLayer(i).setParams(gan.getLayer(i).getParams());
         }
-      }
-      trainData.reset();
     }
 
-    // Copy the GANs generator to gen.
-    updateGen(gen, gan);
-
-    gen.save(new File("mnist-mlp-generator.dlj"));
-  }
-
-  private static void copyParams(MultiLayerNetwork gen, MultiLayerNetwork dis, MultiLayerNetwork gan) {
-    int genLayerCount = gen.getLayers().length;
-    for (int i = 0; i < gan.getLayers().length; i++) {
-      if (i < genLayerCount) {
-        if(gan.getLayer(i).getParams() != null)
-         gen.getLayer(i).setParams(gan.getLayer(i).getParams());
-      } else {
-        if(gan.getLayer(i).getParams() != null)
-        dis.getLayer(i - genLayerCount).setParams(gan.getLayer(i).getParams());
-      }
-    }
-  }
-
-  private static void updateGen(MultiLayerNetwork gen, MultiLayerNetwork gan) {
-    for (int i = 0; i < gen.getLayers().length; i++) {
-      gen.getLayer(i).setParams(gan.getLayer(i).getParams());
-    }
-  }
-
-  private static void updateGan(MultiLayerNetwork gen, MultiLayerNetwork dis, MultiLayerNetwork gan) {
-    int genLayerCount = gen.getLayers().length;
-    for (int i = genLayerCount; i < gan.getLayers().length; i++) {
-      gan.getLayer(i).setParams(dis.getLayer(i - genLayerCount).getParams());
-    }
-  }
-
-  private static JFrame visualize(INDArray[] samples, int batchElements, JFrame frame, boolean isOrig) {
-    if (isOrig) {
-      frame.setTitle("Viz Original");
-    } else {
-      frame.setTitle("Generated");
-    }
-
-    frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
-    frame.setLayout(new BorderLayout());
-
-    JPanel panelx = new JPanel();
-
-    panelx.setLayout(new GridLayout(4, 4, 8, 8));
-    for (INDArray sample : samples) {
-      for(int i = 0; i<batchElements; i++) {
-        panelx.add(getImage(sample, i, isOrig));
-      }
-    }
-    frame.add(panelx, BorderLayout.CENTER);
-    frame.setVisible(true);
-
-    frame.revalidate();
-    frame.setMinimumSize(new Dimension(300, 20));
-    frame.pack();
-    return frame;
-  }
-
-  private static JLabel getImage(INDArray tensor, int batchElement, boolean isOrig) {
-    final BufferedImage bi = new BufferedImage(X_DIM, Y_DIM, BufferedImage.TYPE_BYTE_GRAY);
-    final int imageSize = X_DIM * Y_DIM;
-    final int offset = batchElement * imageSize;
-    int pxl = offset * CHANNELS; //where to start in the INDArray
-
-    //Image in NCHW - channels first format
-    for (int c = 0; c < CHANNELS; c++) { //step through the num channels for each pixel
-      for (int y = 0; y < Y_DIM; y++) { // step through the columns x
-        for (int x = 0; x < X_DIM; x++) { //step through the rows y
-          if(isOrig) log.trace("'{}.' Image (x,y,c): ({}, {}, {}) with INDArray with index {} and value '{}'", batchElement, x, y, c, pxl, tensor.getFloat(pxl));
-          bi.getRaster().setSample(x, y, c, tensor.getFloat(pxl));
-          pxl++; //next item in INDArray
+    private static void updateGan(MultiLayerNetwork gen, MultiLayerNetwork dis, MultiLayerNetwork gan) {
+        int genLayerCount = gen.getLayers().length;
+        for (int i = genLayerCount; i < gan.getLayers().length; i++) {
+            gan.getLayer(i).setParams(dis.getLayer(i - genLayerCount).getParams());
         }
-      }
     }
 
-    ImageIcon orig = new ImageIcon(bi);
+    private static void visualize(INDArray[] samples) {
+        if (frame == null) {
+            frame = new JFrame();
+            frame.setTitle("Viz");
+            frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
+            frame.setLayout(new BorderLayout());
 
-    Image imageScaled = orig.getImage().getScaledInstance((4 * X_DIM), (4 * Y_DIM), Image.SCALE_DEFAULT);
+            panel = new JPanel();
 
-    ImageIcon scaled = new ImageIcon(imageScaled);
+            panel.setLayout(new GridLayout(samples.length / 3, 1, 8, 8));
+            frame.add(panel, BorderLayout.CENTER);
+            frame.setVisible(true);
+        }
 
-    return new JLabel(scaled);
+        panel.removeAll();
 
-  }
+        for (INDArray sample : samples) {
+            panel.add(getImage(sample));
+        }
+
+        frame.revalidate();
+        frame.pack();
+    }
+
+    private static JLabel getImage(INDArray tensor) {
+        BufferedImage bi = new BufferedImage(28, 28, BufferedImage.TYPE_BYTE_GRAY);
+        for (int i = 0; i < 784; i++) {
+            int pixel = (int)(((tensor.getDouble(i) + 1) * 2) * 255);
+            bi.getRaster().setSample(i % 28, i / 28, 0, pixel);
+        }
+        ImageIcon orig = new ImageIcon(bi);
+        Image imageScaled = orig.getImage().getScaledInstance((8 * 28), (8 * 28), Image.SCALE_REPLICATE);
+
+        ImageIcon scaled = new ImageIcon(imageScaled);
+
+        return new JLabel(scaled);
+    }
 }

brutex-extended-tests/src/test/java/net/brutex/gan/App2.java

@@ -0,0 +1,343 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * This program and the accompanying materials are made available under the
+ *    * terms of the Apache License, Version 2.0 which is available at
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
+ *    *
+ *    *  See the NOTICE file distributed with this work for additional
+ *    *  information regarding copyright ownership.
+ *    * Unless required by applicable law or agreed to in writing, software
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * License for the specific language governing permissions and limitations
+ *    * under the License.
+ *    *
+ *    * SPDX-License-Identifier: Apache-2.0
+ *    *****************************************************************************
+ *
+ */
+
+package net.brutex.gan;
+
+
+
+import java.awt.*;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.util.*;
+import java.util.List;
+import javax.imageio.ImageIO;
+import javax.swing.*;
+import lombok.extern.slf4j.Slf4j;
+import org.datavec.api.split.FileSplit;
+import org.datavec.image.loader.NativeImageLoader;
+import org.datavec.image.recordreader.ImageRecordReader;
+import org.datavec.image.transform.*;
+import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
+import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
+import org.deeplearning4j.nn.conf.GradientNormalization;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.*;
+import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.optimize.listeners.PerformanceListener;
+import org.junit.jupiter.api.Test;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.dataset.DataSet;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.factory.Nd4j;
+
+@Slf4j
+public class App2 {
+
+    final int INPUT = CHANNELS*DIMENSIONS*DIMENSIONS;
+    static final float COLORSPACE = 255f;
+    static final int DIMENSIONS = 28;
+    static final int CHANNELS = 1;
+    final int ARRAY_SIZE_PER_SAMPLE = DIMENSIONS*DIMENSIONS*CHANNELS;
+    final int OUTPUT_PER_PANEL = 10;
+
+    final boolean BIAS = true;
+
+    static final int BATCHSIZE=128;
+
+    private JFrame frame2, frame;
+    static final String OUTPUT_DIR = "d:/out/";
+
+    final static INDArray label_real = Nd4j.ones(BATCHSIZE, 1);
+    final static INDArray label_fake = Nd4j.zeros(BATCHSIZE, 1);
+
+    @Test
+    void runTest() throws IOException {
+        Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);
+
+        MnistDataSetIterator mnistIter = new MnistDataSetIterator(20, 200);
+        FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/humans2"), NativeImageLoader.getALLOWED_FORMATS());
+        ImageTransform transform = new ColorConversionTransform(new Random(42), 7 );
+        ImageTransform transform2 = new ShowImageTransform("Tester", 30);
+        ImageTransform transform3 = new ResizeImageTransform(DIMENSIONS, DIMENSIONS);
+
+        ImageTransform tr = new PipelineImageTransform.Builder()
+               .addImageTransform(transform) //convert to GREY SCALE
+                .addImageTransform(transform3)
+                //.addImageTransform(transform2)
+                .build();
+
+        ImageRecordReader imageRecordReader = new ImageRecordReader(DIMENSIONS, DIMENSIONS, CHANNELS);
+        imageRecordReader.initialize(fileSplit, tr);
+        DataSetIterator trainData = new RecordReaderDataSetIterator(imageRecordReader, BATCHSIZE );
+        trainData = new MnistDataSetIterator(BATCHSIZE, true, 42);
+
+        MultiLayerNetwork dis = new MultiLayerNetwork(App2Config.discriminator());
+        MultiLayerNetwork gen = new MultiLayerNetwork(App2Config.generator());
+
+        LayerConfiguration[] disLayers = App2Config.discriminator().getFlattenedLayerConfigurations().stream()
+                .map((layer) -> {
+                    if (layer instanceof DenseLayer || layer instanceof OutputLayer) {
+                        return FrozenLayerWithBackprop.builder(layer).name("frozen-for-"+layer.getName()).build();
+                    } else {
+                        return layer;
+                    }
+                }).toArray(LayerConfiguration[]::new);
+
+    NeuralNetConfiguration netConfiguration =
+        NeuralNetConfiguration.builder()
+            .name("GAN")
+            .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
+                .gradientNormalizationThreshold(100)
+                .updater(App2Config.UPDATER)
+            .innerConfigurations(new ArrayList<>(List.of(App2Config.generator())))
+            .layersFromList(new ArrayList<>(Arrays.asList(disLayers)))
+            // .inputType(InputType.convolutional(DIMENSIONS, DIMENSIONS, CHANNELS))
+            // .inputPreProcessor(4, new CnnToFeedForwardPreProcessor())
+            //.inputPreProcessor(0, new CnnToFeedForwardPreProcessor())
+           // .inputPreProcessor(2, new FeedForwardToCnnPreProcessor(DIMENSIONS, DIMENSIONS, CHANNELS))
+                //.inputPreProcessor(2, new CnnToFeedForwardPreProcessor())
+                //.dataType(DataType.FLOAT)
+            .build();
+
+        MultiLayerNetwork gan = new MultiLayerNetwork(netConfiguration );
+
+        dis.init(); log.debug("Discriminator network: {}", dis);
+        gen.init(); log.debug("Generator network: {}", gen);
+        gan.init(); log.debug("GAN network: {}", gan);
+
+
+        log.info("Generator Summary:\n{}", gen.summary());
+        log.info("GAN Summary:\n{}", gan.summary());
+        dis.addTrainingListeners(new PerformanceListener(10, true, "DIS"));
+        gen.addTrainingListeners(new PerformanceListener(10, true, "GEN"));
+        gan.addTrainingListeners(new PerformanceListener(10, true, "GAN"));
+
+        int j = 0;
+        for (int i = 0; i < 51; i++) { //epoch
+            while (trainData.hasNext()) {
+                j++;
+                DataSet next = trainData.next();
+                // generate data
+                INDArray real = next.getFeatures(); //.muli(2).subi(1);;//.div(255f);
+
+                //start next round if there are not enough images left to have a full batchsize dataset
+                if(real.length() < ARRAY_SIZE_PER_SAMPLE*BATCHSIZE) {
+                    log.warn("Your total number of input images is not a multiple of {}, "
+                            + "thus skipping {} images to make it fit", BATCHSIZE, real.length()/ARRAY_SIZE_PER_SAMPLE);
+                    break;
+                }
+
+                //if(i%20 == 0) {
+
+               // frame2 = visualize(new INDArray[]{real}, BATCHSIZE,
+               //         frame2 == null ? new JFrame() : frame2, true); //real has batchsize number of images
+                //}
+                //real.divi(255f);
+
+//        int batchSize = (int) real.shape()[0];
+
+                //INDArray fakeIn = Nd4j.rand(BATCHSIZE, CHANNELS, DIMENSIONS, DIMENSIONS);
+                //INDArray fakeIn = Nd4j.rand(new int[]{batchSize, X_DIM*Y_DIM}); //hack for MNIST only, use above otherwise
+                INDArray fakeIn = Nd4j.rand(BATCHSIZE, App2Config.INPUT);
+
+                INDArray fake = gan.activateSelectedLayers(0, gen.getLayers().length - 1, fakeIn);
+                // when generator has TANH as activation - value range is -1 to 1
+                // when generator has SIGMOID, then range is 0 to 1
+                fake.addi(1f).divi(2f);
+
+                DataSet realSet = new DataSet(real, label_real);
+                DataSet fakeSet = new DataSet(fake, label_fake);
+
+                DataSet data = DataSet.merge(Arrays.asList(realSet, fakeSet));
+
+                dis.fit(data);
+                dis.fit(data);
+                // Update the discriminator in the GAN network
+                updateGan(gen, dis, gan);
+
+                gan.fit(new DataSet(Nd4j.rand(BATCHSIZE, App2Config.INPUT), label_fake));
+
+                //Visualize and reporting
+                if (j % 10 == 1) {
+                    System.out.println("Epoch " + i + " Iteration " + j + " Visualizing...");
+                    INDArray[] samples = BATCHSIZE > OUTPUT_PER_PANEL ? new INDArray[OUTPUT_PER_PANEL] : new INDArray[BATCHSIZE];
+
+
+                    for (int k = 0; k < samples.length; k++) {
+                        DataSet fakeSet2 = new DataSet(fakeIn, label_fake);
+                        INDArray input = fakeSet2.get(k).getFeatures();
+
+                        //input = input.reshape(1,CHANNELS, DIMENSIONS, DIMENSIONS); //batch size will be 1 here for images
+                        input = input.reshape(1, App2Config.INPUT);
+
+                        //samples[k] = gen.output(input, false);
+                        samples[k] = gen.activateSelectedLayers(0, gen.getLayers().length - 1, input);
+                        samples[k] = samples[k].reshape(1, CHANNELS, DIMENSIONS, DIMENSIONS);
+                        //samples[k] =
+                        //samples[k].muli(255f);
+
+                    }
+                    frame = visualize(samples, 1, frame == null ? new JFrame() : frame, false); //each samples only has 1 image, thus batchElements=1
+                }
+            }
+
+            if (trainData.resetSupported()) {
+                trainData.reset();
+            } else {
+                log.error("Trainingdata {} does not support reset.", trainData.toString());
+            }
+            // Copy the GANs generator to gen.
+            updateGen(gen, gan);
+            log.info("Updated GAN's generator from gen.");
+            gen.save(new File("mnist-mlp-generator.dlj"));
+        }
+    }
+
+
+
+
+
+
+
+
+
+
+
+    private static JFrame visualize(INDArray[] samples, int batchElements, JFrame frame, boolean isOrig) {
+        if (isOrig) {
+            frame.setTitle("Viz Original");
+        } else {
+            frame.setTitle("Generated");
+        }
+
+        frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
+        frame.setLayout(new BorderLayout());
+
+        JPanel panelx = new JPanel();
+
+        panelx.setLayout(new GridLayout(4, 4, 8, 8));
+        for (INDArray sample : samples) {
+            for(int i = 0; i<batchElements; i++) {
+                panelx.add(getImage(sample, i, isOrig));
+            }
+        }
+        frame.add(panelx, BorderLayout.CENTER);
+        frame.setVisible(true);
+
+        frame.revalidate();
+        frame.setMinimumSize(new Dimension(300, 20));
+        frame.pack();
+        return frame;
+    }
+
+
+    private static JLabel getImage(INDArray tensor, int batchElement, boolean isOrig) {
+        final BufferedImage bi;
+        if(CHANNELS >1) {
+            bi = new BufferedImage(DIMENSIONS, DIMENSIONS, BufferedImage.TYPE_INT_RGB); //need to change here based on channels
+        } else {
+            bi = new BufferedImage(DIMENSIONS, DIMENSIONS, BufferedImage.TYPE_BYTE_GRAY); //need to change here based on channels
+        }
+        final int imageSize = DIMENSIONS * DIMENSIONS;
+        final int offset = batchElement * imageSize;
+        int pxl = offset * CHANNELS; //where to start in the INDArray
+
+        //Image in NCHW - channels first format
+        for (int c = 0; c < CHANNELS; c++) { //step through the num channels for each pixel
+            for (int y = 0; y < DIMENSIONS; y++) { // step through the columns x
+                for (int x = 0; x < DIMENSIONS; x++) { //step through the rows y
+                    float f_pxl = tensor.getFloat(pxl) * COLORSPACE;
+                    if(isOrig) log.trace("'{}.' Image (x,y,c): ({}, {}, {}) with INDArray with index {} and value '{}'", batchElement, x, y, c, pxl, f_pxl);
+                    bi.getRaster().setSample(x, y, c, f_pxl);
+                    pxl++; //next item in INDArray
+                }
+            }
+        }
+        ImageIcon orig = new ImageIcon(bi);
+        Image imageScaled = orig.getImage().getScaledInstance((4 * DIMENSIONS), (4 * DIMENSIONS), Image.SCALE_DEFAULT);
+        ImageIcon scaled = new ImageIcon(imageScaled);
+        if(! isOrig)  saveImage(imageScaled, batchElement, isOrig);
+        return new JLabel(scaled);
+
+    }
+
+    private static void saveImage(Image image, int batchElement, boolean isOrig) {
+        String outputDirectory = OUTPUT_DIR; // Set the output directory where the images will be saved
+
+        try {
+            // Save the images to disk
+            saveImage(image, outputDirectory, UUID.randomUUID().toString()+".png");
+
+            log.debug("Images saved successfully.");
+        } catch (IOException e) {
+            log.error("Error saving the images: {}", e.getMessage());
+        }
+    }
+    private static void saveImage(Image image, String outputDirectory, String fileName) throws IOException {
+        File directory = new File(outputDirectory);
+        if (!directory.exists()) {
+            directory.mkdir();
+        }
+
+        File outputFile = new File(directory, fileName);
+        ImageIO.write(imageToBufferedImage(image), "png", outputFile);
+    }
+
+    public static BufferedImage imageToBufferedImage(Image image) {
+        if (image instanceof BufferedImage) {
+            return (BufferedImage) image;
+        }
+
+    // Create a buffered image with the same dimensions and transparency as the original image
+        BufferedImage bufferedImage;
+    if (CHANNELS > 1) {
+      bufferedImage =
+          new BufferedImage(
+              image.getWidth(null), image.getHeight(null), BufferedImage.TYPE_INT_ARGB);
+        } else {
+        bufferedImage =
+                new BufferedImage(
+                        image.getWidth(null), image.getHeight(null), BufferedImage.TYPE_BYTE_GRAY);
+        }
+
+        // Draw the original image onto the buffered image
+        Graphics2D g2d = bufferedImage.createGraphics();
+        g2d.drawImage(image, 0, 0, null);
+        g2d.dispose();
+
+        return bufferedImage;
+    }
+    private static void updateGen(MultiLayerNetwork gen, MultiLayerNetwork gan) {
+        for (int i = 0; i < gen.getLayers().length; i++) {
+            gen.getLayer(i).setParams(gan.getLayer(i).getParams());
+        }
+    }
+
+    private static void updateGan(MultiLayerNetwork gen, MultiLayerNetwork dis, MultiLayerNetwork gan) {
+        int genLayerCount = gen.getLayers().length;
+        for (int i = genLayerCount; i < gan.getLayers().length; i++) {
+            gan.getLayer(i).setParams(dis.getLayer(i - genLayerCount).getParams());
+        }
+    }
+
+}

brutex-extended-tests/src/test/java/net/brutex/gan/App2Config.java

@@ -0,0 +1,176 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * This program and the accompanying materials are made available under the
+ *    * terms of the Apache License, Version 2.0 which is available at
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
+ *    *
+ *    *  See the NOTICE file distributed with this work for additional
+ *    *  information regarding copyright ownership.
+ *    * Unless required by applicable law or agreed to in writing, software
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * License for the specific language governing permissions and limitations
+ *    * under the License.
+ *    *
+ *    * SPDX-License-Identifier: Apache-2.0
+ *    *****************************************************************************
+ *
+ */
+
+package net.brutex.gan;
+
+import static net.brutex.ai.dnn.api.NN.*;
+
+import org.deeplearning4j.nn.conf.GradientNormalization;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.*;
+import org.deeplearning4j.nn.weights.WeightInit;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.activations.impl.ActivationLReLU;
+import org.nd4j.linalg.learning.config.Adam;
+import org.nd4j.linalg.learning.config.IUpdater;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+public class App2Config {
+
+  public static final int INPUT = 100;
+  public static final int X_DIM = 28;
+  public static final int y_DIM = 28;
+  public static final int CHANNELS = 1;
+  public static final IUpdater UPDATER = Adam.builder().learningRate(0.0002).beta1(0.5).build();
+
+  static LayerConfiguration[] genLayerConfig() {
+    return new LayerConfiguration[] {
+            /*
+      DenseLayer.builder().name("L-0").nIn(INPUT).nOut(INPUT + (INPUT / 2)).activation(Activation.RELU).build(),
+      ActivationLayer.builder().activation(Activation.RELU).build(), /*
+                Deconvolution2D.builder().name("L-Deconv-01").nIn(CHANNELS).nOut(CHANNELS)
+                        .kernelSize(2,2)
+                        .stride(1,1)
+                        .padding(0,0)
+                        .convolutionMode(ConvolutionMode.Truncate)
+                        .activation(Activation.RELU)
+                        .hasBias(BIAS).build(),
+                //BatchNormalization.builder().nOut(CHANNELS).build(),
+                Deconvolution2D.builder().name("L-Deconv-02").nIn(CHANNELS).nOut(CHANNELS)
+                        .kernelSize(2,2)
+                        .stride(2,2)
+                        .padding(0,0)
+                        .convolutionMode(ConvolutionMode.Truncate)
+                        .activation(Activation.RELU)
+                        .hasBias(BIAS).build(),
+               //BatchNormalization.builder().name("L-batch").nOut(CHANNELS).build(),
+
+
+      DenseLayer.builder().name("L-x").nIn(INPUT + (INPUT / 2)).nOut(2 * INPUT).build(),
+      ActivationLayer.builder().activation(Activation.RELU).dropOut(0.2).build(),
+      DenseLayer.builder().name("L-x").nIn(2 * INPUT).nOut(3 * INPUT).build(),
+      ActivationLayer.builder().activation(Activation.RELU).dropOut(0.2).build(),
+      DenseLayer.builder().name("L-x").nIn(3 * INPUT).nOut(2 * INPUT).build(),
+      ActivationLayer.builder().activation(Activation.RELU).dropOut(0.2).build(),
+      // DropoutLayer.builder(0.001).build(),
+      DenseLayer.builder().nIn(2 * INPUT).nOut(INPUT).activation(Activation.TANH).build()    */
+
+            dense().nIn(INPUT).nOut(256).weightInit(WeightInit.NORMAL).build(),
+            ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+            dense().nIn(256).nOut(512).build(),
+            ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+            dense().nIn(512).nOut(1024).build(),
+            ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+            dense().nIn(1024).nOut(784).activation(Activation.TANH).build(),
+
+    };
+  }
+
+  static LayerConfiguration[] disLayerConfig() {
+    return new LayerConfiguration[] {/*
+                Convolution2D.builder().nIn(CHANNELS).kernelSize(2,2).padding(1,1).stride(1,1).nOut(CHANNELS)
+                        .build(),
+                Convolution2D.builder().nIn(CHANNELS).kernelSize(3,3).padding(1,1).stride(2,2).nOut(CHANNELS)
+                        .build(),
+                ActivationLayer.builder().activation(Activation.LEAKYRELU).build(),
+                BatchNormalization.builder().build(),
+                OutputLayer.builder().nOut(1).lossFunction(LossFunctions.LossFunction.MCXENT)
+                        .activation(Activation.SIGMOID)
+                        .build()
+
+
+            dense().name("L-dense").nIn(INPUT).nOut(INPUT).build(),
+            ActivationLayer.builder().activation(Activation.RELU).build(),
+            DropoutLayer.builder(0.5).build(),
+
+            DenseLayer.builder().nIn(INPUT).nOut(INPUT/2).build(),
+            ActivationLayer.builder().activation(Activation.RELU).build(),
+            DropoutLayer.builder(0.5).build(),
+
+            DenseLayer.builder().nIn(INPUT/2).nOut(INPUT/4).build(),
+            ActivationLayer.builder().activation(Activation.RELU).build(),
+            DropoutLayer.builder(0.5).build(),
+
+            OutputLayer.builder().nIn(INPUT/4).nOut(1).lossFunction(LossFunctions.LossFunction.XENT)
+                    .activation(Activation.SIGMOID)
+                    .build() */
+            dense().nIn(784).nOut(1024).hasBias(true).build(),
+            ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+            DropoutLayer.builder(1 - 0.5).build(),
+            dense().nIn(1024).nOut(512).hasBias(true).build(),
+            ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+            DropoutLayer.builder(1 - 0.5).build(),
+            dense().nIn(512).nOut(256).hasBias(true).build(),
+            ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
+            DropoutLayer.builder(1 - 0.5).build(),
+            OutputLayer.builder(LossFunctions.LossFunction.XENT).nIn(256).nOut(1).activation(Activation.SIGMOID).build()
+    };
+  }
+
+
+  static NeuralNetConfiguration generator() {
+    NeuralNetConfiguration conf =
+            NeuralNetConfiguration.builder()
+                    .name("generator")
+                    .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
+                    .gradientNormalizationThreshold(100)
+                    .seed(42)
+                    .updater(UPDATER)
+                    .weightInit(WeightInit.XAVIER)
+                    //.weightNoise(new WeightNoise(new NormalDistribution(0.5, 0.5)))
+                    .weightNoise(null)
+                    // .weightInitFn(new WeightInitXavier())
+                    // .activationFn(new ActivationIdentity())
+                    .activation(Activation.IDENTITY)
+                    .layersFromArray(App2Config.genLayerConfig())
+                    // .inputType(InputType.convolutional(DIMENSIONS, DIMENSIONS, CHANNELS))
+                    //.inputPreProcessor(0, new CnnToFeedForwardPreProcessor())
+                    //.inputPreProcessor(2, new FeedForwardToCnnPreProcessor(DIMENSIONS, DIMENSIONS, CHANNELS))
+                    //.inputPreProcessor(4, new CnnToFeedForwardPreProcessor())
+
+                    .build();
+    conf.init();
+    return conf;
+  }
+
+  static NeuralNetConfiguration discriminator() {
+    NeuralNetConfiguration conf =
+            NeuralNetConfiguration.builder()
+                    .name("discriminator")
+                    .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
+                    .gradientNormalizationThreshold(100)
+                    .seed(42)
+                    .updater(UPDATER)
+                    .weightInit(WeightInit.XAVIER)
+                    // .weightNoise(new WeightNoise(new NormalDistribution(0.5, 0.5)))
+                    .weightNoise(null)
+                    // .weightInitFn(new WeightInitXavier())
+                    // .activationFn(new ActivationIdentity())
+                    .activation(Activation.IDENTITY)
+                    .layersFromArray(disLayerConfig())
+                    //.inputPreProcessor(0, new FeedForwardToCnnPreProcessor(DIMENSIONS, DIMENSIONS, CHANNELS))
+                    //.inputPreProcessor(0, new CnnToFeedForwardPreProcessor())
+                    //.dataType(DataType.FLOAT)
+                    .build();
+    conf.init();
+    return conf;
+  }
+}

brutex-extended-tests/src/test/java/net/brutex/gan/MnistSimpleGAN.java

@@ -24,12 +24,14 @@ package net.brutex.gan;
 import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.ActivationLayer;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.DropoutLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
+import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.impl.ActivationLReLU;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -98,7 +100,10 @@ public class MnistSimpleGAN {
 
     return new MultiLayerNetwork(discConf);
   }
-
+  @Test
+  public void runTest() throws Exception {
+    main(null);
+  }
   public static void main(String[] args) throws Exception {
     GAN gan = new GAN.Builder()
         .generator(MnistSimpleGAN::getGenerator)
@@ -108,6 +113,7 @@ public class MnistSimpleGAN {
         .updater(UPDATER)
         .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
         .gradientNormalizationThreshold(100)
+
         .build();
 
     Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);

brutex-extended-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/CNN1DTestCases.java

@@ -88,7 +88,7 @@ public class CNN1DTestCases {
                         .convolutionMode(ConvolutionMode.Same))
                         .graphBuilder()
                         .addInputs("in")
-                        .layer("0", Convolution1DLayer.builder().nOut(32).activation(Activation.TANH).kernelSize(3).stride(1).build(), "in")
+                        .layer("0", Convolution1D.builder().nOut(32).activation(Activation.TANH).kernelSize(3).stride(1).build(), "in")
                         .layer("1", Subsampling1DLayer.builder().kernelSize(2).stride(1).poolingType(SubsamplingLayer.PoolingType.MAX.toPoolingType()).build(), "0")
                         .layer("2", Cropping1D.builder(1).build(), "1")
                         .layer("3", ZeroPadding1DLayer.builder(1).build(), "2")

brutex-extended-tests/src/test/resources/simplelogger.properties

@@ -25,7 +25,7 @@
 # Default logging detail level for all instances of SimpleLogger.
 # Must be one of ("trace", "debug", "info", "warn", or "error").
 # If not specified, defaults to "info".
-org.slf4j.simpleLogger.defaultLogLevel=trace
+org.slf4j.simpleLogger.defaultLogLevel=debug
 
 # Logging detail level for a SimpleLogger instance named "xxxxx".
 # Must be one of ("trace", "debug", "info", "warn", or "error").
@@ -42,8 +42,8 @@ org.slf4j.simpleLogger.defaultLogLevel=trace
 # If the format is not specified or is invalid, the default format is used.
 # The default format is yyyy-MM-dd HH:mm:ss:SSS Z.
 #org.slf4j.simpleLogger.dateTimeFormat=yyyy-MM-dd HH:mm:ss:SSS Z
-org.slf4j.simpleLogger.dateTimeFormat=yyyy-MM-dd HH:mm:ss
+#org.slf4j.simpleLogger.dateTimeFormat=yyyy-MM-dd HH:mm:ss
 
 # Set to true if you want to output the current thread name.
 # Defaults to true.
-org.slf4j.simpleLogger.showThreadName=true
+#org.slf4j.simpleLogger.showThreadName=true

cavis-common-platform/build.gradle

@@ -71,7 +71,7 @@ dependencies {
        // api "com.fasterxml.jackson.module:jackson-module-scala_${scalaVersion}"
 
 
-        api "org.projectlombok:lombok:1.18.26"
+        api "org.projectlombok:lombok:1.18.28"
 
         /*Logging*/
         api 'org.slf4j:slf4j-api:2.0.3'

cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java

@@ -2385,11 +2385,15 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     long[] stride();
 
-    /**
-     * Return the ordering (fortran or c  'f' and 'c' respectively) of this ndarray
-     * @return the ordering of this ndarray
-     */
-    char ordering();
+  /**
+   * Return the ordering (fortran or c  'f' and 'c' respectively) of this ndarray <br/><br/>
+   * C Is Contiguous layout. Mathematically speaking, row major.<br/>
+   * F Is Fortran contiguous layout. Mathematically speaking, column major.<br/>
+   * {@see https://en.wikipedia.org/wiki/Row-_and_column-major_order}<br/>
+   *
+   * @return the ordering of this ndarray
+   */
+  char ordering();
 
     /**
      * Returns the size along a specified dimension

cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/dataset/DataSet.java

@@ -334,6 +334,7 @@ public class DataSet implements org.nd4j.linalg.dataset.api.DataSet {
     public void save(File to) {
         try (FileOutputStream fos = new FileOutputStream(to, false);
                         BufferedOutputStream bos = new BufferedOutputStream(fos)) {
+            to.mkdirs();
             save(bos);
         } catch (IOException e) {
             throw new RuntimeException(e);

cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java

@@ -5121,7 +5121,7 @@ public class Nd4j {
             Nd4j.backend = backend;
             updateNd4jContext();
             props = Nd4jContext.getInstance().getConf();
-            logger.info("Properties for Nd4jContext " + props);
+            log.debug("Properties for Nd4jContext {}", props);
             PropertyParser pp = new PropertyParser(props);
 
             String otherDtype = pp.toString(ND4JSystemProperties.DTYPE);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/datasets/iterator/DataSetIteratorTest.java

@@ -166,10 +166,10 @@ public class DataSetIteratorTest extends BaseDL4JTest {
         int seed = 123;
         int listenerFreq = 1;
 
-        LFWDataSetIterator lfw = new LFWDataSetIterator(batchSize, numSamples,
+        final LFWDataSetIterator lfw = new LFWDataSetIterator(batchSize, numSamples,
                         new int[] {numRows, numColumns, numChannels}, outputNum, false, true, 1.0, new Random(seed));
 
-        NeuralNetConfiguration.NeuralNetConfigurationBuilder builder = NeuralNetConfiguration.builder().seed(seed)
+        final var builder = NeuralNetConfiguration.builder().seed(seed)
                         .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
                         .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                         .layer(0, ConvolutionLayer.builder(5, 5).nIn(numChannels).nOut(6)
@@ -181,7 +181,7 @@ public class DataSetIteratorTest extends BaseDL4JTest {
                                         .build())
                         .inputType(InputType.convolutionalFlat(numRows, numColumns, numChannels));
 
-        MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
+        final MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
         model.init();
 
         model.addTrainingListeners(new ScoreIterationListener(listenerFreq));

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStopping.java

@@ -45,6 +45,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution;
 import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.optimize.api.BaseTrainingListener;
@@ -924,8 +925,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
         };
 
         for(EpochTerminationCondition e : etc ){
-            String s = NeuralNetConfiguration.mapper().writeValueAsString(e);
-            EpochTerminationCondition c = NeuralNetConfiguration.mapper().readValue(s, EpochTerminationCondition.class);
+            String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(e);
+            EpochTerminationCondition c = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(s, EpochTerminationCondition.class);
             assertEquals(e, c);
         }
 
@@ -936,8 +937,8 @@ public class TestEarlyStopping extends BaseDL4JTest {
         };
 
         for(IterationTerminationCondition i : itc ){
-            String s = NeuralNetConfiguration.mapper().writeValueAsString(i);
-            IterationTerminationCondition c = NeuralNetConfiguration.mapper().readValue(s, IterationTerminationCondition.class);
+            String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(i);
+            IterationTerminationCondition c = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(s, IterationTerminationCondition.class);
             assertEquals(i, c);
         }
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/exceptions/TestInvalidConfigurations.java

@@ -309,7 +309,7 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
 
         try {
             NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
-                            .list()
+
                             .layer(0, ConvolutionLayer.builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5)
                                             .build())
                             .layer(1, OutputLayer.builder().nOut(10).build())

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/BNGradientCheckTest.java

@@ -77,7 +77,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
                 NeuralNetConfiguration.builder().updater(new NoOp())
                         .dataType(DataType.DOUBLE)
                         .seed(12345L)
-                        .dist(new NormalDistribution(0, 1)).list()
+                        .weightInit(new NormalDistribution(0, 1))
                         .layer(0, DenseLayer.builder().nIn(4).nOut(3)
                                 .activation(Activation.IDENTITY).build())
                         .layer(1,BatchNormalization.builder().useLogStd(useLogStd).nOut(3).build())
@@ -122,7 +122,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
                     .dataType(DataType.DOUBLE)
                     .updater(new NoOp()).seed(12345L)
                     .dist(new NormalDistribution(0, 2)).list()
-                    .layer(0, ConvolutionLayer.builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2)
+                    .layer(0, Convolution2D.builder().kernelSize(2, 2).stride(1, 1).nIn(depth).nOut(2)
                             .activation(Activation.IDENTITY).build())
                     .layer(1,BatchNormalization.builder().useLogStd(useLogStd).build())
                     .layer(2, ActivationLayer.builder().activation(Activation.TANH).build())

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DGradientCheckTest.java

@@ -91,9 +91,8 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                   .updater(new NoOp())
                   .dist(new NormalDistribution(0, 1))
                   .convolutionMode(ConvolutionMode.Same)
-                  .list()
                   .layer(
-                      Convolution1DLayer.builder()
+                      Convolution1D.builder()
                           .activation(afn)
                           .kernelSize(kernel)
                           .stride(stride)
@@ -202,7 +201,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                     .dist(new NormalDistribution(0, 1))
                     .convolutionMode(ConvolutionMode.Same)
                     .layer(
-                        Convolution1DLayer.builder()
+                        Convolution1D.builder()
                             .activation(afn)
                             .kernelSize(kernel)
                             .stride(stride)
@@ -211,7 +210,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                             .build())
                     .layer(Cropping1D.builder(cropping).build())
                     .layer(
-                        Convolution1DLayer.builder()
+                        Convolution1D.builder()
                             .activation(afn)
                             .kernelSize(kernel)
                             .stride(stride)
@@ -317,7 +316,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                     .dist(new NormalDistribution(0, 1))
                     .convolutionMode(ConvolutionMode.Same)
                     .layer(
-                        Convolution1DLayer.builder()
+                        Convolution1D.builder()
                             .activation(afn)
                             .kernelSize(kernel)
                             .stride(stride)
@@ -326,7 +325,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                             .build())
                     .layer(ZeroPadding1DLayer.builder(zeroPadding).build())
                     .layer(
-                        Convolution1DLayer.builder()
+                        Convolution1D.builder()
                             .activation(afn)
                             .kernelSize(kernel)
                             .stride(stride)
@@ -435,10 +434,9 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                     .updater(new NoOp())
                     .dist(new NormalDistribution(0, 1))
                     .convolutionMode(ConvolutionMode.Same)
-                    .list()
                     .layer(
                         0,
-                        Convolution1DLayer.builder()
+                        Convolution1D.builder()
                             .activation(afn)
                             .kernelSize(kernel)
                             .stride(stride)
@@ -447,7 +445,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                             .build())
                     .layer(
                         1,
-                        Convolution1DLayer.builder()
+                        Convolution1D.builder()
                             .activation(afn)
                             .kernelSize(kernel)
                             .stride(stride)
@@ -461,6 +459,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                             .stride(stride)
                             .padding(padding)
                             .pnorm(pnorm)
+                                .name("SubsamplingLayer")
                             .build())
                     .layer(
                         3,
@@ -548,7 +547,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                   .seed(12345)
                   .list()
                   .layer(
-                      Convolution1DLayer.builder()
+                      Convolution1D.builder()
                           .kernelSize(2)
                           .rnnDataFormat(RNNFormat.NCW)
                           .stride(stride)
@@ -562,7 +561,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                           .pnorm(pnorm)
                           .build())
                   .layer(
-                      Convolution1DLayer.builder()
+                      Convolution1D.builder()
                           .kernelSize(2)
                           .rnnDataFormat(RNNFormat.NCW)
                           .stride(stride)
@@ -655,7 +654,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
               .seed(12345)
               .list()
               .layer(
-                  Convolution1DLayer.builder()
+                  Convolution1D.builder()
                       .kernelSize(k)
                       .dilation(d)
                       .hasBias(hasBias)
@@ -664,7 +663,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                       .nOut(convNOut1)
                       .build())
               .layer(
-                  Convolution1DLayer.builder()
+                  Convolution1D.builder()
                       .kernelSize(k)
                       .dilation(d)
                       .convolutionMode(ConvolutionMode.Causal)

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DNewGradientCheckTest.java

@@ -0,0 +1,811 @@
+/*
+ *  ******************************************************************************
+ *  *
+ *  *
+ *  * This program and the accompanying materials are made available under the
+ *  * terms of the Apache License, Version 2.0 which is available at
+ *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *  *
+ *  *  See the NOTICE file distributed with this work for additional
+ *  *  information regarding copyright ownership.
+ *  * Unless required by applicable law or agreed to in writing, software
+ *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *  * License for the specific language governing permissions and limitations
+ *  * under the License.
+ *  *
+ *  * SPDX-License-Identifier: Apache-2.0
+ *  *****************************************************************************
+ */
+
+package org.deeplearning4j.gradientcheck;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import lombok.extern.slf4j.Slf4j;
+import org.deeplearning4j.BaseDL4JTest;
+import org.deeplearning4j.TestUtils;
+import org.deeplearning4j.datasets.iterator.INDArrayDataSetIterator;
+import org.deeplearning4j.nn.conf.ConvolutionMode;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.RNNFormat;
+import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
+import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.layers.*;
+import org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.util.Convolution1DUtils;
+import org.junit.jupiter.api.Test;
+import org.nd4j.common.primitives.Pair;
+import org.nd4j.evaluation.classification.Evaluation;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.dataset.DataSet;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.indexing.NDArrayIndex;
+import org.nd4j.linalg.learning.config.NoOp;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+
+@Slf4j
+public class CNN1DNewGradientCheckTest extends BaseDL4JTest {
+  private static final boolean PRINT_RESULTS = true;
+  private static final boolean RETURN_ON_FIRST_FAILURE = false;
+  private static final double DEFAULT_EPS = 1e-6;
+  private static final double DEFAULT_MAX_REL_ERROR = 1e-3;
+  private static final double DEFAULT_MIN_ABS_ERROR = 1e-8;
+
+  static {
+    Nd4j.setDataType(DataType.DOUBLE);
+  }
+
+  @Test
+  public void testCnn1D() {
+    int minibatchSize = 4;
+    int[] dataChannels = {4, 10}; //the input
+    int[] kernels = {2,4,5,8};
+    int stride = 2;
+    int padding = 3;
+    int seriesLength = 300;
+
+    for (int kernel : kernels) {
+      for (int dChannels : dataChannels) {
+        int numLabels = ((seriesLength + (2 * padding) - kernel) / stride) + 1;
+        final NeuralNetConfiguration conf =
+            NeuralNetConfiguration.builder()
+                .dataType(DataType.DOUBLE)
+                .updater(new NoOp())
+                .dist(new NormalDistribution(0, 1))
+                .convolutionMode(ConvolutionMode.Same)
+                .layer(
+                    Convolution1DNew.builder()
+                        .activation(Activation.RELU)
+                        .kernelSize(kernel)
+                        .stride(stride)
+                        .padding(padding)
+                        .nIn(dChannels) // channels
+                        .nOut(3)
+                        .rnnDataFormat(RNNFormat.NCW)
+                        .build())
+                .layer(
+                    RnnOutputLayer.builder()
+                        .lossFunction(LossFunctions.LossFunction.MCXENT)
+                        .activation(Activation.SOFTMAX)
+                        .nOut(4)
+                        .build())
+                .inputType(InputType.recurrent(dChannels, seriesLength))
+                .build();
+
+        INDArray input = Nd4j.rand(minibatchSize, dChannels, seriesLength);
+        INDArray labels = Nd4j.zeros(minibatchSize, 4, numLabels);
+        for (int i = 0; i < minibatchSize; i++) {
+          for (int j = 0; j < numLabels; j++) {
+            labels.putScalar(new int[] {i, i % 4, j}, 1.0);
+          }
+        }
+        final MultiLayerNetwork net = new MultiLayerNetwork(conf);
+        net.init();
+        String msg =
+            "Minibatch="
+                + minibatchSize
+                + ", activationFn="
+                + Activation.RELU
+                + ", kernel = "
+                + kernel;
+
+        System.out.println(msg);
+        for (int j = 0; j < net.getnLayers(); j++)
+          System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
+/**
+        List<Pair<INDArray, INDArray>> iter = new java.util.ArrayList<>(Collections.emptyList());
+        iter.add(new Pair<>(input, labels));
+        for(int x=0;x<100; x++) net.fit(input, labels);
+        Evaluation eval = net.evaluate(new INDArrayDataSetIterator(iter,2), Arrays.asList(new String[]{"One", "Two", "Three", "Four"}));
+        // net.fit(input, labels);
+        eval.eval(labels, net.output(input));
+
+ **/
+        boolean gradOK =
+            GradientCheckUtil.checkGradients(
+                net,
+                DEFAULT_EPS,
+                DEFAULT_MAX_REL_ERROR,
+                DEFAULT_MIN_ABS_ERROR,
+                PRINT_RESULTS,
+                RETURN_ON_FIRST_FAILURE,
+                input,
+                labels);
+
+        assertTrue(gradOK, msg);
+        TestUtils.testModelSerialization(net);
+      }
+      }
+
+
+  }
+
+
+  @Test
+  public void testCnn1DWithLocallyConnected1D() {
+    Nd4j.getRandom().setSeed(1337);
+
+    int[] minibatchSizes = {2, 3};
+    int length = 25;
+    int convNIn = 18;
+    int convNOut1 = 3;
+    int convNOut2 = 4;
+    int finalNOut = 4;
+
+    int[] kernels = {1,2,4};
+    int stride = 1;
+    int padding = 0;
+
+    Activation[] activations = {Activation.SIGMOID};
+
+    for (Activation afn : activations) {
+      for (int minibatchSize : minibatchSizes) {
+        for (int kernel : kernels) {
+          INDArray input = Nd4j.rand(minibatchSize, convNIn, length);
+          INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length);
+          for (int i = 0; i < minibatchSize; i++) {
+            for (int j = 0; j < length; j++) {
+              labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
+            }
+          }
+
+          NeuralNetConfiguration conf =
+              NeuralNetConfiguration.builder()
+                  .dataType(DataType.DOUBLE)
+                  .updater(new NoOp())
+                  .dist(new NormalDistribution(0, 1))
+                  .convolutionMode(ConvolutionMode.Same)
+                  .layer(
+                      Convolution1DNew.builder()
+                          .activation(afn)
+                          .kernelSize(kernel)
+                          .stride(stride)
+                          .padding(padding)
+                          .nIn(convNIn)
+                          .nOut(convNOut1)
+                          .rnnDataFormat(RNNFormat.NCW)
+                          .build())
+                  .layer(
+                      LocallyConnected1D.builder()
+                          .activation(afn)
+                          .kernelSize(kernel)
+                          .stride(stride)
+                          .padding(padding)
+                          .nIn(convNOut1)
+                          .nOut(convNOut2)
+                          .hasBias(false)
+                          .build())
+                  .layer(
+                      RnnOutputLayer.builder()
+                          .lossFunction(LossFunctions.LossFunction.MCXENT)
+                          .activation(Activation.SOFTMAX)
+                          .nOut(finalNOut)
+                          .build())
+                  .inputType(InputType.recurrent(convNIn, length))
+                  .build();
+
+          String json = conf.toJson();
+          NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
+          assertEquals(conf, c2);
+
+          MultiLayerNetwork net = new MultiLayerNetwork(conf);
+          net.init();
+
+          String msg =
+              "Minibatch=" + minibatchSize + ", activationFn=" + afn + ", kernel = " + kernel;
+
+          if (PRINT_RESULTS) {
+            System.out.println(msg);
+            //                        for (int j = 0; j < net.getnLayers(); j++)
+            //                            System.out.println("ILayer " + j + " # params: " +
+            // net.getLayer(j).numParams());
+          }
+
+          boolean gradOK =
+              GradientCheckUtil.checkGradients(
+                  net,
+                  DEFAULT_EPS,
+                  DEFAULT_MAX_REL_ERROR,
+                  DEFAULT_MIN_ABS_ERROR,
+                  PRINT_RESULTS,
+                  RETURN_ON_FIRST_FAILURE,
+                  input,
+                  labels);
+
+          assertTrue(gradOK, msg);
+
+          TestUtils.testModelSerialization(net);
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testCnn1DWithCropping1D() {
+    Nd4j.getRandom().setSeed(1337);
+
+    int[] minibatchSizes = {1, 3};
+    int length = 7;
+    int convNIn = 2;
+    int convNOut1 = 3;
+    int convNOut2 = 4;
+    int finalNOut = 4;
+
+    int[] kernels = {1, 2, 4};
+    int stride = 1;
+
+    int padding = 0;
+    int cropping = 1;
+    int croppedLength = length - 2 * cropping;
+
+    Activation[] activations = {Activation.SIGMOID};
+    SubsamplingLayer.PoolingType[] poolingTypes =
+        new SubsamplingLayer.PoolingType[] {
+          SubsamplingLayer.PoolingType.MAX,
+          SubsamplingLayer.PoolingType.AVG,
+          SubsamplingLayer.PoolingType.PNORM
+        };
+
+    for (Activation afn : activations) {
+      for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
+        for (int minibatchSize : minibatchSizes) {
+          for (int kernel : kernels) {
+            INDArray input = Nd4j.rand(minibatchSize, convNIn, length);
+            INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, croppedLength);
+            for (int i = 0; i < minibatchSize; i++) {
+              for (int j = 0; j < croppedLength; j++) {
+                labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
+              }
+            }
+
+            NeuralNetConfiguration conf =
+                NeuralNetConfiguration.builder()
+                    .dataType(DataType.DOUBLE)
+                    .updater(new NoOp())
+                    .dist(new NormalDistribution(0, 1))
+                    .convolutionMode(ConvolutionMode.Same)
+                    .layer(
+                        Convolution1DNew.builder()
+                            .activation(afn)
+                            .kernelSize(kernel)
+                            .stride(stride)
+                            .padding(padding)
+                            .nOut(convNOut1)
+                            .build())
+                    .layer(Cropping1D.builder(cropping).build())
+                    .layer(
+                        Convolution1DNew.builder()
+                            .activation(afn)
+                            .kernelSize(kernel)
+                            .stride(stride)
+                            .padding(padding)
+                            .nOut(convNOut2)
+                            .build())
+                    .layer(
+                        RnnOutputLayer.builder()
+                            .lossFunction(LossFunctions.LossFunction.MCXENT)
+                            .activation(Activation.SOFTMAX)
+                            .nOut(finalNOut)
+                            .build())
+                    .inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
+                    .build();
+
+            String json = conf.toJson();
+            NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
+            assertEquals(conf, c2);
+
+            MultiLayerNetwork net = new MultiLayerNetwork(conf);
+            net.init();
+
+            String msg =
+                "PoolingType="
+                    + poolingType
+                    + ", minibatch="
+                    + minibatchSize
+                    + ", activationFn="
+                    + afn
+                    + ", kernel = "
+                    + kernel;
+
+            if (PRINT_RESULTS) {
+              System.out.println(msg);
+              //                            for (int j = 0; j < net.getnLayers(); j++)
+              //                                System.out.println("ILayer " + j + " # params: " +
+              // net.getLayer(j).numParams());
+            }
+
+            boolean gradOK =
+                GradientCheckUtil.checkGradients(
+                    net,
+                    DEFAULT_EPS,
+                    DEFAULT_MAX_REL_ERROR,
+                    DEFAULT_MIN_ABS_ERROR,
+                    PRINT_RESULTS,
+                    RETURN_ON_FIRST_FAILURE,
+                    input,
+                    labels);
+
+            assertTrue(gradOK, msg);
+
+            TestUtils.testModelSerialization(net);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testCnn1DWithZeroPadding1D() {
+    Nd4j.getRandom().setSeed(1337);
+
+    int[] minibatchSizes = {1, 3};
+    int length = 7;
+    int convNIn = 2;
+    int convNOut1 = 3;
+    int convNOut2 = 4;
+    int finalNOut = 4;
+
+    int[] kernels = {1, 2, 4};
+    int stride = 1;
+    int pnorm = 2;
+
+    int padding = 0;
+    int zeroPadding = 2;
+    int paddedLength = length + 2 * zeroPadding;
+
+    Activation[] activations = {Activation.SIGMOID};
+    SubsamplingLayer.PoolingType[] poolingTypes =
+        new SubsamplingLayer.PoolingType[] {
+          SubsamplingLayer.PoolingType.MAX,
+          SubsamplingLayer.PoolingType.AVG,
+          SubsamplingLayer.PoolingType.PNORM
+        };
+
+    for (Activation afn : activations) {
+      for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
+        for (int minibatchSize : minibatchSizes) {
+          for (int kernel : kernels) {
+            INDArray input = Nd4j.rand(minibatchSize, convNIn, length);
+            INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, paddedLength);
+            for (int i = 0; i < minibatchSize; i++) {
+              for (int j = 0; j < paddedLength; j++) {
+                labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
+              }
+            }
+
+            NeuralNetConfiguration conf =
+                NeuralNetConfiguration.builder()
+                    .dataType(DataType.DOUBLE)
+                    .updater(new NoOp())
+                    .dist(new NormalDistribution(0, 1))
+                    .convolutionMode(ConvolutionMode.Same)
+                    .layer(
+                        Convolution1DNew.builder()
+                            .activation(afn)
+                            .kernelSize(2, kernel)
+                            .stride(stride)
+                            .padding(padding)
+                            .nOut(convNOut1)
+                            .build())
+                    .layer(ZeroPadding1DLayer.builder(zeroPadding).build())
+                    .layer(
+                        Convolution1DNew.builder()
+                            .activation(afn)
+                            .kernelSize(kernel)
+                            .stride(stride)
+                            .padding(padding)
+                            .nOut(convNOut2)
+                            .build())
+                    .layer(ZeroPadding1DLayer.builder(0).build())
+                    .layer(
+                        Subsampling1DLayer.builder(poolingType)
+                            .kernelSize(kernel)
+                            .stride(stride)
+                            .padding(padding)
+                            .pnorm(pnorm)
+                            .build())
+                    .layer(
+                        RnnOutputLayer.builder()
+                            .lossFunction(LossFunctions.LossFunction.MCXENT)
+                            .activation(Activation.SOFTMAX)
+                            .nOut(finalNOut)
+                            .build())
+                    .inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
+                    .build();
+
+            String json = conf.toJson();
+            NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
+            assertEquals(conf, c2);
+
+            MultiLayerNetwork net = new MultiLayerNetwork(conf);
+            net.init();
+
+            String msg =
+                "PoolingType="
+                    + poolingType
+                    + ", minibatch="
+                    + minibatchSize
+                    + ", activationFn="
+                    + afn
+                    + ", kernel = "
+                    + kernel;
+
+            if (PRINT_RESULTS) {
+              System.out.println(msg);
+              //                            for (int j = 0; j < net.getnLayers(); j++)
+              //                                System.out.println("ILayer " + j + " # params: " +
+              // net.getLayer(j).numParams());
+            }
+
+            boolean gradOK =
+                GradientCheckUtil.checkGradients(
+                    net,
+                    DEFAULT_EPS,
+                    DEFAULT_MAX_REL_ERROR,
+                    DEFAULT_MIN_ABS_ERROR,
+                    PRINT_RESULTS,
+                    RETURN_ON_FIRST_FAILURE,
+                    input,
+                    labels);
+
+            assertTrue(gradOK, msg);
+            TestUtils.testModelSerialization(net);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testCnn1DWithSubsampling1D() {
+    Nd4j.getRandom().setSeed(12345);
+
+    int[] minibatchSizes = {1, 3};
+    int length = 7;
+    int convNIn = 2;
+    int convNOut1 = 3;
+    int convNOut2 = 4;
+    int finalNOut = 4;
+
+    int[] kernels = {1, 2, 4};
+    int stride = 1;
+    int padding = 0;
+    int pnorm = 2;
+
+    Activation[] activations = {Activation.SIGMOID, Activation.TANH};
+    SubsamplingLayer.PoolingType[] poolingTypes =
+        new SubsamplingLayer.PoolingType[] {
+          SubsamplingLayer.PoolingType.MAX,
+          SubsamplingLayer.PoolingType.AVG,
+          SubsamplingLayer.PoolingType.PNORM
+        };
+
+    for (Activation afn : activations) {
+      for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
+        for (int minibatchSize : minibatchSizes) {
+          for (int kernel : kernels) {
+            INDArray input = Nd4j.rand(minibatchSize, convNIn, length);
+            INDArray labels = Nd4j.zeros(minibatchSize, finalNOut, length);
+            for (int i = 0; i < minibatchSize; i++) {
+              for (int j = 0; j < length; j++) {
+                labels.putScalar(new int[] {i, i % finalNOut, j}, 1.0);
+              }
+            }
+
+            NeuralNetConfiguration conf =
+                NeuralNetConfiguration.builder()
+                    .dataType(DataType.DOUBLE)
+                    .updater(new NoOp())
+                    .dist(new NormalDistribution(0, 1))
+                    .convolutionMode(ConvolutionMode.Same)
+                    .layer(
+                        0,
+                        Convolution1DNew.builder()
+                            .activation(afn)
+                            .kernelSize(kernel)
+                            .stride(stride)
+                            .padding(padding)
+                            .nOut(convNOut1)
+                            .build())
+                    .layer(
+                        1,
+                        Convolution1DNew.builder()
+                            .activation(afn)
+                            .kernelSize(kernel)
+                            .stride(stride)
+                            .padding(padding)
+                            .nOut(convNOut2)
+                            .build())
+                    .layer(
+                        2,
+                        Subsampling1DLayer.builder(poolingType)
+                            .kernelSize(kernel)
+                            .stride(stride)
+                            .padding(padding)
+                            .pnorm(pnorm)
+                            .name("SubsamplingLayer")
+                            .build())
+                    .layer(
+                        3,
+                        RnnOutputLayer.builder()
+                            .lossFunction(LossFunctions.LossFunction.MCXENT)
+                            .activation(Activation.SOFTMAX)
+                            .nOut(finalNOut)
+                            .build())
+                    .inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
+                    .build();
+
+            String json = conf.toJson();
+            NeuralNetConfiguration c2 = NeuralNetConfiguration.fromJson(json);
+            assertEquals(conf, c2);
+
+            MultiLayerNetwork net = new MultiLayerNetwork(conf);
+            net.init();
+
+            String msg =
+                "PoolingType="
+                    + poolingType
+                    + ", minibatch="
+                    + minibatchSize
+                    + ", activationFn="
+                    + afn
+                    + ", kernel = "
+                    + kernel;
+
+            if (PRINT_RESULTS) {
+              System.out.println(msg);
+              //                            for (int j = 0; j < net.getnLayers(); j++)
+              //                                System.out.println("ILayer " + j + " # params: " +
+              // net.getLayer(j).numParams());
+            }
+
+            boolean gradOK =
+                GradientCheckUtil.checkGradients(
+                    net,
+                    DEFAULT_EPS,
+                    DEFAULT_MAX_REL_ERROR,
+                    DEFAULT_MIN_ABS_ERROR,
+                    PRINT_RESULTS,
+                    RETURN_ON_FIRST_FAILURE,
+                    input,
+                    labels);
+
+            assertTrue(gradOK, msg);
+            TestUtils.testModelSerialization(net);
+          }
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testCnn1dWithMasking() {
+    int length = 12;
+    int convNIn = 2;
+    int convNOut1 = 3;
+    int convNOut2 = 4;
+    int finalNOut = 3;
+
+    int pnorm = 2;
+
+    SubsamplingLayer.PoolingType[] poolingTypes =
+        new SubsamplingLayer.PoolingType[] {
+          SubsamplingLayer.PoolingType.MAX, SubsamplingLayer.PoolingType.AVG
+        };
+
+    for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
+      for (ConvolutionMode cm :
+          new ConvolutionMode[] {ConvolutionMode.Same, ConvolutionMode.Truncate}) {
+        for (int stride : new int[] {1, 2}) {
+          String s = cm + ", stride=" + stride + ", pooling=" + poolingType;
+          log.info("Starting test: " + s);
+          Nd4j.getRandom().setSeed(12345);
+
+          NeuralNetConfiguration conf =
+              NeuralNetConfiguration.builder()
+                  .dataType(DataType.DOUBLE)
+                  .updater(new NoOp())
+                  .activation(Activation.TANH)
+                  .dist(new NormalDistribution(0, 1))
+                  .convolutionMode(cm)
+                  .seed(12345)
+                  .layer(
+                      Convolution1DNew.builder()
+                          .kernelSize(2)
+                          .rnnDataFormat(RNNFormat.NCW)
+                          .stride(stride)
+                          .nIn(convNIn)
+                          .nOut(convNOut1)
+                          .build())
+                  .layer(
+                      Subsampling1DLayer.builder(poolingType)
+                          .kernelSize(2)
+                          .stride(stride)
+                          .pnorm(pnorm)
+                          .build())
+                  .layer(
+                      Convolution1DNew.builder()
+                          .kernelSize(2)
+                          .rnnDataFormat(RNNFormat.NCW)
+                          .stride(stride)
+                          .nIn(convNOut1)
+                          .nOut(convNOut2)
+                          .build())
+                  .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())
+                  .layer(
+                      OutputLayer.builder()
+                          .lossFunction(LossFunctions.LossFunction.MCXENT)
+                          .activation(Activation.SOFTMAX)
+                          .nOut(finalNOut)
+                          .build())
+                  .inputType(InputType.recurrent(convNIn, length))
+                  .build();
+
+          MultiLayerNetwork net = new MultiLayerNetwork(conf);
+          net.init();
+
+          INDArray f = Nd4j.rand(2, convNIn, length);
+          INDArray fm = Nd4j.create(2, length);
+          fm.get(NDArrayIndex.point(0), NDArrayIndex.all()).assign(1);
+          fm.get(NDArrayIndex.point(1), NDArrayIndex.interval(0, 6)).assign(1);
+
+          INDArray label = TestUtils.randomOneHot(2, finalNOut);
+
+          boolean gradOK =
+              GradientCheckUtil.checkGradients(
+                  new GradientCheckUtil.MLNConfig().net(net).input(f).labels(label).inputMask(fm));
+
+          assertTrue(gradOK, s);
+          TestUtils.testModelSerialization(net);
+
+          // TODO also check that masked step values don't impact forward pass, score or gradients
+
+          DataSet ds = new DataSet(f, label, fm, null);
+          double scoreBefore = net.score(ds);
+          net.setInput(f);
+          net.setLabels(label);
+          net.setLayerMaskArrays(fm, null);
+          net.computeGradientAndScore();
+          INDArray gradBefore = net.getFlattenedGradients().dup();
+          f.putScalar(1, 0, 10, 10.0);
+          f.putScalar(1, 1, 11, 20.0);
+          double scoreAfter = net.score(ds);
+          net.setInput(f);
+          net.setLabels(label);
+          net.setLayerMaskArrays(fm, null);
+          net.computeGradientAndScore();
+          INDArray gradAfter = net.getFlattenedGradients().dup();
+
+          assertEquals(scoreBefore, scoreAfter, 1e-6);
+          assertEquals(gradBefore, gradAfter);
+        }
+      }
+    }
+  }
+
+  @Test
+  public void testCnn1Causal() throws Exception {
+    int convNIn = 2;
+    int convNOut1 = 3;
+    int convNOut2 = 4;
+    int finalNOut = 3;
+
+    int[] lengths = {11, 12, 13, 9, 10, 11};
+    int[] kernels = {2, 3, 2, 4, 2, 3};
+    int[] dilations = {1, 1, 2, 1, 2, 1};
+    int[] strides = {1, 2, 1, 2, 1, 1};
+    boolean[] masks = {false, true, false, true, false, true};
+    boolean[] hasB = {true, false, true, false, true, true};
+    for (int i = 0; i < lengths.length; i++) {
+      int length = lengths[i];
+      int k = kernels[i];
+      int d = dilations[i];
+      int st = strides[i];
+      boolean mask = masks[i];
+      boolean hasBias = hasB[i];
+      // TODO has bias
+      String s = "k=" + k + ", s=" + st + " d=" + d + ", seqLen=" + length;
+      log.info("Starting test: " + s);
+      Nd4j.getRandom().setSeed(12345);
+
+      NeuralNetConfiguration conf =
+          NeuralNetConfiguration.builder()
+              .dataType(DataType.DOUBLE)
+              .updater(new NoOp())
+              .activation(Activation.TANH)
+              .weightInit(new NormalDistribution(0, 1))
+              .seed(12345)
+              .layer(
+                  Convolution1DNew.builder()
+                      .kernelSize(k)
+                      .dilation(d)
+                      .hasBias(hasBias)
+                      .convolutionMode(ConvolutionMode.Causal)
+                      .stride(st)
+                      .nOut(convNOut1)
+                      .build())
+              .layer(
+                  Convolution1DNew.builder()
+                      .kernelSize(k)
+                      .dilation(d)
+                      .convolutionMode(ConvolutionMode.Causal)
+                      .stride(st)
+                      .nOut(convNOut2)
+                      .build())
+              .layer(
+                  RnnOutputLayer.builder()
+                      .lossFunction(LossFunctions.LossFunction.MCXENT)
+                      .activation(Activation.SOFTMAX)
+                      .nOut(finalNOut)
+                      .build())
+              .inputType(InputType.recurrent(convNIn, length, RNNFormat.NCW))
+              .build();
+
+      MultiLayerNetwork net = new MultiLayerNetwork(conf);
+      net.init();
+
+      INDArray f = Nd4j.rand(DataType.DOUBLE, 2, convNIn, length);
+      INDArray fm = null;
+      if (mask) {
+        fm = Nd4j.create(2, length);
+        fm.get(NDArrayIndex.point(0), NDArrayIndex.all()).assign(1);
+        fm.get(NDArrayIndex.point(1), NDArrayIndex.interval(0, length - 2)).assign(1);
+      }
+
+      long outSize1 = Convolution1DUtils.getOutputSize(length, k, st, 0, ConvolutionMode.Causal, d);
+      long outSize2 =
+          Convolution1DUtils.getOutputSize(outSize1, k, st, 0, ConvolutionMode.Causal, d);
+
+      INDArray label = TestUtils.randomOneHotTimeSeries(2, finalNOut, (int) outSize2);
+
+      String msg =
+              "Minibatch="
+                      + 1
+                      + ", activationFn="
+                      + Activation.RELU
+                      + ", kernel = "
+                      + k;
+
+      System.out.println(msg);
+      for (int j = 0; j < net.getnLayers(); j++)
+        System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
+
+      boolean gradOK =
+          GradientCheckUtil.checkGradients(
+              new GradientCheckUtil.MLNConfig().net(net).input(f).labels(label).inputMask(fm));
+
+      assertTrue(gradOK, s);
+      TestUtils.testModelSerialization(net);
+    }
+  }
+}

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java

@@ -112,9 +112,8 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
 
                                             NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
                                                     .dataType(DataType.DOUBLE)
-                                                    .updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
+                                                    .updater(new NoOp())
                                                     .dist(new NormalDistribution(0, 1))
-                                                    .list()
                                                     .layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
                                                             .stride(stride).nIn(convNIn).nOut(convNOut1).hasBias(false)
                                                             .convolutionMode(mode).dataFormat(df)
@@ -400,7 +399,6 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
                                 .updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
                                 .dist(new NormalDistribution(0, 1))
                                 .seed(12345)
-                                .list()
                                 .layer(0, Convolution3D.builder().activation(afn).kernelSize(1, 1, 1)
                                         .nIn(convNIn).nOut(convNOut).hasBias(false)
                                         .convolutionMode(mode).dataFormat(df)

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java

@@ -108,8 +108,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                   .updater(new NoOp())
                   .weightInit(WeightInit.XAVIER)
                   .seed(12345L)
-                  .list()
-                  .layer(0, ConvolutionLayer.builder(1, 1).nOut(6).activation(afn).build())
+
+                  .layer(0, Convolution2D.builder().kernelSize(1).stride(1).nOut(6).activation(afn).build())
                   .layer(1, OutputLayer.builder(lf).activation(outputActivation).nOut(3).build())
                   .inputType(InputType.convolutionalFlat(1, 4, 1));
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/LossFunctionGradientCheck.java

@@ -32,6 +32,7 @@ import org.deeplearning4j.nn.conf.distribution.UniformDistribution;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.LossLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
@@ -336,7 +337,7 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
                 // to ensure that we carry the parameters through
                 // the serializer.
                 try{
-                    ObjectMapper m = NeuralNetConfiguration.mapper();
+                    ObjectMapper m = CavisMapper.getMapper(CavisMapper.Type.JSON);
                     String s = m.writeValueAsString(lossFunctions[i]);
                     ILossFunction lf2 = m.readValue(s, lossFunctions[i].getClass());
                     lossFunctions[i] = lf2;

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/dtypes/DTypeTests.java

@@ -180,7 +180,7 @@ public class DTypeTests extends BaseDL4JTest {
             Pooling2D.class,        //Alias for SubsamplingLayer
             Convolution2D.class,    //Alias for ConvolutionLayer
             Pooling1D.class,        //Alias for Subsampling1D
-            Convolution1D.class,    //Alias for  Convolution1DLayer
+            Convolution1D.class,    //Alias for  Convolution1D
             TensorFlowCnnToFeedForwardPreProcessor.class    //Deprecated
     ));
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvDataFormatTests.java

@@ -37,7 +37,7 @@ import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.workspace.ArrayType;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
-import org.deeplearning4j.util.ConvolutionUtils;
+import org.deeplearning4j.util.Convolution2DUtils;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.Timeout;
 import org.nd4j.linalg.activations.Activation;
@@ -1026,7 +1026,7 @@ public class ConvDataFormatTests extends BaseDL4JTest {
                 } catch (DL4JInvalidInputException e) {
 //                    e.printStackTrace();
                     String msg = e.getMessage();
-                    assertTrue(msg.contains(ConvolutionUtils.NCHW_NHWC_ERROR_MSG) || msg.contains("input array channels does not match CNN layer configuration"), msg);
+                    assertTrue(msg.contains(Convolution2DUtils.NCHW_NHWC_ERROR_MSG) || msg.contains("input array channels does not match CNN layer configuration"), msg);
                 }
             }
         }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvolutionLayerTest.java

@@ -36,7 +36,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.RNNFormat;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.*;
-import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
+import org.deeplearning4j.nn.conf.layers.Convolution1D;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
@@ -921,7 +921,7 @@ public class ConvolutionLayerTest extends BaseDL4JTest {
         NeuralNetConfiguration.builder()
             .convolutionMode(ConvolutionMode.Same)
             .layer(
-                Convolution1DLayer.builder()
+                Convolution1D.builder()
                     .nOut(3)
                     .kernelSize(2)
                     .activation(Activation.TANH)
@@ -975,7 +975,7 @@ public class ConvolutionLayerTest extends BaseDL4JTest {
 
   @Test
   public void testConv1dCausalAllowed() {
-    Convolution1DLayer.builder().convolutionMode(ConvolutionMode.Causal).kernelSize(2).build();
+    Convolution1D.builder().convolutionMode(ConvolutionMode.Causal).kernelSize(2).build();
     Subsampling1DLayer.builder().convolutionMode(ConvolutionMode.Causal).kernelSize(2).build();
   }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/TestConvolutionModes.java

@@ -33,7 +33,7 @@ import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
-import org.deeplearning4j.util.ConvolutionUtils;
+import org.deeplearning4j.util.Convolution2DUtils;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -346,7 +346,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
         assertEquals(2, it.getHeight());
         assertEquals(2, it.getWidth());
         assertEquals(dOut, it.getChannels());
-        int[] outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Strict);
+        int[] outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Strict);
         assertEquals(2, outSize[0]);
         assertEquals(2, outSize[1]);
 
@@ -357,7 +357,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
         assertEquals(2, it.getHeight());
         assertEquals(2, it.getWidth());
         assertEquals(dOut, it.getChannels());
-        outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Truncate);
+        outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Truncate);
         assertEquals(2, outSize[0]);
         assertEquals(2, outSize[1]);
 
@@ -367,7 +367,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
         assertEquals(3, it.getHeight());
         assertEquals(3, it.getWidth());
         assertEquals(dOut, it.getChannels());
-        outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, null, ConvolutionMode.Same);
+        outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, null, ConvolutionMode.Same);
         assertEquals(3, outSize[0]);
         assertEquals(3, outSize[1]);
 
@@ -397,7 +397,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
             System.out.println(e.getMessage());
         }
         try {
-            outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Strict);
+            outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Strict);
             fail("Exception expected");
         } catch (DL4JException e) {
             System.out.println(e.getMessage());
@@ -409,7 +409,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
         assertEquals(1, it.getHeight());
         assertEquals(1, it.getWidth());
         assertEquals(dOut, it.getChannels());
-        outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Truncate);
+        outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, padding, ConvolutionMode.Truncate);
         assertEquals(1, outSize[0]);
         assertEquals(1, outSize[1]);
 
@@ -419,7 +419,7 @@ public class TestConvolutionModes extends BaseDL4JTest {
         assertEquals(2, it.getHeight());
         assertEquals(2, it.getWidth());
         assertEquals(dOut, it.getChannels());
-        outSize = ConvolutionUtils.getOutputSize(inData, kernel, stride, null, ConvolutionMode.Same);
+        outSize = Convolution2DUtils.getOutputSize(inData, kernel, stride, null, ConvolutionMode.Same);
         assertEquals(2, outSize[0]);
         assertEquals(2, outSize[1]);
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/normalization/BatchNormalizationTest.java

@@ -732,7 +732,7 @@ public class BatchNormalizationTest extends BaseDL4JTest {
                     .weightInit(WeightInit.XAVIER)
                     .convolutionMode(ConvolutionMode.Same)
                     .layer(rnn ? LSTM.builder().nOut(3).build() :
-                            Convolution1DLayer.builder().kernelSize(3).stride(1).nOut(3).build())
+                            Convolution1D.builder().kernelSize(3).stride(1).nOut(3).build())
                     .layer(BatchNormalization.builder().build())
                     .layer(RnnOutputLayer.builder().nOut(3).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build())
                     .inputType(InputType.recurrent(3))

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/weights/WeightInitIdentityTest.java

@@ -52,7 +52,7 @@ public class WeightInitIdentityTest extends BaseDL4JTest {
                 .graphBuilder()
                 .addInputs(inputName)
                 .setOutputs(output)
-                .layer(conv, Convolution1DLayer.builder(7)
+                .layer(conv, Convolution1D.builder(7)
                         .convolutionMode(ConvolutionMode.Same)
                         .nOut(input.size(1))
                         .weightInit(new WeightInitIdentity())

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/TestDistributionDeserializer.java

@@ -23,6 +23,7 @@ package org.deeplearning4j.regressiontest;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.distribution.*;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.junit.jupiter.api.Test;
 import com.fasterxml.jackson.databind.ObjectMapper;
 
@@ -38,7 +39,7 @@ public class TestDistributionDeserializer extends BaseDL4JTest {
                         new Distribution[] {new NormalDistribution(3, 0.5), new UniformDistribution(-2, 1),
                                         new GaussianDistribution(2, 1.0), new BinomialDistribution(10, 0.3)};
 
-        ObjectMapper om = NeuralNetConfiguration.mapper();
+        ObjectMapper om = CavisMapper.getMapper(CavisMapper.Type.JSON);
 
         for (Distribution d : distributions) {
             String json = om.writeValueAsString(d);
@@ -50,7 +51,7 @@ public class TestDistributionDeserializer extends BaseDL4JTest {
 
     @Test
     public void testDistributionDeserializerLegacyFormat() throws Exception {
-        ObjectMapper om = NeuralNetConfiguration.mapper();
+        ObjectMapper om = CavisMapper.getMapper(CavisMapper.Type.JSON);
 
         String normalJson = "{\n" + "          \"normal\" : {\n" + "            \"mean\" : 0.1,\n"
                         + "            \"std\" : 1.2\n" + "          }\n" + "        }";

cavis-dnn/cavis-dnn-cudnn/src/main/java/org/deeplearning4j/cuda/convolution/CudnnConvolutionHelper.java

@@ -38,7 +38,7 @@ import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.cuda.BaseCudnnHelper;
 import org.deeplearning4j.nn.layers.convolution.ConvolutionHelper;
 import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
-import org.deeplearning4j.util.ConvolutionUtils;
+import org.deeplearning4j.util.Convolution2DUtils;
 import org.nd4j.jita.allocator.Allocator;
 import org.nd4j.jita.allocator.impl.AtomicAllocator;
 import org.nd4j.jita.conf.CudaEnvironment;
@@ -681,9 +681,9 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
 
         int[] outSize;
         if (convolutionMode == ConvolutionMode.Same) {
-            outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
-            padding = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
-            int[] padBottomRight = ConvolutionUtils.getSameModeBottomRightPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
+            outSize = Convolution2DUtils.getOutputSize(input, kernel, strides, null, convolutionMode, dilation, format); //Also performs validation
+            padding = Convolution2DUtils.getSameModeTopLeftPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
+            int[] padBottomRight = Convolution2DUtils.getSameModeBottomRightPadding(outSize, new int[] {(int) inH, (int) inW}, kernel, strides, dilation);
             if(!Arrays.equals(padding, padBottomRight)){
                 /*
                 CuDNN - even as of 7.1 (CUDA 9.1) still doesn't have support for proper SAME mode padding (i.e., asymmetric
@@ -731,7 +731,7 @@ public class CudnnConvolutionHelper extends BaseCudnnHelper implements Convoluti
                 // CuDNN handle
             }
         } else {
-            outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, padding, convolutionMode, dilation, format); //Also performs validation
+            outSize = Convolution2DUtils.getOutputSize(input, kernel, strides, padding, convolutionMode, dilation, format); //Also performs validation
         }
 
         return new CudnnForwardArgs(manualPadBottom, manualPadRight, input, origInput, padding, outSize);

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/KerasModel.java

@@ -42,7 +42,7 @@ import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
 import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelBuilder;
 import org.deeplearning4j.nn.modelimport.keras.utils.KerasModelUtils;
 import org.deeplearning4j.nn.modelimport.keras.utils.KerasOptimizerUtils;
-import org.deeplearning4j.util.ConvolutionUtils;
+import org.deeplearning4j.util.Convolution2DUtils;
 import org.nd4j.common.primitives.Counter;
 import org.nd4j.common.primitives.Pair;
 import org.nd4j.linalg.learning.config.IUpdater;
@@ -442,8 +442,8 @@ public class KerasModel {
                     KerasInput kerasInput = (KerasInput) layer;
                     LayerConfiguration layer1 = layersOrdered.get(kerasLayerIdx + 1).layer;
                     //no dim order, try to pull it from the next layer if there is one
-                    if(ConvolutionUtils.layerHasConvolutionLayout(layer1)) {
-                        CNN2DFormat formatForLayer = ConvolutionUtils.getFormatForLayer(layer1);
+                    if(Convolution2DUtils.layerHasConvolutionLayout(layer1)) {
+                        CNN2DFormat formatForLayer = Convolution2DUtils.getFormatForLayer(layer1);
                         if(formatForLayer == CNN2DFormat.NCHW) {
                             dimOrder = KerasLayer.DimOrder.THEANO;
                         }  else if(formatForLayer == CNN2DFormat.NHWC) {

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/KerasSequentialModel.java

@@ -43,223 +43,255 @@ import static org.deeplearning4j.nn.modelimport.keras.utils.KerasModelUtils.impo
 @Slf4j
 public class KerasSequentialModel extends KerasModel {
 
-    /**
-     * (Recommended) Builder-pattern constructor for Sequential model.
-     *
-     * @param modelBuilder builder object
-     * @throws IOException                            I/O exception
-     * @throws InvalidKerasConfigurationException     Invalid Keras configuration
-     * @throws UnsupportedKerasConfigurationException Unsupported Keras configuration
-     */
-    public KerasSequentialModel(KerasModelBuilder modelBuilder)
-            throws UnsupportedKerasConfigurationException, IOException, InvalidKerasConfigurationException {
-        this(modelBuilder.getModelJson(), modelBuilder.getModelYaml(), modelBuilder.getWeightsArchive(),
-                modelBuilder.getWeightsRoot(), modelBuilder.getTrainingJson(), modelBuilder.getTrainingArchive(),
-                modelBuilder.isEnforceTrainingConfig(), modelBuilder.getInputShape());
+  /**
+   * (Recommended) Builder-pattern constructor for Sequential model.
+   *
+   * @param modelBuilder builder object
+   * @throws IOException I/O exception
+   * @throws InvalidKerasConfigurationException Invalid Keras configuration
+   * @throws UnsupportedKerasConfigurationException Unsupported Keras configuration
+   */
+  public KerasSequentialModel(KerasModelBuilder modelBuilder)
+      throws UnsupportedKerasConfigurationException,
+          IOException,
+          InvalidKerasConfigurationException {
+    this(
+        modelBuilder.getModelJson(),
+        modelBuilder.getModelYaml(),
+        modelBuilder.getWeightsArchive(),
+        modelBuilder.getWeightsRoot(),
+        modelBuilder.getTrainingJson(),
+        modelBuilder.getTrainingArchive(),
+        modelBuilder.isEnforceTrainingConfig(),
+        modelBuilder.getInputShape());
+  }
+
+  /**
+   * (Not recommended) Constructor for Sequential model from model configuration (JSON or YAML),
+   * training configuration (JSON), weights, and "training mode" boolean indicator. When built in
+   * training mode, certain unsupported configurations (e.g., unknown regularizers) will throw
+   * Exceptions. When enforceTrainingConfig=false, these will generate warnings but will be
+   * otherwise ignored.
+   *
+   * @param modelJson model configuration JSON string
+   * @param modelYaml model configuration YAML string
+   * @param trainingJson training configuration JSON string
+   * @throws IOException I/O exception
+   */
+  public KerasSequentialModel(
+      String modelJson,
+      String modelYaml,
+      Hdf5Archive weightsArchive,
+      String weightsRoot,
+      String trainingJson,
+      Hdf5Archive trainingArchive,
+      boolean enforceTrainingConfig,
+      int[] inputShape)
+      throws IOException,
+          InvalidKerasConfigurationException,
+          UnsupportedKerasConfigurationException {
+
+    Map<String, Object> modelConfig = KerasModelUtils.parseModelConfig(modelJson, modelYaml);
+    this.kerasMajorVersion = KerasModelUtils.determineKerasMajorVersion(modelConfig, config);
+    this.kerasBackend = KerasModelUtils.determineKerasBackend(modelConfig, config);
+    this.enforceTrainingConfig = enforceTrainingConfig;
+
+    /* Determine model configuration type. */
+    if (!modelConfig.containsKey(config.getFieldClassName()))
+      throw new InvalidKerasConfigurationException(
+          "Could not determine Keras model class (no "
+              + config.getFieldClassName()
+              + " field found)");
+    this.className = (String) modelConfig.get(config.getFieldClassName());
+    if (!this.className.equals(config.getFieldClassNameSequential()))
+      throw new InvalidKerasConfigurationException(
+          "Model class name must be "
+              + config.getFieldClassNameSequential()
+              + " (found "
+              + this.className
+              + ")");
+
+    /* Process layer configurations. */
+    if (!modelConfig.containsKey(config.getModelFieldConfig()))
+      throw new InvalidKerasConfigurationException(
+          "Could not find layer configurations (no "
+              + config.getModelFieldConfig()
+              + " field found)");
+
+    // Prior to Keras 2.2.3 the "config" of a Sequential model was a list of layer configurations.
+    // For consistency
+    // "config" is now an object containing a "name" and "layers", the latter contain the same data
+    // as before.
+    // This change only affects Sequential models.
+    List<Object> layerList;
+    try {
+      layerList = (List<Object>) modelConfig.get(config.getModelFieldConfig());
+    } catch (Exception e) {
+      HashMap layerMap = (HashMap<String, Object>) modelConfig.get(config.getModelFieldConfig());
+      layerList = (List<Object>) layerMap.get("layers");
     }
 
-    /**
-     * (Not recommended) Constructor for Sequential model from model configuration
-     * (JSON or YAML), training configuration (JSON), weights, and "training mode"
-     * boolean indicator. When built in training mode, certain unsupported configurations
-     * (e.g., unknown regularizers) will throw Exceptions. When enforceTrainingConfig=false, these
-     * will generate warnings but will be otherwise ignored.
-     *
-     * @param modelJson    model configuration JSON string
-     * @param modelYaml    model configuration YAML string
-     * @param trainingJson training configuration JSON string
-     * @throws IOException I/O exception
-     */
-    public KerasSequentialModel(String modelJson, String modelYaml, Hdf5Archive weightsArchive, String weightsRoot,
-                                String trainingJson, Hdf5Archive trainingArchive, boolean enforceTrainingConfig,
-                                int[] inputShape)
-            throws IOException, InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
+    Pair<Map<String, KerasLayer>, List<KerasLayer>> layerPair = prepareLayers(layerList);
+    this.layers = layerPair.getFirst();
+    this.layersOrdered = layerPair.getSecond();
 
-        Map<String, Object> modelConfig = KerasModelUtils.parseModelConfig(modelJson, modelYaml);
-        this.kerasMajorVersion = KerasModelUtils.determineKerasMajorVersion(modelConfig, config);
-        this.kerasBackend = KerasModelUtils.determineKerasBackend(modelConfig, config);
-        this.enforceTrainingConfig = enforceTrainingConfig;
+    KerasLayer inputLayer;
+    if (this.layersOrdered.get(0) instanceof KerasInput) {
+      inputLayer = this.layersOrdered.get(0);
+    } else {
+      /* Add placeholder input layer and update lists of input and output layers. */
+      int[] firstLayerInputShape = this.layersOrdered.get(0).getInputShape();
+      Preconditions.checkState(
+          ArrayUtil.prod(firstLayerInputShape) > 0, "Input shape must not be zero!");
+      inputLayer = new KerasInput("input1", firstLayerInputShape);
+      inputLayer.setDimOrder(this.layersOrdered.get(0).getDimOrder());
+      this.layers.put(inputLayer.getName(), inputLayer);
+      this.layersOrdered.add(0, inputLayer);
+    }
+    this.inputLayerNames = new ArrayList<>(Collections.singletonList(inputLayer.getName()));
+    this.outputLayerNames =
+        new ArrayList<>(
+            Collections.singletonList(
+                this.layersOrdered.get(this.layersOrdered.size() - 1).getName()));
 
-        /* Determine model configuration type. */
-        if (!modelConfig.containsKey(config.getFieldClassName()))
-            throw new InvalidKerasConfigurationException(
-                    "Could not determine Keras model class (no " + config.getFieldClassName() + " field found)");
-        this.className = (String) modelConfig.get(config.getFieldClassName());
-        if (!this.className.equals(config.getFieldClassNameSequential()))
-            throw new InvalidKerasConfigurationException("Model class name must be " + config.getFieldClassNameSequential()
-                    + " (found " + this.className + ")");
-
-        /* Process layer configurations. */
-        if (!modelConfig.containsKey(config.getModelFieldConfig()))
-            throw new InvalidKerasConfigurationException(
-                    "Could not find layer configurations (no " + config.getModelFieldConfig() + " field found)");
-
-        // Prior to Keras 2.2.3 the "config" of a Sequential model was a list of layer configurations. For consistency
-        // "config" is now an object containing a "name" and "layers", the latter contain the same data as before.
-        // This change only affects Sequential models.
-        List<Object> layerList;
-        try {
-            layerList = (List<Object>) modelConfig.get(config.getModelFieldConfig());
-        } catch (Exception e) {
-            HashMap layerMap = (HashMap<String, Object>) modelConfig.get(config.getModelFieldConfig());
-            layerList =  (List<Object>) layerMap.get("layers");
-        }
-
-        Pair<Map<String, KerasLayer>, List<KerasLayer>> layerPair =
-                prepareLayers(layerList);
-        this.layers = layerPair.getFirst();
-        this.layersOrdered = layerPair.getSecond();
-
-        KerasLayer inputLayer;
-        if (this.layersOrdered.get(0) instanceof KerasInput) {
-            inputLayer = this.layersOrdered.get(0);
-        } else {
-            /* Add placeholder input layer and update lists of input and output layers. */
-            int[] firstLayerInputShape = this.layersOrdered.get(0).getInputShape();
-            Preconditions.checkState(ArrayUtil.prod(firstLayerInputShape) > 0,"Input shape must not be zero!");
-            inputLayer = new KerasInput("input1", firstLayerInputShape);
-            inputLayer.setDimOrder(this.layersOrdered.get(0).getDimOrder());
-            this.layers.put(inputLayer.getName(), inputLayer);
-            this.layersOrdered.add(0, inputLayer);
-        }
-        this.inputLayerNames = new ArrayList<>(Collections.singletonList(inputLayer.getName()));
-        this.outputLayerNames = new ArrayList<>(
-                Collections.singletonList(this.layersOrdered.get(this.layersOrdered.size() - 1).getName()));
-
-        /* Update each layer's inbound layer list to include (only) previous layer. */
-        KerasLayer prevLayer = null;
-        for (KerasLayer layer : this.layersOrdered) {
-            if (prevLayer != null)
-                layer.setInboundLayerNames(Collections.singletonList(prevLayer.getName()));
-            prevLayer = layer;
-        }
-
-        /* Import training configuration. */
-        if (enforceTrainingConfig) {
-            if (trainingJson != null)
-                importTrainingConfiguration(trainingJson);
-            else log.warn("If enforceTrainingConfig is true, a training " +
-                    "configuration object has to be provided. Usually the only practical way to do this is to store" +
-                    " your keras model with `model.save('model_path.h5'. If you store model config and weights" +
-                    " separately no training configuration is attached.");
-        }
-
-        this.outputTypes = inferOutputTypes(inputShape);
-
-        if (weightsArchive != null)
-            importWeights(weightsArchive, weightsRoot, layers, kerasMajorVersion, kerasBackend);
+    /* Update each layer's inbound layer list to include (only) previous layer. */
+    KerasLayer prevLayer = null;
+    for (KerasLayer layer : this.layersOrdered) {
+      if (prevLayer != null)
+        layer.setInboundLayerNames(Collections.singletonList(prevLayer.getName()));
+      prevLayer = layer;
     }
 
-    /**
-     * Default constructor
-     */
-    public KerasSequentialModel() {
-        super();
+    /* Import training configuration. */
+    if (enforceTrainingConfig) {
+      if (trainingJson != null) importTrainingConfiguration(trainingJson);
+      else
+        log.warn(
+            "If enforceTrainingConfig is true, a training "
+                + "configuration object has to be provided. Usually the only practical way to do this is to store"
+                + " your keras model with `model.save('model_path.h5'. If you store model config and weights"
+                + " separately no training configuration is attached.");
     }
 
-    /**
-     * Configure a NeuralNetConfiguration from this Keras Sequential model configuration.
-     *
-     * @return NeuralNetConfiguration
-     */
-    public NeuralNetConfiguration getNeuralNetConfiguration()
-            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
-        if (!this.className.equals(config.getFieldClassNameSequential()))
-            throw new InvalidKerasConfigurationException(
-                    "Keras model class name " + this.className + " incompatible with MultiLayerNetwork");
-        if (this.inputLayerNames.size() != 1)
-            throw new InvalidKerasConfigurationException(
-                    "MultiLayerNetwork expects only 1 input (found " + this.inputLayerNames.size() + ")");
-        if (this.outputLayerNames.size() != 1)
-            throw new InvalidKerasConfigurationException(
-                    "MultiLayerNetwork expects only 1 output (found " + this.outputLayerNames.size() + ")");
+    this.outputTypes = inferOutputTypes(inputShape);
 
-        NeuralNetConfiguration.NeuralNetConfigurationBuilder modelBuilder = NeuralNetConfiguration.builder();
+    if (weightsArchive != null)
+      importWeights(weightsArchive, weightsRoot, layers, kerasMajorVersion, kerasBackend);
+  }
 
-        if (optimizer != null) {
-            modelBuilder.updater(optimizer);
+  /** Default constructor */
+  public KerasSequentialModel() {
+    super();
+  }
+
+  /**
+   * Configure a NeuralNetConfiguration from this Keras Sequential model configuration.
+   *
+   * @return NeuralNetConfiguration
+   */
+  public NeuralNetConfiguration getNeuralNetConfiguration()
+      throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
+    if (!this.className.equals(config.getFieldClassNameSequential()))
+      throw new InvalidKerasConfigurationException(
+          "Keras model class name " + this.className + " incompatible with MultiLayerNetwork");
+    if (this.inputLayerNames.size() != 1)
+      throw new InvalidKerasConfigurationException(
+          "MultiLayerNetwork expects only 1 input (found " + this.inputLayerNames.size() + ")");
+    if (this.outputLayerNames.size() != 1)
+      throw new InvalidKerasConfigurationException(
+          "MultiLayerNetwork expects only 1 output (found " + this.outputLayerNames.size() + ")");
+
+    NeuralNetConfiguration.NeuralNetConfigurationBuilder modelBuilder =
+        NeuralNetConfiguration.builder();
+
+    if (optimizer != null) {
+      modelBuilder.updater(optimizer);
+    }
+
+    // don't forcibly override for keras import
+    modelBuilder.overrideNinUponBuild(false);
+    /* Add layers one at a time. */
+    KerasLayer prevLayer = null;
+    int layerIndex = 0;
+    for (KerasLayer layer : this.layersOrdered) {
+      if (layer.isLayer()) {
+        int nbInbound = layer.getInboundLayerNames().size();
+        if (nbInbound != 1)
+          throw new InvalidKerasConfigurationException(
+              "Layers in NeuralNetConfiguration must have exactly one inbound layer (found "
+                  + nbInbound
+                  + " for layer "
+                  + layer.getName()
+                  + ")");
+        if (prevLayer != null) {
+          InputType[] inputTypes = new InputType[1];
+          InputPreProcessor preprocessor;
+          if (prevLayer.isInputPreProcessor()) {
+            inputTypes[0] = this.outputTypes.get(prevLayer.getInboundLayerNames().get(0));
+            preprocessor = prevLayer.getInputPreprocessor(inputTypes);
+            InputType outputType = preprocessor.getOutputType(inputTypes[0]);
+            layer.getLayer().setNIn(outputType, modelBuilder.isOverrideNinUponBuild());
+          } else {
+            inputTypes[0] = this.outputTypes.get(prevLayer.getName());
+            preprocessor = layer.getInputPreprocessor(inputTypes);
+            if (preprocessor != null) {
+              InputType outputType = preprocessor.getOutputType(inputTypes[0]);
+              layer.getLayer().setNIn(outputType, modelBuilder.isOverrideNinUponBuild());
+            } else layer.getLayer().setNIn(inputTypes[0], modelBuilder.isOverrideNinUponBuild());
+          }
+          if (preprocessor != null) {
+
+            Map<Integer, InputPreProcessor> map = new HashMap<>();
+            map.put(layerIndex, preprocessor);
+            modelBuilder.inputPreProcessors(map);
+          }
         }
 
-
-        //don't forcibly override for keras import
-        modelBuilder.overrideNinUponBuild(false);
-        /* Add layers one at a time. */
-        KerasLayer prevLayer = null;
-        int layerIndex = 0;
-        for (KerasLayer layer : this.layersOrdered) {
-            if (layer.isLayer()) {
-                int nbInbound = layer.getInboundLayerNames().size();
-                if (nbInbound != 1)
-                    throw new InvalidKerasConfigurationException(
-                            "Layers in NeuralNetConfiguration must have exactly one inbound layer (found "
-                                    + nbInbound + " for layer " + layer.getName() + ")");
-                if (prevLayer != null) {
-                    InputType[] inputTypes = new InputType[1];
-                    InputPreProcessor preprocessor;
-                    if (prevLayer.isInputPreProcessor()) {
-                        inputTypes[0] = this.outputTypes.get(prevLayer.getInboundLayerNames().get(0));
-                        preprocessor = prevLayer.getInputPreprocessor(inputTypes);
-                        InputType outputType = preprocessor.getOutputType(inputTypes[0]);
-                        layer.getLayer().setNIn(outputType,modelBuilder.isOverrideNinUponBuild());
-                    } else {
-                        inputTypes[0] = this.outputTypes.get(prevLayer.getName());
-                        preprocessor = layer.getInputPreprocessor(inputTypes);
-                        if(preprocessor != null) {
-                            InputType outputType = preprocessor.getOutputType(inputTypes[0]);
-                            layer.getLayer().setNIn(outputType,modelBuilder.isOverrideNinUponBuild());
-                        }
-                        else
-                            layer.getLayer().setNIn(inputTypes[0],modelBuilder.isOverrideNinUponBuild());
-
-                    }
-                    if (preprocessor != null)
-                        modelBuilder.inputPreProcessor(layerIndex, preprocessor);
-
-
-                }
-
-                modelBuilder.layer(layerIndex++, layer.getLayer());
-            } else if (layer.getVertex() != null)
-                throw new InvalidKerasConfigurationException("Cannot add vertex to NeuralNetConfiguration (class name "
-                        + layer.getClassName() + ", layer name " + layer.getName() + ")");
-            prevLayer = layer;
-        }
-
-        /* Whether to use standard backprop (or BPTT) or truncated BPTT. */
-        if (this.useTruncatedBPTT && this.truncatedBPTT > 0)
-            modelBuilder.backpropType(BackpropType.TruncatedBPTT)
-                .tbpttFwdLength(truncatedBPTT)
-                .tbpttBackLength(truncatedBPTT);
-        else
-            modelBuilder.backpropType(BackpropType.Standard);
-
-        NeuralNetConfiguration build = modelBuilder.build();
-
-
-        return build;
+        modelBuilder.layer(layerIndex++, layer.getLayer());
+      } else if (layer.getVertex() != null)
+        throw new InvalidKerasConfigurationException(
+            "Cannot add vertex to NeuralNetConfiguration (class name "
+                + layer.getClassName()
+                + ", layer name "
+                + layer.getName()
+                + ")");
+      prevLayer = layer;
     }
 
-    /**
-     * Build a MultiLayerNetwork from this Keras Sequential model configuration.
-     *
-     * @return MultiLayerNetwork
-     */
-    public MultiLayerNetwork getMultiLayerNetwork()
-            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
-        return getMultiLayerNetwork(true);
-    }
+    /* Whether to use standard backprop (or BPTT) or truncated BPTT. */
+    if (this.useTruncatedBPTT && this.truncatedBPTT > 0)
+      modelBuilder
+          .backpropType(BackpropType.TruncatedBPTT)
+          .tbpttFwdLength(truncatedBPTT)
+          .tbpttBackLength(truncatedBPTT);
+    else modelBuilder.backpropType(BackpropType.Standard);
 
-    /**
-     * Build a MultiLayerNetwork from this Keras Sequential model configuration and import weights.
-     *
-     * @return MultiLayerNetwork
-     */
-    public MultiLayerNetwork getMultiLayerNetwork(boolean importWeights)
-            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
-        MultiLayerNetwork model = new MultiLayerNetwork(getNeuralNetConfiguration());
-        model.init();
-        if (importWeights)
-            model = (MultiLayerNetwork) KerasModelUtils.copyWeightsToModel(model, this.layers);
-        return model;
-    }
+    NeuralNetConfiguration build = modelBuilder.build();
+
+    return build;
+  }
+
+  /**
+   * Build a MultiLayerNetwork from this Keras Sequential model configuration.
+   *
+   * @return MultiLayerNetwork
+   */
+  public MultiLayerNetwork getMultiLayerNetwork()
+      throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
+    return getMultiLayerNetwork(true);
+  }
+
+  /**
+   * Build a MultiLayerNetwork from this Keras Sequential model configuration and import weights.
+   *
+   * @return MultiLayerNetwork
+   */
+  public MultiLayerNetwork getMultiLayerNetwork(boolean importWeights)
+      throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
+    MultiLayerNetwork model = new MultiLayerNetwork(getNeuralNetConfiguration());
+    model.init();
+    if (importWeights)
+      model = (MultiLayerNetwork) KerasModelUtils.copyWeightsToModel(model, this.layers);
+    return model;
+  }
 }

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution1D.java

@@ -23,7 +23,7 @@ package org.deeplearning4j.nn.modelimport.keras.layers.convolutional;
 import org.deeplearning4j.nn.api.layers.LayerConstraint;
 import org.deeplearning4j.nn.conf.RNNFormat;
 import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
+import org.deeplearning4j.nn.conf.layers.Convolution1D;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@@ -84,29 +84,29 @@ public class KerasAtrousConvolution1D extends KerasConvolution {
         IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
                 enforceTrainingConfig, conf, kerasMajorVersion);
 
-        ConvolutionLayer.ConvolutionLayerBuilder builder = Convolution1DLayer.builder().name(this.name)
+        var builder = Convolution1D.builder().name(this.name)
                 .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
                 .activation(getIActivationFromConfig(layerConfig, conf))
                 .weightInit(init)
                 .dilation(getDilationRate(layerConfig, 1, conf, true)[0])
                 .l1(this.weightL1Regularization).l2(this.weightL2Regularization)
                 .convolutionMode(getConvolutionModeFromConfig(layerConfig, conf))
-                .kernelSize(getKernelSizeFromConfig(layerConfig, 1, conf, kerasMajorVersion)[0])
+                .kernelSize(getKernelSizeFromConfig(layerConfig, 1, conf, kerasMajorVersion))
                 .hasBias(hasBias)
                 .rnnDataFormat(dimOrder == DimOrder.TENSORFLOW ? RNNFormat.NWC : RNNFormat.NCW)
-                .stride(getStrideFromConfig(layerConfig, 1, conf)[0]);
+                .stride(getStrideFromConfig(layerConfig, 1, conf));
         int[] padding = getPaddingFromBorderModeConfig(layerConfig, 1, conf, kerasMajorVersion);
         if (hasBias)
             builder.biasInit(0.0);
         if (padding != null)
-            builder.padding(padding[0]);
+            builder.padding(padding);
         if (biasConstraint != null)
             builder.constrainBias(biasConstraint);
         if (weightConstraint != null)
             builder.constrainWeights(weightConstraint);
         this.layer = builder.build();
-        Convolution1DLayer convolution1DLayer = (Convolution1DLayer) layer;
-        convolution1DLayer.setDefaultValueOverriden(true);
+        Convolution1D convolution1D = (Convolution1D) layer;
+        convolution1D.setDefaultValueOverriden(true);
     }
 
     /**
@@ -114,8 +114,8 @@ public class KerasAtrousConvolution1D extends KerasConvolution {
      *
      * @return ConvolutionLayer
      */
-    public Convolution1DLayer getAtrousConvolution1D() {
-        return (Convolution1DLayer) this.layer;
+    public Convolution1D getAtrousConvolution1D() {
+        return (Convolution1D) this.layer;
     }
 
     /**

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution2D.java

@@ -24,6 +24,7 @@ import lombok.val;
 import org.deeplearning4j.nn.api.layers.LayerConstraint;
 import org.deeplearning4j.nn.conf.CNN2DFormat;
 import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.layers.Convolution2D;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@@ -85,7 +86,7 @@ public class KerasAtrousConvolution2D extends KerasConvolution {
         IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
                 enforceTrainingConfig, conf, kerasMajorVersion);
 
-        val builder = ConvolutionLayer.builder().name(this.name)
+        val builder = Convolution2D.builder().name(this.name)
                 .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
                 .activation(getIActivationFromConfig(layerConfig, conf))
                 .weightInit(init)

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution1D.java

@@ -28,7 +28,7 @@ import org.deeplearning4j.nn.conf.CNN2DFormat;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.RNNFormat;
 import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
+import org.deeplearning4j.nn.conf.layers.Convolution1D;
 import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@@ -93,7 +93,7 @@ public class KerasConvolution1D extends KerasConvolution {
 
         IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
                 enforceTrainingConfig, conf, kerasMajorVersion);
-        Convolution1DLayer.Convolution1DLayerBuilder builder = Convolution1DLayer.builder().name(this.name)
+        var builder = Convolution1D.builder().name(this.name)
                 .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
                 .activation(getIActivationFromConfig(layerConfig, conf))
                 .weightInit(init)
@@ -125,9 +125,9 @@ public class KerasConvolution1D extends KerasConvolution {
 
         this.layer = builder.build();
         //set this in order to infer the dimensional format
-        Convolution1DLayer convolution1DLayer = (Convolution1DLayer) this.layer;
-        convolution1DLayer.setDataFormat(dimOrder == DimOrder.TENSORFLOW ? CNN2DFormat.NHWC : CNN2DFormat.NCHW);
-        convolution1DLayer.setDefaultValueOverriden(true);
+        Convolution1D convolution1D = (Convolution1D) this.layer;
+        convolution1D.setDataFormat(dimOrder == DimOrder.TENSORFLOW ? CNN2DFormat.NHWC : CNN2DFormat.NCHW);
+        convolution1D.setDefaultValueOverriden(true);
     }
 
     /**
@@ -135,8 +135,8 @@ public class KerasConvolution1D extends KerasConvolution {
      *
      * @return  ConvolutionLayer
      */
-    public Convolution1DLayer getConvolution1DLayer() {
-        return (Convolution1DLayer) this.layer;
+    public Convolution1D getConvolution1DLayer() {
+        return (Convolution1D) this.layer;
     }
 
 

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution2D.java

@@ -28,6 +28,7 @@ import org.deeplearning4j.nn.api.layers.LayerConstraint;
 import org.deeplearning4j.nn.conf.CNN2DFormat;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.layers.Convolution2D;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@@ -95,7 +96,7 @@ public class KerasConvolution2D extends KerasConvolution {
         LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
                 layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
 
-        final var builder = ConvolutionLayer.builder().name(this.name)
+        final var builder = Convolution2D.builder().name(this.name)
                 .nOut(getNOutFromConfig(layerConfig, conf)).dropOut(this.dropout)
                 .activation(getIActivationFromConfig(layerConfig, conf))
                 .weightInit(init)

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/JsonTest.java

@@ -23,6 +23,7 @@ package org.deeplearning4j.nn.modelimport.keras.configurations;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.BaseDL4JTest;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.deeplearning4j.nn.modelimport.keras.preprocessors.KerasFlattenRnnPreprocessor;
 import org.deeplearning4j.nn.modelimport.keras.preprocessors.PermutePreprocessor;
 import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor;
@@ -41,8 +42,8 @@ public class JsonTest extends BaseDL4JTest {
 
         };
         for(InputPreProcessor p : pp ){
-            String s = NeuralNetConfiguration.mapper().writeValueAsString(p);
-            InputPreProcessor p2 = NeuralNetConfiguration.mapper().readValue(s, InputPreProcessor.class);
+            String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(p);
+            InputPreProcessor p2 = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(s, InputPreProcessor.class);
             assertEquals(p, p2);
         }
 

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java

@@ -29,11 +29,8 @@ import org.deeplearning4j.gradientcheck.GradientCheckUtil;
 import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.api.layers.IOutputLayer;
 import org.deeplearning4j.nn.conf.ConvolutionMode;
-import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
-import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
-import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
-import org.deeplearning4j.nn.conf.layers.LossLayer;
-import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
+import org.deeplearning4j.nn.conf.layers.*;
+import org.deeplearning4j.nn.conf.layers.Convolution1D;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.nn.modelimport.keras.Hdf5Archive;
@@ -656,7 +653,7 @@ public class KerasModelEndToEndTest extends BaseDL4JTest {
             MultiLayerNetwork net = importEndModelTest(modelPath, inputsOutputPath, true, true,
                     true, true, false, null, null);
             Layer l = net.getLayer(0);
-            Convolution1DLayer c1d = (Convolution1DLayer) l.getTrainingConfig();
+            Convolution1D c1d = (Convolution1D) l.getTrainingConfig();
             assertEquals(ConvolutionMode.Causal, c1d.getConvolutionMode());
         }
     }

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasAtrousConvolution1DTest.java

@@ -22,7 +22,7 @@ package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
 
 import org.deeplearning4j.nn.conf.ConvolutionMode;
 import org.deeplearning4j.nn.conf.dropout.Dropout;
-import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
+import org.deeplearning4j.nn.conf.layers.Convolution1D;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.nn.modelimport.keras.KerasTestUtils;
 import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
@@ -97,7 +97,7 @@ public class KerasAtrousConvolution1DTest extends BaseDL4JTest {
         config.put(conf.getLAYER_FIELD_BORDER_MODE(), BORDER_MODE_VALID);
         layerConfig.put(conf.getLAYER_FIELD_CONFIG(), config);
 
-        Convolution1DLayer layer = new KerasAtrousConvolution1D(layerConfig).getAtrousConvolution1D();
+        Convolution1D layer = new KerasAtrousConvolution1D(layerConfig).getAtrousConvolution1D();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getName());
         assertEquals(INIT_DL4J, layer.getWeightInit());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasConvolution1DTest.java

@@ -22,7 +22,7 @@ package org.deeplearning4j.nn.modelimport.keras.layers.convolution;
 
 import org.deeplearning4j.nn.conf.ConvolutionMode;
 import org.deeplearning4j.nn.conf.dropout.Dropout;
-import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
+import org.deeplearning4j.nn.conf.layers.Convolution1D;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.nn.modelimport.keras.KerasTestUtils;
 import org.deeplearning4j.nn.modelimport.keras.config.Keras1LayerConfiguration;
@@ -119,7 +119,7 @@ public class KerasConvolution1DTest extends BaseDL4JTest {
         config.put(conf.getLAYER_FIELD_BORDER_MODE(), BORDER_MODE_VALID);
         layerConfig.put(conf.getLAYER_FIELD_CONFIG(), config);
 
-        Convolution1DLayer layer = new KerasConvolution1D(layerConfig).getConvolution1DLayer();
+        Convolution1D layer = new KerasConvolution1D(layerConfig).getConvolution1DLayer();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getName());
         assertEquals(INIT_DL4J, layer.getWeightInit());

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/api/INeuralNetworkConfiguration.java

@@ -22,8 +22,6 @@
 package net.brutex.ai.dnn.api;
 
 import java.io.Serializable;
-import java.util.List;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 
 public interface INeuralNetworkConfiguration extends Serializable, Cloneable {
 

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/api/NN.java

@@ -31,9 +31,11 @@ import org.deeplearning4j.nn.conf.layers.DenseLayer;
 public class NN {
 
 
-  public static NeuralNetConfigurationBuilder<?, ?> net() {
+  public static NeuralNetConfigurationBuilder<?, ?> nn() {
     return NeuralNetConfiguration.builder();
   }
 
+  public static DenseLayer.DenseLayerBuilder<?,?> dense() { return DenseLayer.builder(); }
+
 
 }

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/networks/ArtificialNeuralNetwork.java

@@ -23,7 +23,6 @@ package net.brutex.ai.dnn.networks;
 
 import java.io.Serializable;
 import java.util.Arrays;
-import java.util.HashMap;
 import java.util.Map;
 import lombok.Getter;
 import lombok.NonNull;
@@ -33,7 +32,6 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.nd4j.linalg.api.ndarray.INDArray;
 
-
 /**
  * Artificial Neural Network An artificial neural network (1) takes some input data, and (2)
  * transforms this input data by calculating a weighted sum over the inputs and (3) applies a

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/EarlyStoppingConfiguration.java

@@ -20,6 +20,10 @@
 
 package org.deeplearning4j.earlystopping;
 
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
 import lombok.Data;
 import lombok.NoArgsConstructor;
 import net.brutex.ai.dnn.api.IModel;
@@ -30,11 +34,6 @@ import org.deeplearning4j.earlystopping.termination.IterationTerminationConditio
 import org.deeplearning4j.exception.DL4JInvalidConfigException;
 import org.nd4j.common.function.Supplier;
 
-import java.io.Serializable;
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.List;
-
 @Data
 @NoArgsConstructor
 public class EarlyStoppingConfiguration<T extends IModel> implements Serializable {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/EarlyStoppingModelSaver.java

@@ -20,16 +20,15 @@
 
 package org.deeplearning4j.earlystopping;
 
+import com.fasterxml.jackson.annotation.JsonInclude;
+import com.fasterxml.jackson.annotation.JsonSubTypes;
+import com.fasterxml.jackson.annotation.JsonTypeInfo;
+import java.io.IOException;
+import java.io.Serializable;
 import net.brutex.ai.dnn.api.IModel;
 import org.deeplearning4j.earlystopping.saver.InMemoryModelSaver;
 import org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver;
 import org.deeplearning4j.earlystopping.saver.LocalFileModelSaver;
-import com.fasterxml.jackson.annotation.JsonInclude;
-import com.fasterxml.jackson.annotation.JsonSubTypes;
-import com.fasterxml.jackson.annotation.JsonTypeInfo;
-
-import java.io.IOException;
-import java.io.Serializable;
 
 @JsonInclude(JsonInclude.Include.NON_NULL)
 @JsonSubTypes(value = {@JsonSubTypes.Type(value = InMemoryModelSaver.class, name = "InMemoryModelSaver"),

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/EarlyStoppingResult.java

@@ -20,11 +20,10 @@
 
 package org.deeplearning4j.earlystopping;
 
-import lombok.Data;
-import net.brutex.ai.dnn.api.IModel;
-
 import java.io.Serializable;
 import java.util.Map;
+import lombok.Data;
+import net.brutex.ai.dnn.api.IModel;
 
 @Data
 public class EarlyStoppingResult<T extends IModel> implements Serializable {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/saver/InMemoryModelSaver.java

@@ -20,10 +20,9 @@
 
 package org.deeplearning4j.earlystopping.saver;
 
-import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
-import net.brutex.ai.dnn.api.IModel;
-
 import java.io.IOException;
+import net.brutex.ai.dnn.api.IModel;
+import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
 
 public class InMemoryModelSaver<T extends IModel> implements EarlyStoppingModelSaver<T> {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/saver/LocalFileGraphSaver.java

@@ -20,15 +20,14 @@
 
 package org.deeplearning4j.earlystopping.saver;
 
+import java.io.File;
+import java.io.IOException;
+import java.nio.charset.Charset;
 import org.apache.commons.io.FilenameUtils;
 import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.util.ModelSerializer;
 
-import java.io.File;
-import java.io.IOException;
-import java.nio.charset.Charset;
-
 public class LocalFileGraphSaver implements EarlyStoppingModelSaver<ComputationGraph> {
 
     private static final String BEST_GRAPH_BIN = "bestGraph.bin";

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/saver/LocalFileModelSaver.java

@@ -20,15 +20,14 @@
 
 package org.deeplearning4j.earlystopping.saver;
 
+import java.io.File;
+import java.io.IOException;
+import java.nio.charset.Charset;
 import org.apache.commons.io.FilenameUtils;
 import org.deeplearning4j.earlystopping.EarlyStoppingModelSaver;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.util.ModelSerializer;
 
-import java.io.File;
-import java.io.IOException;
-import java.nio.charset.Charset;
-
 public class LocalFileModelSaver implements EarlyStoppingModelSaver<MultiLayerNetwork> {
 
     private static final String BEST_MODEL_BIN = "bestModel.bin";

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/AutoencoderScoreCalculator.java

@@ -26,11 +26,11 @@ import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.nd4j.evaluation.regression.RegressionEvaluation;
 import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
-import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 
 public class AutoencoderScoreCalculator extends BaseScoreCalculator<IModel> {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.java

@@ -20,8 +20,9 @@
 
 package org.deeplearning4j.earlystopping.scorecalc;
 
-import org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator;
+import com.fasterxml.jackson.annotation.JsonProperty;
 import net.brutex.ai.dnn.api.IModel;
+import org.deeplearning4j.earlystopping.scorecalc.base.BaseScoreCalculator;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -29,7 +30,6 @@ import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.dataset.MultiDataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
-import com.fasterxml.jackson.annotation.JsonProperty;
 
 public class DataSetLossCalculator extends BaseScoreCalculator<IModel> {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.java

@@ -20,6 +20,8 @@
 
 package org.deeplearning4j.earlystopping.scorecalc;
 
+import com.fasterxml.jackson.annotation.JsonIgnore;
+import com.fasterxml.jackson.annotation.JsonProperty;
 import lombok.NoArgsConstructor;
 import lombok.val;
 import org.deeplearning4j.nn.graph.ComputationGraph;
@@ -27,8 +29,6 @@ import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.dataset.api.MultiDataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
-import com.fasterxml.jackson.annotation.JsonIgnore;
-import com.fasterxml.jackson.annotation.JsonProperty;
 
 @NoArgsConstructor
 @Deprecated

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/ScoreCalculator.java

@@ -20,12 +20,11 @@
 
 package org.deeplearning4j.earlystopping.scorecalc;
 
-import net.brutex.ai.dnn.api.IModel;
 import com.fasterxml.jackson.annotation.JsonInclude;
 import com.fasterxml.jackson.annotation.JsonSubTypes;
 import com.fasterxml.jackson.annotation.JsonTypeInfo;
-
 import java.io.Serializable;
+import net.brutex.ai.dnn.api.IModel;
 
 @JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
 @JsonInclude(JsonInclude.Include.NON_NULL)

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/VAEReconErrorScoreCalculator.java

@@ -26,11 +26,11 @@ import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.layers.variational.VariationalAutoencoder;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.nd4j.evaluation.regression.RegressionEvaluation;
 import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
-import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 
 public class VAEReconErrorScoreCalculator extends BaseScoreCalculator<IModel> {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/base/BaseIEvaluationScoreCalculator.java

@@ -20,9 +20,9 @@
 
 package org.deeplearning4j.earlystopping.scorecalc.base;
 
+import net.brutex.ai.dnn.api.IModel;
 import org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator;
 import org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator;
-import net.brutex.ai.dnn.api.IModel;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.nd4j.evaluation.IEvaluation;

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/scorecalc/base/BaseScoreCalculator.java

@@ -21,8 +21,8 @@
 package org.deeplearning4j.earlystopping.scorecalc.base;
 
 import lombok.NonNull;
-import org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator;
 import net.brutex.ai.dnn.api.IModel;
+import org.deeplearning4j.earlystopping.scorecalc.ScoreCalculator;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.dataset.api.MultiDataSet;

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/BestScoreEpochTerminationCondition.java

@@ -20,8 +20,8 @@
 
 package org.deeplearning4j.earlystopping.termination;
 
-import lombok.Data;
 import com.fasterxml.jackson.annotation.JsonProperty;
+import lombok.Data;
 
 @Data
 public class BestScoreEpochTerminationCondition implements EpochTerminationCondition {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/EpochTerminationCondition.java

@@ -22,9 +22,7 @@ package org.deeplearning4j.earlystopping.termination;
 
 
 import com.fasterxml.jackson.annotation.JsonInclude;
-import com.fasterxml.jackson.annotation.JsonSubTypes;
 import com.fasterxml.jackson.annotation.JsonTypeInfo;
-
 import java.io.Serializable;
 
 @JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/IterationTerminationCondition.java

@@ -22,7 +22,6 @@ package org.deeplearning4j.earlystopping.termination;
 
 import com.fasterxml.jackson.annotation.JsonInclude;
 import com.fasterxml.jackson.annotation.JsonTypeInfo;
-
 import java.io.Serializable;
 
 @JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/MaxEpochsTerminationCondition.java

@@ -20,10 +20,10 @@
 
 package org.deeplearning4j.earlystopping.termination;
 
-import lombok.Data;
-import lombok.NoArgsConstructor;
 import com.fasterxml.jackson.annotation.JsonCreator;
 import com.fasterxml.jackson.annotation.JsonProperty;
+import lombok.Data;
+import lombok.NoArgsConstructor;
 
 @NoArgsConstructor
 @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/MaxScoreIterationTerminationCondition.java

@@ -20,8 +20,8 @@
 
 package org.deeplearning4j.earlystopping.termination;
 
-import lombok.Data;
 import com.fasterxml.jackson.annotation.JsonProperty;
+import lombok.Data;
 
 @Data
 public class MaxScoreIterationTerminationCondition implements IterationTerminationCondition {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/MaxTimeIterationTerminationCondition.java

@@ -20,10 +20,9 @@
 
 package org.deeplearning4j.earlystopping.termination;
 
-import lombok.Data;
 import com.fasterxml.jackson.annotation.JsonProperty;
-
 import java.util.concurrent.TimeUnit;
+import lombok.Data;
 
 /**Terminate training based on max time.
  */

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/termination/ScoreImprovementEpochTerminationCondition.java

@@ -20,9 +20,9 @@
 
 package org.deeplearning4j.earlystopping.termination;
 
+import com.fasterxml.jackson.annotation.JsonProperty;
 import lombok.Data;
 import lombok.extern.slf4j.Slf4j;
-import com.fasterxml.jackson.annotation.JsonProperty;
 
 @Slf4j
 @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/trainer/BaseEarlyStoppingTrainer.java

@@ -20,6 +20,12 @@
 
 package org.deeplearning4j.earlystopping.trainer;
 
+import java.io.FileNotFoundException;
+import java.io.IOException;
+import java.util.Collection;
+import java.util.Iterator;
+import java.util.LinkedHashMap;
+import java.util.Map;
 import net.brutex.ai.dnn.api.IModel;
 import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;
 import org.deeplearning4j.earlystopping.EarlyStoppingResult;
@@ -40,13 +46,6 @@ import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import java.io.FileNotFoundException;
-import java.io.IOException;
-import java.util.Collection;
-import java.util.Iterator;
-import java.util.LinkedHashMap;
-import java.util.Map;
-
 public abstract class BaseEarlyStoppingTrainer<T extends IModel> implements IEarlyStoppingTrainer<T> {
 
     private static final Logger log = LoggerFactory.getLogger(BaseEarlyStoppingTrainer.class);

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/trainer/EarlyStoppingGraphTrainer.java

@@ -20,7 +20,6 @@
 
 package org.deeplearning4j.earlystopping.trainer;
 
-import org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator;
 import org.deeplearning4j.datasets.iterator.impl.SingletonDataSetIterator;
 import org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator;
 import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/BaseEvaluation.java

@@ -20,6 +20,13 @@
 
 package org.deeplearning4j.eval;
 
+import com.fasterxml.jackson.annotation.JsonAutoDetect;
+import com.fasterxml.jackson.databind.DeserializationFeature;
+import com.fasterxml.jackson.databind.MapperFeature;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.fasterxml.jackson.databind.SerializationFeature;
+import com.fasterxml.jackson.databind.module.SimpleModule;
+import com.fasterxml.jackson.dataformat.yaml.YAMLFactory;
 import lombok.EqualsAndHashCode;
 import lombok.Getter;
 import org.nd4j.common.primitives.AtomicBoolean;
@@ -28,14 +35,6 @@ import org.nd4j.common.primitives.serde.JsonDeserializerAtomicBoolean;
 import org.nd4j.common.primitives.serde.JsonDeserializerAtomicDouble;
 import org.nd4j.common.primitives.serde.JsonSerializerAtomicBoolean;
 import org.nd4j.common.primitives.serde.JsonSerializerAtomicDouble;
-import com.fasterxml.jackson.annotation.JsonAutoDetect;
-import com.fasterxml.jackson.core.JsonProcessingException;
-import com.fasterxml.jackson.databind.DeserializationFeature;
-import com.fasterxml.jackson.databind.MapperFeature;
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.fasterxml.jackson.databind.SerializationFeature;
-import com.fasterxml.jackson.databind.module.SimpleModule;
-import com.fasterxml.jackson.dataformat.yaml.YAMLFactory;
 
 @Deprecated
 @EqualsAndHashCode(callSuper = false)

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/ConfusionMatrix.java

@@ -20,15 +20,8 @@
 
 package org.deeplearning4j.eval;
 
-import com.google.common.collect.HashMultiset;
-import com.google.common.collect.Multiset;
-import lombok.Getter;
 
-import java.io.Serializable;
-import java.util.ArrayList;
 import java.util.List;
-import java.util.Map;
-import java.util.concurrent.ConcurrentHashMap;
 
 @Deprecated
 public class ConfusionMatrix<T extends Comparable<? super T>> extends org.nd4j.evaluation.classification.ConfusionMatrix<T> {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/Evaluation.java

@@ -20,14 +20,11 @@
 
 package org.deeplearning4j.eval;
 
-import lombok.EqualsAndHashCode;
-import lombok.NonNull;
-import org.nd4j.evaluation.EvaluationAveraging;
-import org.nd4j.evaluation.IEvaluation;
-import org.nd4j.linalg.api.ndarray.INDArray;
-
 import java.util.List;
 import java.util.Map;
+import lombok.EqualsAndHashCode;
+import lombok.NonNull;
+import org.nd4j.linalg.api.ndarray.INDArray;
 
 @EqualsAndHashCode(callSuper = true)
 @Deprecated

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/EvaluationCalibration.java

@@ -20,9 +20,9 @@
 
 package org.deeplearning4j.eval;
 
+import com.fasterxml.jackson.annotation.JsonProperty;
 import lombok.EqualsAndHashCode;
 import lombok.Getter;
-import com.fasterxml.jackson.annotation.JsonProperty;
 
 @Deprecated
 @Getter

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/RegressionEvaluation.java

@@ -20,11 +20,10 @@
 
 package org.deeplearning4j.eval;
 
+import java.util.List;
 import lombok.Data;
 import lombok.EqualsAndHashCode;
 
-import java.util.List;
-
 @Deprecated
 @Data
 @EqualsAndHashCode(callSuper = true)

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/curves/Histogram.java

@@ -20,10 +20,10 @@
 
 package org.deeplearning4j.eval.curves;
 
+import com.fasterxml.jackson.annotation.JsonProperty;
 import lombok.Data;
 import lombok.EqualsAndHashCode;
 import org.nd4j.evaluation.curves.BaseHistogram;
-import com.fasterxml.jackson.annotation.JsonProperty;
 
 @Deprecated
 @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/curves/PrecisionRecallCurve.java

@@ -20,13 +20,9 @@
 
 package org.deeplearning4j.eval.curves;
 
-import com.google.common.base.Preconditions;
-import lombok.AllArgsConstructor;
+import com.fasterxml.jackson.annotation.JsonProperty;
 import lombok.Data;
 import lombok.EqualsAndHashCode;
-import com.fasterxml.jackson.annotation.JsonProperty;
-
-import java.util.Arrays;
 
 @Deprecated
 @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/curves/ReliabilityDiagram.java

@@ -20,8 +20,8 @@
 
 package org.deeplearning4j.eval.curves;
 
-import lombok.NonNull;
 import com.fasterxml.jackson.annotation.JsonProperty;
+import lombok.NonNull;
 
 @Deprecated
 public class ReliabilityDiagram extends org.nd4j.evaluation.curves.ReliabilityDiagram {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/curves/RocCurve.java

@@ -20,10 +20,9 @@
 
 package org.deeplearning4j.eval.curves;
 
-import com.google.common.base.Preconditions;
+import com.fasterxml.jackson.annotation.JsonProperty;
 import lombok.Data;
 import lombok.EqualsAndHashCode;
-import com.fasterxml.jackson.annotation.JsonProperty;
 
 @Deprecated
 @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/eval/meta/Prediction.java

@@ -20,7 +20,6 @@
 
 package org.deeplearning4j.eval.meta;
 
-import lombok.AllArgsConstructor;
 import lombok.Data;
 
 @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/adapters/YoloModelAdapter.java

@@ -20,6 +20,7 @@
 
 package org.deeplearning4j.nn.adapters;
 
+import java.util.List;
 import lombok.AllArgsConstructor;
 import lombok.Builder;
 import lombok.NoArgsConstructor;
@@ -32,8 +33,6 @@ import org.deeplearning4j.nn.layers.objdetect.Yolo2OutputLayer;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.exception.ND4JIllegalStateException;
 
-import java.util.List;
-
 @Builder
 @AllArgsConstructor
 @NoArgsConstructor

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/AbstractParamInitializer.java

@@ -21,7 +21,6 @@
 
 package org.deeplearning4j.nn.api;
 
-import lombok.Getter;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Classifier.java

@@ -20,14 +20,12 @@
 
 package org.deeplearning4j.nn.api;
 
+import java.util.List;
 import net.brutex.ai.dnn.api.IModel;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.api.DataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 
-import java.util.List;
-
-
 public interface Classifier extends IModel {
 
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/ITraininableLayerConfiguration.java

@@ -20,13 +20,12 @@
 
 package org.deeplearning4j.nn.api;
 
+import java.util.List;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.learning.regularization.Regularization;
 
-import java.util.List;
-
 public interface ITraininableLayerConfiguration {
 
     /**

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Layer.java

@@ -21,7 +21,7 @@
 package org.deeplearning4j.nn.api;
 
 
-import java.util.Map;
+import java.io.Serializable;
 import net.brutex.ai.dnn.api.IModel;
 import org.deeplearning4j.nn.conf.CacheMode;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@@ -29,10 +29,8 @@ import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.layers.LayerHelper;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
-import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.common.primitives.Pair;
-
-import java.io.Serializable;
+import org.nd4j.linalg.api.ndarray.INDArray;
 
 /**
  * A layer is the highest-level building block in deep learning. A layer is a container that usually

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/ParamInitializer.java

@@ -20,13 +20,12 @@
 
 package org.deeplearning4j.nn.api;
 
+import java.util.List;
+import java.util.Map;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.nd4j.linalg.api.ndarray.INDArray;
 
-import java.util.List;
-import java.util.Map;
-
 /**
  * Param initializer for a layer
  *

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Updater.java

@@ -20,11 +20,10 @@
 
 package org.deeplearning4j.nn.api;
 
-import org.deeplearning4j.nn.gradient.Gradient;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
-
 import java.io.Serializable;
+import org.deeplearning4j.nn.gradient.Gradient;
+import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
+import org.nd4j.linalg.api.ndarray.INDArray;
 
 /**
  * Update the model

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/IOutputLayer.java

@@ -22,8 +22,8 @@ package org.deeplearning4j.nn.api.layers;
 
 import org.deeplearning4j.nn.api.Classifier;
 import org.deeplearning4j.nn.api.Layer;
-import org.nd4j.linalg.api.ndarray.INDArray;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
+import org.nd4j.linalg.api.ndarray.INDArray;
 
 public interface IOutputLayer extends Layer, Classifier {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/LayerConstraint.java

@@ -20,11 +20,10 @@
 
 package org.deeplearning4j.nn.api.layers;
 
-import org.deeplearning4j.nn.api.Layer;
 import com.fasterxml.jackson.annotation.JsonTypeInfo;
-
 import java.io.Serializable;
 import java.util.Set;
+import org.deeplearning4j.nn.api.Layer;
 
 @JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
 public interface LayerConstraint extends Cloneable, Serializable {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/RecurrentLayer.java

@@ -20,13 +20,12 @@
 
 package org.deeplearning4j.nn.api.layers;
 
+import java.util.Map;
 import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.gradient.Gradient;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.common.primitives.Pair;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
-
-import java.util.Map;
+import org.nd4j.common.primitives.Pair;
+import org.nd4j.linalg.api.ndarray.INDArray;
 
 public interface RecurrentLayer extends Layer {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.java

@@ -20,6 +20,12 @@
 
 package org.deeplearning4j.nn.conf;
 
+import com.fasterxml.jackson.databind.JsonNode;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
+import java.io.IOException;
+import java.io.Serializable;
+import java.util.*;
 import lombok.*;
 import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.graph.GraphVertex;
@@ -34,6 +40,7 @@ import org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep;
 import org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex;
 import org.deeplearning4j.nn.conf.memory.MemoryReport;
 import org.deeplearning4j.nn.conf.memory.NetworkMemoryReport;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.deeplearning4j.nn.conf.serde.JsonMappers;
 import org.deeplearning4j.nn.weights.IWeightInit;
 import org.deeplearning4j.nn.weights.WeightInit;
@@ -42,16 +49,9 @@ import org.nd4j.common.base.Preconditions;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.IActivation;
 import org.nd4j.linalg.api.buffer.DataType;
-import com.fasterxml.jackson.databind.JsonNode;
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import java.io.IOException;
-import java.io.Serializable;
-import java.util.*;
-
 @Data
 @EqualsAndHashCode(exclude = {"trainingWorkspaceMode", "inferenceWorkspaceMode", "cacheMode", "topologicalOrder", "topologicalOrderStr"})
 @AllArgsConstructor(access = AccessLevel.PRIVATE)
@@ -110,7 +110,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
      * @return YAML representation of configuration
      */
     public String toYaml() {
-        ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
+        ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.YAML);
         synchronized (mapper) {
             try {
                 return mapper.writeValueAsString(this);
@@ -127,7 +127,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
      * @return {@link ComputationGraphConfiguration}
      */
     public static ComputationGraphConfiguration fromYaml(String json) {
-        ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
+        ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.YAML);
         try {
             return mapper.readValue(json, ComputationGraphConfiguration.class);
         } catch (IOException e) {
@@ -140,7 +140,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
      */
     public String toJson() {
         //As per NeuralNetConfiguration.toJson()
-        ObjectMapper mapper = NeuralNetConfiguration.mapper();
+        ObjectMapper mapper =CavisMapper.getMapper(CavisMapper.Type.JSON);
         synchronized (mapper) {
             //JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields occasionally
             //when writeValueAsString is used by multiple threads. This results in invalid JSON. See issue #3243
@@ -160,7 +160,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
      */
     public static ComputationGraphConfiguration fromJson(String json) {
         //As per NeuralNetConfiguration.fromJson()
-        ObjectMapper mapper = NeuralNetConfiguration.mapper();
+        ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
         ComputationGraphConfiguration conf;
         try {
             conf = mapper.readValue(json, ComputationGraphConfiguration.class);

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/DataFormat.java

@@ -19,10 +19,10 @@
  */
 package org.deeplearning4j.nn.conf;
 
-import org.deeplearning4j.nn.conf.serde.format.DataFormatDeserializer;
-import org.deeplearning4j.nn.conf.serde.format.DataFormatSerializer;
 import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
 import com.fasterxml.jackson.databind.annotation.JsonSerialize;
+import org.deeplearning4j.nn.conf.serde.format.DataFormatDeserializer;
+import org.deeplearning4j.nn.conf.serde.format.DataFormatSerializer;
 
 @JsonSerialize(using = DataFormatSerializer.class)
 @JsonDeserialize(using = DataFormatDeserializer.class)

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/InputPreProcessor.java

@@ -21,14 +21,13 @@
 package org.deeplearning4j.nn.conf;
 
 
+import com.fasterxml.jackson.annotation.JsonTypeInfo;
+import java.io.Serializable;
 import org.deeplearning4j.nn.api.MaskState;
 import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.common.primitives.Pair;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
-import com.fasterxml.jackson.annotation.JsonTypeInfo;
-
-import java.io.Serializable;
+import org.nd4j.common.primitives.Pair;
+import org.nd4j.linalg.api.ndarray.INDArray;
 
 @JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
 public interface InputPreProcessor extends Serializable, Cloneable {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/NeuralNetBaseBuilderConfiguration.java

@@ -21,10 +21,9 @@
 package org.deeplearning4j.nn.conf;
 
 import com.fasterxml.jackson.annotation.JsonIgnore;
+import com.fasterxml.jackson.annotation.JsonProperty;
 import com.fasterxml.jackson.annotation.JsonTypeInfo;
-import com.fasterxml.jackson.databind.JsonNode;
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.fasterxml.jackson.databind.node.ArrayNode;
+import java.util.*;
 import lombok.*;
 import lombok.experimental.SuperBuilder;
 import lombok.extern.slf4j.Slf4j;
@@ -35,10 +34,8 @@ import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.dropout.Dropout;
 import org.deeplearning4j.nn.conf.dropout.IDropout;
 import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
-import org.deeplearning4j.nn.conf.serde.JsonMappers;
 import org.deeplearning4j.nn.conf.stepfunctions.StepFunction;
 import org.deeplearning4j.nn.conf.weightnoise.IWeightNoise;
 import org.deeplearning4j.nn.weights.IWeightInit;
@@ -47,7 +44,6 @@ import org.deeplearning4j.nn.weights.WeightInitDistribution;
 import org.deeplearning4j.nn.weights.WeightInitXavier;
 import org.deeplearning4j.util.NetworkUtils;
 import org.nd4j.common.base.Preconditions;
-import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.IActivation;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.learning.config.IUpdater;
@@ -57,9 +53,6 @@ import org.nd4j.linalg.learning.regularization.L2Regularization;
 import org.nd4j.linalg.learning.regularization.Regularization;
 import org.nd4j.linalg.learning.regularization.WeightDecay;
 
-import java.io.IOException;
-import java.util.*;
-
 /**
  * Deeplearning4j is a domain-specific language to configure deep neural networks, which are made of
  * multiple layers. Everything starts with a NeuralNetConfiguration, which organizes those layers
@@ -159,7 +152,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
   @Getter @Setter @NonNull @lombok.Builder.Default
   protected BackpropType backpropType = BackpropType.Standard;
 
-  @Getter @lombok.Builder.Default
+  @Getter @Setter @Singular
   protected Map<Integer, InputPreProcessor> inputPreProcessors = new HashMap<>();
   /**
    * When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated)
@@ -331,7 +324,6 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
    */
   @Getter @Setter @lombok.Builder.Default private IUpdater biasUpdater = null;
 
-
   /**
    * Weight initialization scheme to use, for initial weight values Note: values set by this method
    * will be applied to all applicable layers in the network, unless a different value is explicitly
@@ -339,6 +331,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
    * and can be overridden on a per-layer basis.
    */
   @Getter @Setter @lombok.Builder.Default private IWeightInit weightInit = new WeightInitXavier();
+  
   /**
    * Sets the convolution mode for convolutional layers, which impacts padding and output sizes. See
    * {@link ConvolutionMode} for details. Defaults to ConvolutionMode.TRUNCATE<br>
@@ -416,113 +409,6 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
   @Getter @Setter @lombok.Builder.Default private double biasInit = 0.0;
   @Getter @Setter @lombok.Builder.Default private double gainInit = 1.0;
 
-  /**
-   * Handle {@link WeightInit} and {@link Distribution} from legacy configs in Json format. Copied
-   * from handling of {@link Activation} above.
-   *
-   * @return True if all is well and layer iteration shall continue. False else-wise.
-   */
-  private static boolean handleLegacyWeightInitFromJson(
-      String json, LayerConfiguration l, ObjectMapper mapper, JsonNode confs, int layerCount) {
-    if ((l instanceof BaseLayerConfiguration)
-        && ((BaseLayerConfiguration) l).getWeightInit() == null) {
-      try {
-        JsonNode jsonNode = mapper.readTree(json);
-        if (confs == null) {
-          confs = jsonNode.get("confs");
-        }
-        if (confs instanceof ArrayNode) {
-          ArrayNode layerConfs = (ArrayNode) confs;
-          JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
-          if (outputLayerNNCNode == null) {
-            return false; // Should never happen...
-          }
-          JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
-
-          if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
-            return true;
-          }
-
-          JsonNode layerNode = layerWrapperNode.elements().next();
-          JsonNode weightInit =
-              layerNode.get("weightInit"); // Should only have 1 element: "dense", "output", etc
-          JsonNode distribution = layerNode.get("dist");
-
-          Distribution dist = null;
-          if (distribution != null) {
-            dist = mapper.treeToValue(distribution, Distribution.class);
-          }
-
-          if (weightInit != null) {
-            final IWeightInit wi =
-                WeightInit.valueOf(weightInit.asText()).getWeightInitFunction(dist);
-            ((BaseLayerConfiguration) l).setWeightInit(wi);
-          }
-        }
-
-      } catch (IOException e) {
-        log.warn(
-            "ILayer with null WeightInit detected: " + l.getName() + ", could not parse JSON",
-            e);
-      }
-    }
-    return true;
-  }
-
-  /**
-   * Object mapper for serialization of configurations
-   *
-   * @return
-   */
-  public static ObjectMapper mapperYaml() {
-    return JsonMappers.getMapperYaml();
-  }
-
-  /**
-   * Object mapper for serialization of configurations
-   *
-   * @return
-   */
-  public static ObjectMapper mapper() {
-    return JsonMappers.getMapper();
-  }
-
-  public static NeuralNetBaseBuilderConfiguration fromYaml(String input) {
-    throw new RuntimeException("Needs fixing - not supported."); // TODO
-  }
-
-  /**
-   * @return JSON representation of NN configuration
-   */
-  public String toYaml() {
-    ObjectMapper mapper = NeuralNetBaseBuilderConfiguration.mapperYaml();
-    synchronized (mapper) {
-      try {
-        return mapper.writeValueAsString(this);
-      } catch (com.fasterxml.jackson.core.JsonProcessingException e) {
-        throw new RuntimeException(e);
-      }
-    }
-  }
-
-  /**
-   * @return JSON representation of NN configuration
-   */
-  public String toJson() {
-    ObjectMapper mapper = NeuralNetBaseBuilderConfiguration.mapper();
-    synchronized (mapper) {
-      // JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields
-      // occasionally
-      // when writeValueAsString is used by multiple threads. This results in invalid JSON. See
-      // issue #3243
-      try {
-        return mapper.writeValueAsString(this);
-      } catch (com.fasterxml.jackson.core.JsonProcessingException e) {
-        throw new RuntimeException(e);
-      }
-    }
-  }
-
   @Override
   public NeuralNetBaseBuilderConfiguration clone() {
     NeuralNetBaseBuilderConfiguration clone;
@@ -561,14 +447,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
 
     List<Object> innerConfigurations$value = new ArrayList<>(); // initialize with an empty list
 
-    public B activation(Activation activation) {
-      this.activation = activation;
-      return self();
-    }
-    public B activation(IActivation activation) {
-      this.activation = activation;
-      return self();
-    }
+
     /**
      * Set constraints to be applied to all layers. Default: no constraints.<br>
      * Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm
@@ -583,7 +462,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
     public B constrainWeights(LayerConstraint... constraints) {
       constrainWeights$value = Arrays.asList(constraints);
       constrainWeights$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -618,7 +497,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
     public B constrainAllParameters(LayerConstraint... constraints) {
       allParamConstraints$value = Arrays.asList(constraints);
       allParamConstraints$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -635,7 +514,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
     public B constrainBias(LayerConstraint... constraints) {
       biasConstraints$value = Arrays.asList(constraints);
       biasConstraints$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -645,11 +524,11 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      * @param processor what to use to preProcess the data.
      * @return builder pattern
      */
-    public B inputPreProcessor(Integer layer, InputPreProcessor processor) {
-      inputPreProcessors$value.put(layer, processor);
-      inputPreProcessors$set = true;
-      return (B) this;
-    }
+   //public B inputPreProcessor(@NonNull Integer layer, @NonNull InputPreProcessor processor) {
+   //   inputPreProcessors$value.put(layer, processor);
+   //   inputPreProcessors$set = true;
+   //   return self();
+   // }
 
     /**
      * Set layer at index
@@ -658,7 +537,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      * @param layer the layer
      * @return builder
      */
-    public B layer(Integer index, @NonNull LayerConfiguration layer) {
+    public B layer(@NonNull Integer index, @NonNull LayerConfiguration layer) {
       innerConfigurations$value.add(index, layer);
       innerConfigurations$set = true;
       return self();
@@ -680,10 +559,11 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      * @param layer the layer
      * @return builder
      */
+    @JsonIgnore
     public B layer(@NonNull LayerConfiguration layer) {
       innerConfigurations$value.add(layer);
       innerConfigurations$set = true;
-      return (B) this;
+      return self();
     }
     public B layer(@NonNull LayerConfiguration.LayerConfigurationBuilder<?, ?> layer) {
       return this.layer(layer.build());
@@ -699,7 +579,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
     public B layersFromArray(@NonNull LayerConfiguration[] arrLayers) {
       innerConfigurations$value.addAll(List.of(arrLayers));
       innerConfigurations$set = true;
-      return (B) this;
+      return self();
     }
 
     /** Specify additional layer configurations */
@@ -707,7 +587,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
     public B layersFromList(@NonNull List<LayerConfiguration> listLayers) {
       innerConfigurations$value.addAll(listLayers);
       innerConfigurations$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -723,7 +603,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
         regularization$value.add(new L1Regularization(l1));
       }
       regularization$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -751,7 +631,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
         regularization$value.add(new L2Regularization(l2));
       }
       regularization$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -766,7 +646,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
         regularizationBias$value.add(new L1Regularization(l1Bias));
       }
       regularizationBias$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -791,7 +671,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
             "L2 bias regularization removed: incompatible with added WeightDecay regularization");
         regularizationBias$value.add(new L2Regularization(l2Bias));
       }
-      return (B) this;
+      return self();
     }
 
     /**
@@ -833,7 +713,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
         regularization$value.add(new WeightDecay(coefficient, applyLR));
       }
       regularization$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -870,7 +750,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
         regularizationBias$value.add(new WeightDecay(coefficient, applyLR));
       }
       regularization$set = true;
-      return (B) this;
+      return self();
     }
 
 
@@ -881,7 +761,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      */
     @Deprecated
     public B list() {
-      return (B) this;
+      return self();
     }
 
     /**
@@ -893,23 +773,24 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      *
      * @param distribution Distribution to use for weight initialization
      */
-    @JsonIgnore
+    @JsonIgnore @Deprecated
     public B weightInit(Distribution distribution) {
       this.weightInit$value = new WeightInitDistribution(distribution);
       this.weightInit$set = true;
-      return (B) this;
+      return self();
     }
     @JsonIgnore
     public B weightInit(WeightInit weightInit) {
       this.weightInit$value = weightInit.getWeightInitFunction();
       this.weightInit$set = true;
-      return (B) this;
+      return self();
     }
 
+    @JsonProperty("weightInit") //this is needed for Jackson < 2.4, otherwise JsonIgnore on the other setters will ignore this also
     public B weightInit(IWeightInit iWeightInit) {
       this.weightInit$value = iWeightInit;
       this.weightInit$set = true;
-      return (B) this;
+      return self();
     }
 
     /**
@@ -918,12 +799,13 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      * @param distribution
      * @return
      */
+    @JsonIgnore
     public B dist(@NonNull Distribution distribution) {
-      return (B) weightInit(distribution);
+      return weightInit(distribution);
     }
 
     public B dropOut(@NonNull IDropout dropout) {
-      return (B) idropOut(dropout);
+      return idropOut(dropout);
     }
 
     /**
@@ -933,7 +815,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      * @return builder
      */
     public B dropOut(double dropout) {
-      return (B) idropOut(new Dropout(dropout));
+      return idropOut(new Dropout(dropout));
     }
 
     /**
@@ -946,7 +828,8 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
     public B confs(@NonNull List<NeuralNetConfiguration> confs) {
       innerConfigurations$value.addAll(confs);
       innerConfigurations$set = true;
-      return (B) this;
+      return self();
     }
+
   }
 }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/NeuralNetConfiguration.java

@@ -22,39 +22,27 @@ package org.deeplearning4j.nn.conf;
 
 import com.fasterxml.jackson.annotation.JsonIgnore;
 import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
-import com.fasterxml.jackson.databind.JsonNode;
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
-import com.fasterxml.jackson.databind.node.ArrayNode;
+import com.fasterxml.jackson.core.JsonProcessingException;
+import com.fasterxml.jackson.databind.*;
+import java.util.*;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.stream.Collectors;
 import lombok.*;
 import lombok.experimental.SuperBuilder;
 import lombok.extern.jackson.Jacksonized;
 import lombok.extern.slf4j.Slf4j;
 import net.brutex.ai.dnn.api.IModel;
-import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep;
 import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
 import org.deeplearning4j.nn.conf.memory.MemoryReport;
 import org.deeplearning4j.nn.conf.memory.NetworkMemoryReport;
-import org.deeplearning4j.nn.conf.serde.JsonMappers;
-import org.deeplearning4j.nn.weights.IWeightInit;
-import org.deeplearning4j.nn.weights.WeightInit;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.deeplearning4j.util.OutputLayerUtil;
-import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.learning.config.Sgd;
-import org.nd4j.linalg.lossfunctions.LossFunctions;
-import org.nd4j.linalg.lossfunctions.impl.LossBinaryXENT;
-import org.nd4j.linalg.lossfunctions.impl.LossMCXENT;
-import org.nd4j.linalg.lossfunctions.impl.LossMSE;
-import org.nd4j.linalg.lossfunctions.impl.LossNegativeLogLikelihood;
-
-import java.io.IOException;
-import java.util.*;
-import java.util.stream.Collectors;
 
 /**
  * Deeplearning4j is a domain-specific language to configure deep neural networks, which are made of
@@ -62,71 +50,50 @@ import java.util.stream.Collectors;
  * and their hyperparameters. Hyperparameters are variables that determine how a neural network
  * learns. They include how many times to update the weights of the model, how to initialize those
  * weights, which activation function to attach to the nodes, which optimization algorithm to use,
- * and how fast the model should learn. This is what one configuration would look like:
- * <br/><br/>
- *
- * NeuralNetConfiguration conf = NeuralNetConfiguration.builder()<br/>
- * .weightInit(WeightInit.XAVIER) .activation(Activation.RELU)<br/>
- * .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)<br/>
- * .updater(new Sgd(0.05)) //... other hyperparameters <br/>
- *  .backprop(true)<br/>
- * .build();<br/><br/>
- *
- * With Deeplearning4j, you add a layer
- * by calling layer on the NeuralNetConfiguration.NeuralNetConfigurationBuilder(), specifying its place in the order of
+ * and how fast the model should learn. This is what one configuration would look like: <br>
+ * <br>
+ * NeuralNetConfiguration conf = NeuralNetConfiguration.builder()<br>
+ * .weightInit(WeightInit.XAVIER) .activation(Activation.RELU)<br>
+ * .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)<br>
+ * .updater(new Sgd(0.05)) //... other hyperparameters <br>
+ * .backprop(true)<br>
+ * .build();<br>
+ * <br>
+ * With Deeplearning4j, you add a layer by calling layer on the
+ * NeuralNetConfiguration.NeuralNetConfigurationBuilder(), specifying its place in the order of
  * layers (the zero-indexed layer below is the input layer), the number of input and output nodes,
- * nIn and nOut, as well as the type: DenseLayer.<br/><br/>
- *
- * .layer(0, DenseLayer.builder().nIn(784).nOut(250)<br/>
- * .build())<br/><br/>
- *
- * Once you've configured your net, you train the
- * model with model.fit.
+ * nIn and nOut, as well as the type: DenseLayer.<br>
+ * <br>
+ * .layer(0, DenseLayer.builder().nIn(784).nOut(250)<br>
+ * .build())<br>
+ * <br>
+ * Once you've configured your net, you train the model with model.fit.
  */
-
-
 @Data
 @Slf4j
-@Jacksonized
-@JsonIgnoreProperties(value={"net"}, ignoreUnknown = true)
-@EqualsAndHashCode(exclude = {"net"}, callSuper = true)
-//@JsonIdentityInfo(generator= ObjectIdGenerators.IntSequenceGenerator.class, property="@id")
+@JsonIgnoreProperties(value = {"net"})
+@EqualsAndHashCode(callSuper = true)
+// @JsonIdentityInfo(generator= ObjectIdGenerators.IntSequenceGenerator.class, property="@id")
 
-//The inner builder, that we can then extend ...
-@SuperBuilder //TODO fix access
+// The inner builder, that we can then extend ...
+@Jacksonized
+@SuperBuilder // TODO fix access
 public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
 
-
-  private IModel net;
   private static final int DEFAULT_TBPTT_LENGTH = 20;
-  private boolean initCalled = false;
 
-
-  @Getter
-  @Setter
-  @NonNull
-  @lombok.Builder.Default
-  @Deprecated
+  @Getter @Setter @NonNull @lombok.Builder.Default @Deprecated
   protected WorkspaceMode trainingWorkspaceMode = WorkspaceMode.ENABLED;
-  @Getter
-  @Setter
-  @NonNull
-  @lombok.Builder.Default
-  @Deprecated
+
+  @Getter @Setter @NonNull @lombok.Builder.Default @Deprecated
   protected WorkspaceMode inferenceWorkspaceMode = WorkspaceMode.ENABLED;
 
-
-  @Getter
-  @Setter
-  @lombok.Builder.Default
-  protected int iterationCount = 0;
-  //Counter for the number of epochs completed so far. Used for per-epoch schedules
-  @Getter
-  @Setter
-  @lombok.Builder.Default
-  protected int epochCount = 0;
-  @lombok.Builder.Default
-  protected double dampingFactor = 100;
+  @Getter @Setter @lombok.Builder.Default protected int iterationCount = 0;
+  // Counter for the number of epochs completed so far. Used for per-epoch schedules
+  @Getter @Setter @lombok.Builder.Default protected int epochCount = 0;
+  @lombok.Builder.Default protected double dampingFactor = 100;
+  @EqualsAndHashCode.Exclude private IModel net;
+  private boolean initCalled = false;
   // gradient keys used for ensuring order when getting and setting the gradient
   @lombok.Builder.Default private LinkedHashSet<String> netWideVariables = new LinkedHashSet<>();
 
@@ -141,22 +108,19 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    */
   @Getter @Setter @Builder.Default private IUpdater updater = new Sgd();
 
-
   /**
-   * Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN.
-   * See {@link ConvolutionLayer.AlgoMode} for details.  Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
-   * <br>
-   * Note: values set by this method will be applied to all applicable layers in the network, unless a different
-   * value is explicitly set on a given layer. In other words: values set via this method are used as the default
-   * value, and can be overridden on a per-layer basis.
+   * Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage
+   * of cuDNN. See {@link ConvolutionLayer.AlgoMode} for details. Defaults to "PREFER_FASTEST", but
+   * "NO_WORKSPACE" uses less memory. <br>
+   * Note: values set by this method will be applied to all applicable layers in the network, unless
+   * a different value is explicitly set on a given layer. In other words: values set via this
+   * method are used as the default value, and can be overridden on a per-layer basis.
+   *
    * @param cudnnAlgoMode cuDNN algo mode to use
    */
-  @Getter
-  @Setter
-  @lombok.Builder.Default
+  @Getter @Setter @lombok.Builder.Default
   private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
 
-
   /**
    * Create a neural net configuration from json
    *
@@ -164,260 +128,23 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    * @return {@link NeuralNetConfiguration}
    */
   public static NeuralNetConfiguration fromJson(String json) {
-    NeuralNetConfiguration conf;
-    ObjectMapper mapper = NeuralNetConfiguration.mapper();
+    ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
     try {
-      conf = mapper.readValue(json, NeuralNetConfiguration.class);
-    } catch (InvalidTypeIdException e) {
-      if (e.getMessage().contains("@class")) {
-        try {
-          //JSON may be legacy (1.0.0-alpha or earlier), attempt to load it using old format
-          return JsonMappers.getLegacyMapper().readValue(json, NeuralNetConfiguration.class);
-        } catch (InvalidTypeIdException e2) {
-          //Check for legacy custom layers: "Could not resolve type id 'CustomLayer' as a subtype of [simple type, class org.deeplearning4j.nn.conf.layers.ILayer]: known type ids = [Bidirectional, CenterLossOutputLayer, CnnLossLayer, ..."
-          //1.0.0-beta5: dropping support for custom layers defined in pre-1.0.0-beta format. Built-in layers from these formats still work
-          String msg = e2.getMessage();
-          if (msg != null && msg.contains("Could not resolve type id")) {
-            throw new RuntimeException(
-                "Error deserializing NeuralNetConfiguration - configuration may have a custom " +
-                    "layer, vertex or preprocessor, in pre version 1.0.0-beta JSON format.\nModels in legacy format with custom"
-                    +
-                    " layers should be loaded in 1.0.0-beta to 1.0.0-beta4 and saved again, before loading in the current version of DL4J",
-                e);
-          }
-          throw new RuntimeException(e2);
-        } catch (IOException e2) {
-          throw new RuntimeException(e2);
-        }
-      }
-      throw new RuntimeException(e);
-    } catch (IOException e) {
-      //Check if this exception came from legacy deserializer...
-      String msg = e.getMessage();
-      if (msg != null && msg.contains("legacy")) {
-        throw new RuntimeException(
-            "Error deserializing NeuralNetConfiguration - configuration may have a custom " +
-                "layer, vertex or preprocessor, in pre version 1.0.0-alpha JSON format. These layers can be "
-                +
-                "deserialized by first registering them with NeuralNetConfiguration.registerLegacyCustomClassesForJSON(Class...)",
-            e);
-      }
+      return mapper.readValue(json, NeuralNetConfiguration.class);
+    } catch (JsonProcessingException e) {
       throw new RuntimeException(e);
     }
-
-    //To maintain backward compatibility after loss function refactoring (configs generated with v0.5.0 or earlier)
-    // Previously: enumeration used for loss functions. Now: use classes
-    // IN the past, could have only been an OutputLayer or RnnOutputLayer using these enums
-    int layerCount = 0;
-    JsonNode confs = null;
-    for (LayerConfiguration nnc : conf.getFlattenedLayerConfigurations()) {
-      LayerConfiguration l = nnc;
-      if (l instanceof BaseOutputLayer && ((BaseOutputLayer) l).getLossFunction() == null) {
-        //lossFn field null -> may be an old config format, with lossFunction field being for the enum
-        //if so, try walking the JSON graph to extract out the appropriate enum value
-
-        BaseOutputLayer ol = (BaseOutputLayer) l;
-        try {
-          JsonNode jsonNode = mapper.readTree(json);
-          if (confs == null) {
-            confs = jsonNode.get("confs");
-          }
-          if (confs instanceof ArrayNode) {
-            ArrayNode layerConfs = (ArrayNode) confs;
-            JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
-            if (outputLayerNNCNode == null) {
-              throw new RuntimeException(
-                  "should never happen"); //return conf; //Should never happen...
-            }
-            JsonNode outputLayerNode = outputLayerNNCNode.get("layer");
-
-            JsonNode lossFunctionNode = null;
-            if (outputLayerNode.has("output")) {
-              lossFunctionNode = outputLayerNode.get("output").get("lossFunction");
-            } else if (outputLayerNode.has("rnnoutput")) {
-              lossFunctionNode = outputLayerNode.get("rnnoutput").get("lossFunction");
-            }
-
-            if (lossFunctionNode != null) {
-              String lossFunctionEnumStr = lossFunctionNode.asText();
-              LossFunctions.LossFunction lossFunction = null;
-              try {
-                lossFunction = LossFunctions.LossFunction.valueOf(lossFunctionEnumStr);
-              } catch (Exception e) {
-                log.warn(
-                    "OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON",
-                    e);
-              }
-
-              if (lossFunction != null) {
-                switch (lossFunction) {
-                  case MSE:
-                    ol.setLossFunction(new LossMSE());
-                    break;
-                  case XENT:
-                    ol.setLossFunction(new LossBinaryXENT());
-                    break;
-                  case NEGATIVELOGLIKELIHOOD:
-                    ol.setLossFunction(new LossNegativeLogLikelihood());
-                    break;
-                  case MCXENT:
-                    ol.setLossFunction(new LossMCXENT());
-                    break;
-
-                  //Remaining: TODO
-                  case SQUARED_LOSS:
-                  case RECONSTRUCTION_CROSSENTROPY:
-                  default:
-                    log.warn(
-                        "OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not set loss function for {}",
-                        lossFunction);
-                    break;
-                }
-              }
-            }
-
-          } else {
-            log.warn(
-                "OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON: layer 'confs' field is not an ArrayNode (is: {})",
-                (confs != null ? confs.getClass() : null));
-          }
-        } catch (IOException e) {
-          log.warn(
-              "OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON",
-              e);
-          break;
-        }
-      }
-
-      //Also, pre 0.7.2: activation functions were Strings ("activationFunction" field), not classes ("activationFn")
-      //Try to load the old format if necessary, and create the appropriate IActivation instance
-      if ((l instanceof BaseLayerConfiguration) && ((BaseLayerConfiguration) l).getActivationFn() == null) {
-        try {
-          JsonNode jsonNode = mapper.readTree(json);
-          if (confs == null) {
-            confs = jsonNode.get("confs");
-          }
-          if (confs instanceof ArrayNode) {
-            ArrayNode layerConfs = (ArrayNode) confs;
-            JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
-            if (outputLayerNNCNode == null) {
-              throw new RuntimeException(
-                  "Should never happen"); //return conf; //Should never happen...
-            }
-            JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
-
-            if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
-              continue;
-            }
-
-            JsonNode layerNode = layerWrapperNode.elements().next();
-            JsonNode activationFunction = layerNode.get(
-                "activationFunction"); //Should only have 1 element: "dense", "output", etc
-
-            if (activationFunction != null) {
-              Activation ia = Activation.fromString(activationFunction.asText());
-              ((BaseLayerConfiguration) l).setActivation(ia.getActivationFunction());
-            }
-          }
-
-        } catch (IOException e) {
-          log.warn(
-              "ILayer with null ActivationFn field or pre-0.7.2 activation function detected: could not parse JSON",
-              e);
-        }
-      }
-
-      if (!handleLegacyWeightInitFromJson(json, l, mapper, confs, layerCount)) {
-        return conf;
-      }
-
-      layerCount++;
-    }
-    return conf;
-  }
-
-  /**
-   * Handle {@link WeightInit} and {@link Distribution} from legacy configs in Json format. Copied
-   * from handling of {@link Activation} above.
-   *
-   * @return True if all is well and layer iteration shall continue. False else-wise.
-   */
-  private static boolean handleLegacyWeightInitFromJson(String json, LayerConfiguration l,
-      ObjectMapper mapper,
-      JsonNode confs, int layerCount) {
-    if ((l instanceof BaseLayerConfiguration) && ((BaseLayerConfiguration) l).getWeightInit() == null) {
-      try {
-        JsonNode jsonNode = mapper.readTree(json);
-        if (confs == null) {
-          confs = jsonNode.get("confs");
-        }
-        if (confs instanceof ArrayNode) {
-          ArrayNode layerConfs = (ArrayNode) confs;
-          JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
-          if (outputLayerNNCNode == null) {
-            return false; //Should never happen...
-          }
-          JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
-
-          if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
-            return true;
-          }
-
-          JsonNode layerNode = layerWrapperNode.elements().next();
-          JsonNode weightInit = layerNode.get(
-              "weightInit"); //Should only have 1 element: "dense", "output", etc
-          JsonNode distribution = layerNode.get("dist");
-
-          Distribution dist = null;
-          if (distribution != null) {
-            dist = mapper.treeToValue(distribution, Distribution.class);
-          }
-
-          if (weightInit != null) {
-            final IWeightInit wi = WeightInit.valueOf(weightInit.asText())
-                .getWeightInitFunction(dist);
-            ((BaseLayerConfiguration) l).setWeightInit(wi);
-          }
-        }
-
-      } catch (IOException e) {
-        log.warn(
-            "ILayer with null WeightInit detected: " + l.getName() + ", could not parse JSON",
-            e);
-      }
-    }
-    return true;
-
-  }
-
-  /**
-   * Object mapper for serialization of configurations
-   *
-   * @return
-   */
-  public static ObjectMapper mapperYaml() {
-    return JsonMappers.getMapperYaml();
-  }
-
-  /**
-   * Object mapper for serialization of configurations
-   *
-   * @return
-   */
-  public static ObjectMapper mapper() {
-    return JsonMappers.getMapper();
   }
 
   public static NeuralNetConfiguration fromYaml(String input) {
-    throw new RuntimeException("Needs fixing - not supported."); //TODO
+    throw new RuntimeException("Needs fixing - not supported."); // TODO
   }
 
-
   /**
    * @return JSON representation of NN configuration
    */
   public String toYaml() {
-    ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
+    ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.YAML);
     synchronized (mapper) {
       try {
         return mapper.writeValueAsString(this);
@@ -431,10 +158,12 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    * @return JSON representation of NN configuration
    */
   public String toJson() {
-    ObjectMapper mapper = NeuralNetConfiguration.mapper();
+    ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
     synchronized (mapper) {
-      //JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields occasionally
-      //when writeValueAsString is used by multiple threads. This results in invalid JSON. See issue #3243
+      // JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields
+      // occasionally
+      // when writeValueAsString is used by multiple threads. This results in invalid JSON. See
+      // issue #3243
       try {
         return mapper.writeValueAsString(this);
       } catch (com.fasterxml.jackson.core.JsonProcessingException e) {
@@ -453,7 +182,9 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   public NeuralNetConfiguration clone() {
     NeuralNetConfiguration clone;
     clone = (NeuralNetConfiguration) super.clone();
-    if(getStepFunction() != null) {   clone.setStepFunction(getStepFunction().clone()); }
+    if (getStepFunction() != null) {
+      clone.setStepFunction(getStepFunction().clone());
+    }
     clone.netWideVariables = new LinkedHashSet<>(netWideVariables);
     clone.setInnerConfigurations(new ArrayList<>(innerConfigurations));
 
@@ -473,98 +204,109 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
     clone.setDataType(this.getDataType());
 
     return clone;
-
   }
 
-  /**
-   *
-   */
+  /** */
   @Override
   public void init() {
-    if(initCalled) return;
-    initCalled=true;
+    if (initCalled) return;
+    initCalled = true;
 
-    /**
-     * Run init() for each layer
-     */
-    for( NeuralNetConfiguration nconf : getNetConfigurations() ) {
+    /** Run init() for each layer */
+    for (NeuralNetConfiguration nconf : getNetConfigurations()) {
       nconf.init();
     }
-    //getNetConfigurations().stream().forEach( conf -> {
-     // conf.init(); //do not call on self
-    //}); //call init on all embedded net configurations
+    // getNetConfigurations().stream().forEach( conf -> {
+    // conf.init(); //do not call on self
+    // }); //call init on all embedded net configurations
 
-    //TODO do not put inside self to avoid serialization issues
+    // TODO do not put inside self to avoid serialization issues
     // innerConfigurations.add(0, this); //put this configuration at first place
 
+
+    getLayerConfigurations().stream()
+            .forEach(
+                    lconf ->
+                            lconf.setNetConfiguration(
+                                    this)); // set this as net config for all layers (defined in here, not stacked
+
+
     /**
-     * Inherit network wide configuration setting to those layer configurations
-     * that do not have an individual setting (nor a default)
+     * Inherit network wide configuration setting to those layer configurations that do not have an
+     * individual setting (nor a default)
      */
-    for(LayerConfiguration lconf : this.getFlattenedLayerConfigurations()) {
+    for (LayerConfiguration lconf : this.getFlattenedLayerConfigurations()) {
       lconf.runInheritance();
     }
 
-    getLayerConfigurations().stream().forEach( lconf -> lconf.setNetConfiguration(this)); //set this as net config for all layers (defined in here, not stacked
 
-
-    //Validate BackpropType setting
+    // Validate BackpropType setting
     if ((tbpttBackLength != DEFAULT_TBPTT_LENGTH || tbpttFwdLength != DEFAULT_TBPTT_LENGTH)
         && backpropType != BackpropType.TruncatedBPTT) {
-      log.warn("Truncated backpropagation through time lengths have been configured with values "
-          + tbpttFwdLength
-          + " and " + tbpttBackLength + " but backprop type is set to " + backpropType
-          + ". TBPTT configuration" +
-          " settings will only take effect if backprop type is set to BackpropType.TruncatedBPTT");
+      log.warn(
+          "Truncated backpropagation through time lengths have been configured with values "
+              + tbpttFwdLength
+              + " and "
+              + tbpttBackLength
+              + " but backprop type is set to "
+              + backpropType
+              + ". TBPTT configuration"
+              + " settings will only take effect if backprop type is set to BackpropType.TruncatedBPTT");
     }
 
     if (backpropType == BackpropType.TruncatedBPTT && isValidateTbpttConfig()) {
-      //Check for invalid combination - tbptt plus LastTimeStepLayer or
+      // Check for invalid combination - tbptt plus LastTimeStepLayer or
       for (int i = 0; i < getFlattenedLayerConfigurations().size(); i++) {
         LayerConfiguration l = getFlattenedLayerConfigurations().get(i);
         if (l instanceof LastTimeStep || l instanceof GlobalPoolingLayer) {
           throw new IllegalStateException(
               "Invalid network configuration detected: Truncated backpropagation through time (TBPTT)"
-                  +
-                  " cannot be used with layer " + i + " of type " + l.getClass().getName()
-                  + ": TBPTT is incompatible with this layer type (which is designed " +
-                  "to process entire sequences at once, and does support the type of sequence segments that TPBTT uses).\n"
-                  +
-                  "This check can be disabled using validateTbpttConfig(false) but this is not recommended.");
+                  + " cannot be used with layer "
+                  + i
+                  + " of type "
+                  + l.getClass().getName()
+                  + ": TBPTT is incompatible with this layer type (which is designed "
+                  + "to process entire sequences at once, and does support the type of sequence segments that TPBTT uses).\n"
+                  + "This check can be disabled using validateTbpttConfig(false) but this is not recommended.");
         }
       }
     }
 
     if (getInputType() == null && inputPreProcessors.get(0) == null) {
-      //User hasn't set the InputType. Sometimes we can infer it...
-      // For example, Dense/RNN layers, where preprocessor isn't set -> user is *probably* going to feed in
+      // User hasn't set the InputType. Sometimes we can infer it...
+      // For example, Dense/RNN layers, where preprocessor isn't set -> user is *probably* going to
+      // feed in
       // standard feedforward or RNN data
-      //This isn't the most elegant implementation, but should avoid breaking backward compatibility here
-      //Can't infer InputType for CNN layers, however (don't know image dimensions/depth)
+      // This isn't the most elegant implementation, but should avoid breaking backward
+      // compatibility here
+      // Can't infer InputType for CNN layers, however (don't know image dimensions/depth)
       LayerConfiguration firstLayer = getFlattenedLayerConfigurations().get(0);
       if (firstLayer instanceof BaseRecurrentLayer) {
         BaseRecurrentLayer brl = (BaseRecurrentLayer) firstLayer;
         val nIn = brl.getNIn();
         if (nIn > 0) {
-          setInputType( InputType.recurrent(nIn, brl.getDataFormat()));
+          setInputType(InputType.recurrent(nIn, brl.getDataFormat()));
         }
-      } else if (firstLayer instanceof DenseLayer || firstLayer instanceof EmbeddingLayer
+      } else if (firstLayer instanceof DenseLayer
+          || firstLayer instanceof EmbeddingLayer
           || firstLayer instanceof OutputLayer) {
-        //Can't just use "instanceof FeedForwardLayer" here. ConvolutionLayer is also a FeedForwardLayer
+        // Can't just use "instanceof FeedForwardLayer" here. ConvolutionLayer is also a
+        // FeedForwardLayer
         FeedForwardLayer ffl = (FeedForwardLayer) firstLayer;
         val nIn = ffl.getNIn();
         if (nIn > 0) {
-          setInputType( InputType.feedForward(nIn));
+          setInputType(InputType.feedForward(nIn));
         }
       }
     }
 
-    //Add preprocessors and set nIns, if InputType has been set
+    // Add preprocessors and set nIns, if InputType has been set
     // Builder.inputType field can be set in 1 of 4 ways:
     // 1. User calls setInputType directly
     // 2. Via ConvolutionLayerSetup -> internally calls setInputType(InputType.convolutional(...))
-    // 3. Via the above code: i.e., assume input is as expected  by the RNN or dense layer -> sets the inputType field
-    if(inputPreProcessors == null) {
+    // 3. Via the above code: i.e., assume input is as expected  by the RNN or dense layer -> sets
+    // the inputType field
+    if (inputPreProcessors == null) {
       inputPreProcessors = new HashMap<>();
     }
     if (getInputType() != null) {
@@ -572,10 +314,11 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
       for (int i = 0; i < getFlattenedLayerConfigurations().size(); i++) {
         LayerConfiguration l = getFlattenedLayerConfigurations().get(i);
         if (inputPreProcessors.get(i) == null) {
-          //Don't override preprocessor setting, but set preprocessor if required...
+          // Don't override preprocessor setting, but set preprocessor if required...
           @NonNull
           InputPreProcessor inputPreProcessor = l.getPreProcessorForInputType(currentInputType);
           if (inputPreProcessor != null) {
+            inputPreProcessors = new HashMap<>(inputPreProcessors);
             inputPreProcessors.put(i, inputPreProcessor);
           }
         }
@@ -586,41 +329,47 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
         }
         if (i > 0) {
           LayerConfiguration layer = getFlattenedLayerConfigurations().get(i - 1);
-          //convolution 1d is an edge case where it has rnn input type but the filters
-          //should be the output
-          if (layer instanceof Convolution1DLayer) {
+          // convolution 1d is an edge case where it has rnn input type but the filters
+          // should be the output
+          if (layer instanceof Convolution1D || layer instanceof Convolution1DNew) {
             if (l instanceof DenseLayer && getInputType() instanceof InputType.InputTypeRecurrent) {
               FeedForwardLayer feedForwardLayer = (FeedForwardLayer) l;
               if (getInputType() instanceof InputType.InputTypeRecurrent) {
-                InputType.InputTypeRecurrent recurrent = (InputType.InputTypeRecurrent) getInputType();
+                InputType.InputTypeRecurrent recurrent =
+                    (InputType.InputTypeRecurrent) getInputType();
                 feedForwardLayer.setNIn(recurrent.getTimeSeriesLength());
               }
             } else {
-              l.setNIn(currentInputType,
-                  isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
+              l.setNIn(
+                  currentInputType,
+                  isOverrideNinUponBuild()); // Don't override the nIn setting, if it's manually set
+                                             // by the user
             }
           } else {
-            l.setNIn(currentInputType,
-                isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
+            l.setNIn(
+                currentInputType,
+                isOverrideNinUponBuild()); // Don't override the nIn setting, if it's manually set
+                                           // by the user
           }
 
         } else {
-          l.setNIn(currentInputType,
-              isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
+          l.setNIn(
+              currentInputType,
+              isOverrideNinUponBuild()); // Don't override the nIn setting, if it's manually set by
+                                         // the user
         }
 
         currentInputType = l.getOutputType(i, currentInputType);
       }
-
     }
 
     Nd4j.getRandom().setSeed(getSeed());
 
-    //Validate output layer configuration
+    // Validate output layer configuration
     if (isValidateOutputLayerConfig()) {
-      //Validate output layer configurations...
+      // Validate output layer configurations...
       for (LayerConfiguration n : getFlattenedLayerConfigurations()) {
-        OutputLayerUtil.validateOutputLayer(n.getName(), n); //No-op for non output/loss layers
+        OutputLayerUtil.validateOutputLayer(n.getName(), n); // No-op for non output/loss layers
       }
     }
   }
@@ -646,26 +395,28 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
         layerName = String.valueOf(i);
       }
 
-      //Pass input type through preprocessor, if necessary
+      // Pass input type through preprocessor, if necessary
       InputPreProcessor preproc = getInputPreProcess(i);
-      //TODO memory requirements for preprocessor
+      // TODO memory requirements for preprocessor
       if (preproc != null) {
         inputType = preproc.getOutputType(inputType);
       }
 
-      LayerMemoryReport report = getFlattenedLayerConfigurations().get(i).getMemoryReport(inputType);
+      LayerMemoryReport report =
+          getFlattenedLayerConfigurations().get(i).getMemoryReport(inputType);
       memoryReportMap.put(layerName, report);
 
       inputType = getFlattenedLayerConfigurations().get(i).getOutputType(i, inputType);
     }
 
-    return new NetworkMemoryReport(memoryReportMap, NeuralNetConfiguration.class,
-        "MultiLayerNetwork", inputType);
+    return new NetworkMemoryReport(
+        memoryReportMap, NeuralNetConfiguration.class, "MultiLayerNetwork", inputType);
   }
 
   /**
    * For the given input shape/type for the network, return a list of activation sizes for each
-   * layer in the network.<br> i.e., list.get(i) is the output activation sizes for layer i
+   * layer in the network.<br>
+   * i.e., list.get(i) is the output activation sizes for layer i
    *
    * @param inputType Input type for the network
    * @return A lits of activation types for the network, indexed by layer number
@@ -699,38 +450,47 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   public void addNetWideVariable(String variable) {
     if (!netWideVariables.contains(variable)) {
       netWideVariables.add(variable);
-      log.trace("Adding neural network wide variable '{}' to the list of variables. New length is {}.", variable, netWideVariables.size());
+      log.trace(
+          "Adding neural network wide variable '{}' to the list of variables. New length is {}.",
+          variable,
+          netWideVariables.size());
     }
-    log.trace("Skipped adding neural network wide variable '{}' to the list of variables. It was already present. Length remains {}.", variable, netWideVariables.size());
+    log.trace(
+        "Skipped adding neural network wide variable '{}' to the list of variables. It was already present. Length remains {}.",
+        variable,
+        netWideVariables.size());
   }
 
   public void clearNetWideVariable() {
 
     netWideVariables.clear();
-    log.trace("Adding neural network wide variables have been cleared. New length is {}.", netWideVariables.size());
+    log.trace(
+        "Adding neural network wide variables have been cleared. New length is {}.",
+        netWideVariables.size());
   }
 
-
-
   /**
-   * From the list of layers and neural net configurations, only return the Layer Configurations that
-   * are defined in this neural network (it does not include embedded neural network configuration
-   * layers)
+   * From the list of layers and neural net configurations, only return the Layer Configurations
+   * that are defined in this neural network (it does not include embedded neural network
+   * configuration layers)
+   *
    * @return list with layer configurations
    */
   @JsonIgnore
   public List<LayerConfiguration> getLayerConfigurations() {
     return innerConfigurations.stream()
         .filter(obj -> (obj instanceof LayerConfiguration))
-        .map( obj -> (LayerConfiguration)obj )
-        .collect( Collectors.toList());
+        .map(obj -> (LayerConfiguration) obj)
+        .collect(Collectors.toList());
   }
 
   /**
-   * From the list of layers and neural net configurations, only return the neural net configurations
+   * From the list of layers and neural net configurations, only return the neural net
+   * configurations
+   *
    * @return list with neural net configurations
    */
-  //@Synchronized("innerConfigurationsLock")
+  // @Synchronized("innerConfigurationsLock")
   @JsonIgnore
   public List<NeuralNetConfiguration> getNetConfigurations() {
     List<NeuralNetConfiguration> list;
@@ -751,35 +511,47 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    * @return list of layer configurations
    */
   public List<LayerConfiguration> getFlattenedLayerConfigurations(NeuralNetConfiguration conf) {
-    List<LayerConfiguration> ret = new ArrayList<>(); //create the final return list
-    //When properly initialized, _this_ configuration is set first in the list, however we
-    //can find cases where this is not true, thus the first configuration is another net or layer configuration
-    //and should not be skipped. In essence, skip first configuration if that is "this".
-    //TODO: skipping not needed anymore as we removed _this_ from innerConfigurations
+    List<LayerConfiguration> ret = new ArrayList<>(); // create the final return list
+    // When properly initialized, _this_ configuration is set first in the list, however we
+    // can find cases where this is not true, thus the first configuration is another net or layer
+    // configuration
+    // and should not be skipped. In essence, skip first configuration if that is "this".
+    // TODO: skipping not needed anymore as we removed _this_ from innerConfigurations
     int iSkip = 0;
-    if(conf.getInnerConfigurations().size()>0 && conf.getInnerConfigurations().get(0).equals(this)) { iSkip=1;}
-    conf.getInnerConfigurations().stream().skip(iSkip)
-            .forEach(obj -> {
-              //if Layer Config, include in list and inherit parameters from this conf
-              //else if neural net configuration, call self recursively to resolve layer configurations
+    if (conf.getInnerConfigurations().size() > 0
+        && conf.getInnerConfigurations().get(0).equals(this)) {
+      iSkip = 1;
+    }
+    conf.getInnerConfigurations().stream()
+        .skip(iSkip)
+        .forEach(
+            obj -> {
+              // if Layer Config, include in list and inherit parameters from this conf
+              // else if neural net configuration, call self recursively to resolve layer
+              // configurations
               if (obj instanceof LayerConfiguration) {
                 ((LayerConfiguration) obj).setNetConfiguration(conf);
                 ret.add((LayerConfiguration) obj);
-                } else if (obj instanceof NeuralNetConfiguration)
-                ret.addAll(getFlattenedLayerConfigurations(
-                        (NeuralNetConfiguration) obj));
+              } else if (obj instanceof NeuralNetConfiguration)
+                ret.addAll(getFlattenedLayerConfigurations((NeuralNetConfiguration) obj));
               else {
                 log.error(
-                        "The list of layers and neural network configurations does contain an object of {}. Element will be ignored.",
-                        obj.getClass().getSimpleName());
+                    "The list of layers and neural network configurations does contain an object of {}. Element will be ignored.",
+                    obj.getClass().getSimpleName());
               }
             });
+    // make sure the indexes are sequenced properly
+    AtomicInteger i = new AtomicInteger();
+    ret.forEach(obj -> {
+      obj.setIndex(i.getAndIncrement());
+    });
     return ret;
   }
 
   /**
-   * Sames as {@link #getFlattenedLayerConfigurations(NeuralNetConfiguration)}, but uses this configurations
-   * list of configurations
+   * Sames as {@link #getFlattenedLayerConfigurations(NeuralNetConfiguration)}, but uses this
+   * configurations list of configurations
+   *
    * @return list of layer configurations
    */
   @JsonIgnore
@@ -789,6 +561,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
 
   /**
    * Add a new layer to the first position
+   *
    * @param layer configuration
    */
   public void setLayer(@NonNull LayerConfiguration layer) {
@@ -801,26 +574,28 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   }
 
   /**
-   * Deprecated, do not use. Workaround for old tests
-   * and getFlattenedLayerConfigurations().get(0);
+   * Deprecated, do not use. Workaround for old tests and getFlattenedLayerConfigurations().get(0);
+   *
    * @return
    */
-  @Deprecated @JsonIgnore
+  @Deprecated
+  @JsonIgnore
   public LayerConfiguration getFirstLayer() {
     log.warn("This getFirstLayer method is an ugly workaround and will be removed.");
     return getFlattenedLayerConfigurations().get(0);
   }
 
+  /*
 
+    protected boolean canEqual(final Object other) {
+      return other instanceof NeuralNetConfiguration;
+    }
+  */
 
-  protected boolean canEqual(final Object other) {
-    return other instanceof NeuralNetConfiguration;
-  }
-
-
-  public static abstract class NeuralNetConfigurationBuilder<C extends NeuralNetConfiguration,
-      B extends NeuralNetConfiguration.NeuralNetConfigurationBuilder<C, B>> extends
-      NeuralNetBaseBuilderConfigurationBuilder<C, B> {
+  public abstract static class NeuralNetConfigurationBuilder<
+          C extends NeuralNetConfiguration,
+          B extends NeuralNetConfiguration.NeuralNetConfigurationBuilder<C, B>>
+      extends NeuralNetBaseBuilderConfigurationBuilder<C, B> {
 
     public ComputationGraphConfiguration.GraphBuilder graphBuilder() {
       return new ComputationGraphConfiguration.GraphBuilder(this);
@@ -829,10 +604,9 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
     public NeuralNetConfigurationBuilder clone() {
       try {
         return (NeuralNetConfigurationBuilder) super.clone();
-      } catch(CloneNotSupportedException ex) {
+      } catch (CloneNotSupportedException ex) {
         throw new RuntimeException(ex);
       }
     }
-
   }
 }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/RNNFormat.java

@@ -21,7 +21,13 @@
 
 package org.deeplearning4j.nn.conf;
 
+/**
+ * N is the batch size<br/>
+ * C is the number of feature maps (that is,, number of channels)<br/>
+ * H is the image height (not used for 1D conv as this is an RNN format<br/>
+ * W is the image width<br/>
+ * **/
 public enum RNNFormat implements DataFormat {
-    NCW,
-    NWC
+    /** n=batch size; c=channels/ features; w=width **/ NCW,
+    /** n=batch size; w=width; c=channels/ features **/ NWC
 }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/BaseConstraint.java

@@ -20,6 +20,9 @@
 
 package org.deeplearning4j.nn.conf.constraint;
 
+import java.util.HashSet;
+import java.util.Map;
+import java.util.Set;
 import lombok.*;
 import org.apache.commons.lang3.ArrayUtils;
 import org.deeplearning4j.nn.api.Layer;
@@ -27,11 +30,6 @@ import org.deeplearning4j.nn.api.ParamInitializer;
 import org.deeplearning4j.nn.api.layers.LayerConstraint;
 import org.nd4j.linalg.api.ndarray.INDArray;
 
-import java.util.HashSet;
-import java.util.Map;
-import java.util.Set;
-
-
 @AllArgsConstructor
 @EqualsAndHashCode
 @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/MaxNormConstraint.java

@@ -20,6 +20,8 @@
 
 package org.deeplearning4j.nn.conf.constraint;
 
+import java.util.Collections;
+import java.util.Set;
 import lombok.Data;
 import lombok.EqualsAndHashCode;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -27,9 +29,6 @@ import org.nd4j.linalg.factory.Broadcast;
 import org.nd4j.linalg.indexing.BooleanIndexing;
 import org.nd4j.linalg.indexing.conditions.Conditions;
 
-import java.util.Collections;
-import java.util.Set;
-
 @Data
 @EqualsAndHashCode(callSuper = true)
 public class MaxNormConstraint extends BaseConstraint {