Fixing tests

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

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);
+            String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(e);
-            EpochTerminationCondition c = NeuralNetConfiguration.mapper().readValue(s, EpochTerminationCondition.class);
+            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);
+            String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(i);
-            IterationTerminationCondition c = NeuralNetConfiguration.mapper().readValue(s, IterationTerminationCondition.class);
+            IterationTerminationCondition c = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(s, IterationTerminationCondition.class);
             assertEquals(i, c);
         }
     }

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

@@ -112,7 +112,7 @@ 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))
                                                     .layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
                                                             .stride(stride).nIn(convNIn).nOut(convNOut1).hasBias(false)

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

@@ -41,8 +41,8 @@ public class JsonTest extends BaseDL4JTest {
 
         };
         for(InputPreProcessor p : pp ){
-            String s = NeuralNetConfiguration.mapper().writeValueAsString(p);
+            String s = CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(p);
-            InputPreProcessor p2 = NeuralNetConfiguration.mapper().readValue(s, InputPreProcessor.class);
+            InputPreProcessor p2 = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(s, InputPreProcessor.class);
             assertEquals(p, p2);
         }
 

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

@@ -34,6 +34,7 @@ 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;
@@ -110,7 +111,7 @@ 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 +128,7 @@ 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 +141,7 @@ 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 +161,7 @@ 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/NeuralNetBaseBuilderConfiguration.java

@@ -21,6 +21,7 @@
 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;
@@ -39,6 +40,7 @@ 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.CavisMapper;
 import org.deeplearning4j.nn.conf.serde.JsonMappers;
 import org.deeplearning4j.nn.conf.stepfunctions.StepFunction;
 import org.deeplearning4j.nn.conf.weightnoise.IWeightNoise;
@@ -417,113 +419,6 @@ 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;
@@ -562,15 +457,7 @@ 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();
-    }
-    @JsonIgnore
-    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
@@ -897,6 +784,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      *
      * @param distribution Distribution to use for weight initialization
      */
+    @JsonIgnore @Deprecated
     public B weightInit(Distribution distribution) {
       this.weightInit$value = new WeightInitDistribution(distribution);
       this.weightInit$set = true;
@@ -909,6 +797,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
       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;
@@ -921,6 +810,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      * @param distribution
      * @return
      */
+    @JsonIgnore
     public B dist(@NonNull Distribution distribution) {
       return weightInit(distribution);
     }
@@ -951,5 +841,6 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
       innerConfigurations$set = true;
       return self();
     }
+
   }
 }

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

@@ -24,39 +24,24 @@ import com.fasterxml.jackson.annotation.JsonIgnore;
 import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
 import com.fasterxml.jackson.core.JsonProcessingException;
 import com.fasterxml.jackson.databind.*;
-import com.fasterxml.jackson.databind.annotation.JsonSerialize;
+import java.util.*;
-import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
+import java.util.stream.Collectors;
-import com.fasterxml.jackson.databind.json.JsonMapper;
-import com.fasterxml.jackson.databind.node.ArrayNode;
 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.conf.serde.CavisMapper;
-import org.deeplearning4j.nn.weights.IWeightInit;
-import org.deeplearning4j.nn.weights.WeightInit;
 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
@@ -64,71 +49,50 @@ 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:
+ * and how fast the model should learn. This is what one configuration would look like: <br>
- * <br/><br/>
+ * <br>
- *
+ * NeuralNetConfiguration conf = NeuralNetConfiguration.builder()<br>
- * NeuralNetConfiguration conf = NeuralNetConfiguration.builder()<br/>
+ * .weightInit(WeightInit.XAVIER) .activation(Activation.RELU)<br>
- * .weightInit(WeightInit.XAVIER) .activation(Activation.RELU)<br/>
+ * .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)<br>
- * .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)<br/>
+ * .updater(new Sgd(0.05)) //... other hyperparameters <br>
- * .updater(new Sgd(0.05)) //... other hyperparameters <br/>
+ * .backprop(true)<br>
- *  .backprop(true)<br/>
+ * .build();<br>
- * .build();<br/><br/>
+ * <br>
- *
+ * With Deeplearning4j, you add a layer by calling layer on the
- * With Deeplearning4j, you add a layer
+ * NeuralNetConfiguration.NeuralNetConfigurationBuilder(), specifying its place in the order of
- * 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/>
+ * nIn and nOut, as well as the type: DenseLayer.<br>
- *
+ * <br>
- * .layer(0, DenseLayer.builder().nIn(784).nOut(250)<br/>
+ * .layer(0, DenseLayer.builder().nIn(784).nOut(250)<br>
- * .build())<br/><br/>
+ * .build())<br>
- *
+ * <br>
- * Once you've configured your net, you train the
+ * Once you've configured your net, you train the model with model.fit.
- * model with model.fit.
  */
-
-
 @Data
 @Slf4j
-@Jacksonized
+@JsonIgnoreProperties(value = {"net"})
-@JsonIgnoreProperties(value={"net"}, ignoreUnknown = true)
+@EqualsAndHashCode(callSuper = true)
-@EqualsAndHashCode(exclude = {"net"}, callSuper = true)
+// @JsonIdentityInfo(generator= ObjectIdGenerators.IntSequenceGenerator.class, property="@id")
-//@JsonIdentityInfo(generator= ObjectIdGenerators.IntSequenceGenerator.class, property="@id")
 
-//The inner builder, that we can then extend ...
+// The inner builder, that we can then extend ...
-@SuperBuilder //TODO fix access
+@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
+  @Getter @Setter @NonNull @lombok.Builder.Default @Deprecated
-  @NonNull
-  @lombok.Builder.Default
-  @Deprecated
   protected WorkspaceMode inferenceWorkspaceMode = WorkspaceMode.ENABLED;
 
-
+  @Getter @Setter @lombok.Builder.Default protected int iterationCount = 0;
-  @Getter
+  // Counter for the number of epochs completed so far. Used for per-epoch schedules
-  @Setter
+  @Getter @Setter @lombok.Builder.Default protected int epochCount = 0;
-  @lombok.Builder.Default
+  @lombok.Builder.Default protected double dampingFactor = 100;
-  protected int iterationCount = 0;
+  @EqualsAndHashCode.Exclude private IModel net;
-  //Counter for the number of epochs completed so far. Used for per-epoch schedules
+  private boolean initCalled = false;
-  @Getter
-  @Setter
-  @lombok.Builder.Default
-  protected int epochCount = 0;
-  @lombok.Builder.Default
-  protected double dampingFactor = 100;
   // gradient keys used for ensuring order when getting and setting the gradient
   @lombok.Builder.Default private LinkedHashSet<String> netWideVariables = new LinkedHashSet<>();
 
@@ -143,22 +107,19 @@ 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.
+   * Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage
-   * See {@link ConvolutionLayer.AlgoMode} for details.  Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
+   * of cuDNN. See {@link ConvolutionLayer.AlgoMode} for details. Defaults to "PREFER_FASTEST", but
-   * <br>
+   * "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
+   * Note: values set by this method will be applied to all applicable layers in the network, unless
-   * value is explicitly set on a given layer. In other words: values set via this method are used as the default
+   * a different value is explicitly set on a given layer. In other words: values set via this
-   * value, and can be overridden on a per-layer basis.
+   * method are used as the default value, and can be overridden on a per-layer basis.
+   *
    * @param cudnnAlgoMode cuDNN algo mode to use
    */
-  @Getter
+  @Getter @Setter @lombok.Builder.Default
-  @Setter
-  @lombok.Builder.Default
   private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
 
-
   /**
    * Create a neural net configuration from json
    *
@@ -166,270 +127,23 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    * @return {@link NeuralNetConfiguration}
    */
   public static NeuralNetConfiguration fromJson(String json) {
-    //ObjectMapper mapper = NeuralNetConfiguration.mapper();
+    ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
-    JsonMapper mapper = JsonMapper.builder()
-            .enable(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES)
-            .build();
     try {
       return mapper.readValue(json, NeuralNetConfiguration.class);
     } catch (JsonProcessingException e) {
       throw new RuntimeException(e);
     }
-/*
-    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);
-      }
-      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);
@@ -443,14 +157,12 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    * @return JSON representation of NN configuration
    */
   public String toJson() {
-    JsonMapper mapper = JsonMapper.builder()
+    ObjectMapper mapper = CavisMapper.getMapper(CavisMapper.Type.JSON);
-            .enable(SerializationFeature.INDENT_OUTPUT)
-            .enable(MapperFeature.SORT_PROPERTIES_ALPHABETICALLY)
-            .build();
-    //ObjectMapper mapper = NeuralNetConfiguration.mapper();
     synchronized (mapper) {
-      //JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields occasionally
+      // JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields
-      //when writeValueAsString is used by multiple threads. This results in invalid JSON. See issue #3243
+      // 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) {
@@ -469,7 +181,9 @@ 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));
 
@@ -489,98 +203,106 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
     clone.setDataType(this.getDataType());
 
     return clone;
-
   }
 
-  /**
+  /** */
-   *
-   */
   @Override
   public void init() {
-    if(initCalled) return;
+    if (initCalled) return;
-    initCalled=true;
+    initCalled = true;
 
-    /**
+    /** Run init() for each layer */
-     * Run init() for each layer
+    for (NeuralNetConfiguration nconf : getNetConfigurations()) {
-     */
-    for( NeuralNetConfiguration nconf : getNetConfigurations() ) {
       nconf.init();
     }
-    //getNetConfigurations().stream().forEach( conf -> {
+    // getNetConfigurations().stream().forEach( conf -> {
-     // conf.init(); //do not call on self
+    // conf.init(); //do not call on self
-    //}); //call init on all embedded net configurations
+    // }); //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
 
     /**
-     * Inherit network wide configuration setting to those layer configurations
+     * Inherit network wide configuration setting to those layer configurations that do not have an
-     * that do not have an individual setting (nor a default)
+     * 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
+    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 "
+      log.warn(
-          + tbpttFwdLength
+          "Truncated backpropagation through time lengths have been configured with values "
-          + " and " + tbpttBackLength + " but backprop type is set to " + backpropType
+              + tbpttFwdLength
-          + ". TBPTT configuration" +
+              + " and "
-          " settings will only take effect if backprop type is set to BackpropType.TruncatedBPTT");
+              + 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 "
-                  " cannot be used with layer " + i + " of type " + l.getClass().getName()
+                  + i
-                  + ": TBPTT is incompatible with this layer type (which is designed " +
+                  + " of type "
-                  "to process entire sequences at once, and does support the type of sequence segments that TPBTT uses).\n"
+                  + l.getClass().getName()
-                  +
+                  + ": TBPTT is incompatible with this layer type (which is designed "
-                  "This check can be disabled using validateTbpttConfig(false) but this is not recommended.");
+                  + "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...
+      // 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
+      // 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
+      // This isn't the most elegant implementation, but should avoid breaking backward
-      //Can't infer InputType for CNN layers, however (don't know image dimensions/depth)
+      // 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
+    // 3. Via the above code: i.e., assume input is as expected  by the RNN or dense layer -> sets
-    if(inputPreProcessors == null) {
+    // the inputType field
+    if (inputPreProcessors == null) {
       inputPreProcessors = new HashMap<>();
     }
     if (getInputType() != null) {
@@ -588,7 +310,7 @@ 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) {
@@ -602,41 +324,47 @@ 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
+          // convolution 1d is an edge case where it has rnn input type but the filters
-          //should be the output
+          // should be the output
           if (layer instanceof Convolution1DLayer) {
             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,
+              l.setNIn(
-                  isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
+                  currentInputType,
+                  isOverrideNinUponBuild()); // Don't override the nIn setting, if it's manually set
+                                             // by the user
             }
           } else {
-            l.setNIn(currentInputType,
+            l.setNIn(
-                isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
+                currentInputType,
+                isOverrideNinUponBuild()); // Don't override the nIn setting, if it's manually set
+                                           // by the user
           }
 
         } else {
-          l.setNIn(currentInputType,
+          l.setNIn(
-              isOverrideNinUponBuild()); //Don't override the nIn setting, if it's manually set by the user
+              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
       }
     }
   }
@@ -662,26 +390,28 @@ 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,
+    return new NetworkMemoryReport(
-        "MultiLayerNetwork", inputType);
+        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
@@ -715,38 +445,47 @@ 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
+   * From the list of layers and neural net configurations, only return the Layer Configurations
-   * are defined in this neural network (it does not include embedded neural network configuration
+   * that are defined in this neural network (it does not include embedded neural network
-   * layers)
+   * configuration layers)
+   *
    * @return list with layer configurations
    */
   @JsonIgnore
   public List<LayerConfiguration> getLayerConfigurations() {
     return innerConfigurations.stream()
         .filter(obj -> (obj instanceof LayerConfiguration))
-        .map( obj -> (LayerConfiguration)obj )
+        .map(obj -> (LayerConfiguration) obj)
-        .collect( Collectors.toList());
+        .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;
@@ -767,35 +506,42 @@ 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
+    List<LayerConfiguration> ret = new ArrayList<>(); // create the final return list
-    //When properly initialized, _this_ configuration is set first in the list, however we
+    // 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
+    // can find cases where this is not true, thus the first configuration is another net or layer
-    //and should not be skipped. In essence, skip first configuration if that is "this".
+    // configuration
-    //TODO: skipping not needed anymore as we removed _this_ from innerConfigurations
+    // 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;}
+    if (conf.getInnerConfigurations().size() > 0
-    conf.getInnerConfigurations().stream().skip(iSkip)
+        && conf.getInnerConfigurations().get(0).equals(this)) {
-            .forEach(obj -> {
+      iSkip = 1;
-              //if Layer Config, include in list and inherit parameters from this conf
+    }
-              //else if neural net configuration, call self recursively to resolve layer configurations
+    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)
+              } else if (obj instanceof NeuralNetConfiguration)
-                ret.addAll(getFlattenedLayerConfigurations(
+                ret.addAll(getFlattenedLayerConfigurations((NeuralNetConfiguration) obj));
-                        (NeuralNetConfiguration) obj));
               else {
                 log.error(
-                        "The list of layers and neural network configurations does contain an object of {}. Element will be ignored.",
+                    "The list of layers and neural network configurations does contain an object of {}. Element will be ignored.",
-                        obj.getClass().getSimpleName());
+                    obj.getClass().getSimpleName());
               }
             });
     return ret;
   }
 
   /**
-   * Sames as {@link #getFlattenedLayerConfigurations(NeuralNetConfiguration)}, but uses this configurations
+   * Sames as {@link #getFlattenedLayerConfigurations(NeuralNetConfiguration)}, but uses this
-   * list of configurations
+   * configurations list of configurations
+   *
    * @return list of layer configurations
    */
   @JsonIgnore
@@ -805,6 +551,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
 
   /**
    * Add a new layer to the first position
+   *
    * @param layer configuration
    */
   public void setLayer(@NonNull LayerConfiguration layer) {
@@ -817,26 +564,28 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   }
 
   /**
-   * Deprecated, do not use. Workaround for old tests
+   * Deprecated, do not use. Workaround for old tests and getFlattenedLayerConfigurations().get(0);
-   * 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) {
+  public abstract static class NeuralNetConfigurationBuilder<
-    return other instanceof NeuralNetConfiguration;
+          C extends NeuralNetConfiguration,
-  }
+          B extends NeuralNetConfiguration.NeuralNetConfigurationBuilder<C, B>>
-
+      extends NeuralNetBaseBuilderConfigurationBuilder<C, B> {
-
-  public static abstract class NeuralNetConfigurationBuilder<C extends NeuralNetConfiguration,
-      B extends NeuralNetConfiguration.NeuralNetConfigurationBuilder<C, B>> extends
-      NeuralNetBaseBuilderConfigurationBuilder<C, B> {
 
     public ComputationGraphConfiguration.GraphBuilder graphBuilder() {
       return new ComputationGraphConfiguration.GraphBuilder(this);
@@ -845,10 +594,9 @@ 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/distribution/Distribution.java

@@ -25,8 +25,8 @@ import com.fasterxml.jackson.annotation.JsonTypeInfo;
 
 import java.io.Serializable;
 
-@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "type",
+
-                defaultImpl = LegacyDistributionHelper.class)
+@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, property = "@class")
 public abstract class Distribution implements Serializable, Cloneable {
 
     private static final long serialVersionUID = 5401741214954998498L;

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

@@ -20,6 +20,7 @@
 
 package org.deeplearning4j.nn.conf.distribution;
 
+import com.fasterxml.jackson.annotation.JsonTypeInfo;
 import lombok.Data;
 import lombok.EqualsAndHashCode;
 import com.fasterxml.jackson.annotation.JsonCreator;
@@ -48,21 +49,7 @@ public class NormalDistribution extends Distribution {
         this.std = std;
     }
 
-    public double getMean() {
-        return mean;
-    }
 
-    public void setMean(double mean) {
-        this.mean = mean;
-    }
-
-    public double getStd() {
-        return std;
-    }
-
-    public void setStd(double std) {
-        this.std = std;
-    }
 
     @Override
     public int hashCode() {

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

@@ -24,6 +24,7 @@ import lombok.EqualsAndHashCode;
 import lombok.NonNull;
 import org.deeplearning4j.nn.conf.CacheMode;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.nd4j.linalg.api.buffer.DataBuffer;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.buffer.util.DataTypeUtil;
@@ -151,7 +152,7 @@ public abstract class MemoryReport {
 
     public String toJson() {
         try {
-            return NeuralNetConfiguration.mapper().writeValueAsString(this);
+            return CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(this);
         } catch (JsonProcessingException e) {
             throw new RuntimeException(e);
         }
@@ -159,7 +160,7 @@ public abstract class MemoryReport {
 
     public String toYaml() {
         try {
-            return NeuralNetConfiguration.mapperYaml().writeValueAsString(this);
+            return CavisMapper.getMapper(CavisMapper.Type.YAML).writeValueAsString(this);
         } catch (JsonProcessingException e) {
             throw new RuntimeException(e);
         }
@@ -167,7 +168,7 @@ public abstract class MemoryReport {
 
     public static MemoryReport fromJson(String json) {
         try {
-            return NeuralNetConfiguration.mapper().readValue(json, MemoryReport.class);
+            return CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(json, MemoryReport.class);
         } catch (IOException e) {
             throw new RuntimeException(e);
         }
@@ -175,7 +176,7 @@ public abstract class MemoryReport {
 
     public static MemoryReport fromYaml(String yaml) {
         try {
-            return NeuralNetConfiguration.mapperYaml().readValue(yaml, MemoryReport.class);
+            return CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(yaml, MemoryReport.class);
         } catch (IOException e) {
             throw new RuntimeException(e);
         }

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

@@ -251,7 +251,7 @@ public abstract class BaseNetConfigDeserializer<T> extends StdDeserializer<T> im
                     Distribution d = null;
                     if(w == WeightInit.DISTRIBUTION && on.has("dist")){
                         String dist = on.get("dist").toString();
-                        d = NeuralNetConfiguration.mapper().readValue(dist, Distribution.class);
+                        d = CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(dist, Distribution.class);
                     }
                     IWeightInit iwi = w.getWeightInitFunction(d);
                     baseLayerConfiguration.setWeightInit(iwi);

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

@@ -0,0 +1,60 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * 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.nn.conf.serde;
+
+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.json.JsonMapper;
+import com.fasterxml.jackson.dataformat.yaml.YAMLMapper;
+import lombok.NonNull;
+
+public class CavisMapper {
+
+  public static ObjectMapper getMapper(@NonNull Type type) {
+    ObjectMapper mapper;
+    switch (type) {
+      case JSON:
+        mapper = JsonMapper.builder()
+                .enable(SerializationFeature.INDENT_OUTPUT)
+                .enable(MapperFeature.SORT_PROPERTIES_ALPHABETICALLY)
+                .enable(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES)
+                .enable(DeserializationFeature.FAIL_ON_INVALID_SUBTYPE)
+                .enable(DeserializationFeature.FAIL_ON_IGNORED_PROPERTIES)
+                .build();
+        break;
+      case YAML:
+        mapper = YAMLMapper.builder().build();
+        break;
+      default:
+        throw new RuntimeException("Mapper type not recognised.");
+    }
+
+    return mapper;
+  }
+
+  public static enum Type {
+    JSON,
+    YAML
+  }
+}

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

@@ -96,7 +96,7 @@ public class ComputationGraphConfigurationDeserializer
             }
             jsonSubString = s.substring((int) charOffsetStart - 1, charOffsetEnd.intValue());
 
-            ObjectMapper om = NeuralNetConfiguration.mapper();
+            ObjectMapper om = CavisMapper.getMapper(CavisMapper.Type.JSON);
             JsonNode rootNode = om.readTree(jsonSubString);
 
             ObjectNode verticesNode = (ObjectNode) rootNode.get("vertices");

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

@@ -78,7 +78,7 @@ public class NeuralNetConfigurationDeserializer extends BaseNetConfigDeserialize
             }
             String jsonSubString = s.substring((int) charOffsetStart - 1, (int) charOffsetEnd);
 
-            ObjectMapper om = NeuralNetConfiguration.mapper();
+            ObjectMapper om = CavisMapper.getMapper(CavisMapper.Type.JSON);
             JsonNode rootNode = om.readTree(jsonSubString);
 
             ArrayNode confsNode = (ArrayNode)rootNode.get("confs");

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/transferlearning/FineTuneConfiguration.java

@@ -47,6 +47,7 @@ import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerValidation;
 import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.deeplearning4j.nn.conf.stepfunctions.StepFunction;
 import org.deeplearning4j.nn.conf.weightnoise.IWeightNoise;
 import org.deeplearning4j.nn.weights.IWeightInit;
@@ -118,7 +119,7 @@ public class FineTuneConfiguration {
 
   public static FineTuneConfiguration fromJson(String json) {
     try {
-      return NeuralNetConfiguration.mapper().readValue(json, FineTuneConfiguration.class);
+      return CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(json, FineTuneConfiguration.class);
     } catch (IOException e) {
       throw new RuntimeException(e);
     }
@@ -126,7 +127,7 @@ public class FineTuneConfiguration {
 
   public static FineTuneConfiguration fromYaml(String yaml) {
     try {
-      return NeuralNetConfiguration.mapperYaml().readValue(yaml, FineTuneConfiguration.class);
+      return CavisMapper.getMapper(CavisMapper.Type.YAML).readValue(yaml, FineTuneConfiguration.class);
     } catch (IOException e) {
       throw new RuntimeException(e);
     }
@@ -322,7 +323,7 @@ public class FineTuneConfiguration {
 
   public String toJson() {
     try {
-      return NeuralNetConfiguration.mapper().writeValueAsString(this);
+      return CavisMapper.getMapper(CavisMapper.Type.JSON).writeValueAsString(this);
     } catch (JsonProcessingException e) {
       throw new RuntimeException(e);
     }
@@ -330,7 +331,7 @@ public class FineTuneConfiguration {
 
   public String toYaml() {
     try {
-      return NeuralNetConfiguration.mapperYaml().writeValueAsString(this);
+      return CavisMapper.getMapper(CavisMapper.Type.YAML).writeValueAsString(this);
     } catch (JsonProcessingException e) {
       throw new RuntimeException(e);
     }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/weights/IWeightInit.java

@@ -26,7 +26,7 @@ import com.fasterxml.jackson.annotation.JsonTypeInfo;
 
 import java.io.Serializable;
 
-@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
+@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, property = "@class")
 @JsonAutoDetect(fieldVisibility = JsonAutoDetect.Visibility.ANY, getterVisibility = JsonAutoDetect.Visibility.NONE,
         setterVisibility = JsonAutoDetect.Visibility.NONE)
 public interface IWeightInit extends Serializable {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/weights/WeightInitDistribution.java

@@ -20,19 +20,24 @@
 
 package org.deeplearning4j.nn.weights;
 
-import lombok.EqualsAndHashCode;
+import com.fasterxml.jackson.annotation.JsonCreator;
+import lombok.*;
 import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.distribution.Distributions;
+import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.rng.distribution.impl.OrthogonalDistribution;
 import com.fasterxml.jackson.annotation.JsonProperty;
 
 @EqualsAndHashCode
+@NoArgsConstructor
 public class WeightInitDistribution implements IWeightInit {
 
-    private final Distribution distribution;
+    @Getter @Setter
+    private Distribution distribution;
 
-    public WeightInitDistribution(@JsonProperty("distribution") Distribution distribution) {
+
+    public WeightInitDistribution(@NonNull Distribution distribution) {
         if(distribution == null) {
             // Would fail later below otherwise
             throw new IllegalArgumentException("Must set distribution!");
@@ -40,6 +45,7 @@ public class WeightInitDistribution implements IWeightInit {
         this.distribution = distribution;
     }
 
+
     @Override
     public INDArray init(double fanIn, double fanOut, long[] shape, char order, INDArray paramView) {
         //org.nd4j.linalg.api.rng.distribution.Distribution not serializable

cavis-dnn/cavis-dnn-nn/src/test/java/net/brutex/ai/dnn/serde/NeuralNetConfigurationSerdeTest.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.ai.dnn.serde;
+
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
+import org.deeplearning4j.nn.conf.layers.DenseLayer;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.weights.WeightInit;
+import org.junit.jupiter.api.Test;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+import static org.junit.jupiter.api.Assertions.*;
+class NeuralNetConfigurationSerdeTest {
+
+    @Test
+    void toYaml() {
+      }
+
+    @Test
+    void toJson() {
+        final var conf = NeuralNetConfiguration.builder()
+                .weightInit(new NormalDistribution(3, 2))
+                .layer(DenseLayer.builder().nIn(100).nOut(30).build())
+                .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.SQUARED_LOSS).build())
+                .build();
+        assertEquals(conf.toJson(), NeuralNetConfiguration.fromJson(conf.toJson()).toJson());
+      }
+
+    @Test
+    void toJson2() {
+        final var conf = NeuralNetConfiguration.builder()
+                .weightInit(WeightInit.IDENTITY)
+                .layer(DenseLayer.builder().nIn(100).nOut(30).build())
+                .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.SQUARED_LOSS).build())
+                .build();
+        assertEquals(conf.toJson(), NeuralNetConfiguration.fromJson(conf.toJson()).toJson());
+    }
+
+    @Test
+    void fromJson() {
+        final String json = "{\n" +
+                "  \"activation\" : null,\n" +
+                "  \"seed\" : 12345, \n" +
+                "  \"allParamConstraints\" : [ ],\n" +
+                "  \"backpropType\" : \"Standard\",\n" +
+                "  \"biasConstraints\" : [ ],\n" +
+                "  \"biasInit\" : 0.0,\n" +
+                "  \"biasUpdater\" : null,\n" +
+                "  \"cacheMode\" : \"NONE\",\n" +
+                "  \"constrainWeights\" : [ ],\n" +
+                "  \"convolutionMode\" : \"Truncate\",\n" +
+                "  \"cudnnAlgoMode\" : \"PREFER_FASTEST\",\n" +
+                "  \"dampingFactor\" : 100.0,\n" +
+                "  \"dataType\" : \"FLOAT\",\n" +
+                "  \"epochCount\" : 0,\n" +
+                "  \"gainInit\" : 1.0,\n" +
+                "  \"gradientNormalization\" : \"None\",\n" +
+                "  \"gradientNormalizationThreshold\" : 0.0,\n" +
+                "  \"idropOut\" : null,\n" +
+                "  \"inferenceWorkspaceMode\" : \"ENABLED\",\n" +
+                "  \"initCalled\" : false,\n" +
+                "  \"innerConfigurations\" : [ {\n" +
+                "    \"org.deeplearning4j.nn.conf.layers.DenseLayer\" : {\n" +
+                "      \"activation\" : [ \"org.nd4j.linalg.activations.Activation\", \"IDENTITY\" ],\n" +
+                "      \"allParamConstraints\" : null,\n" +
+                "      \"biasConstraints\" : null,\n" +
+                "      \"biasInit\" : 0.0,\n" +
+                "      \"biasUpdater\" : null,\n" +
+                "      \"constrainWeights\" : [ ],\n" +
+                "      \"constraints\" : null,\n" +
+                "      \"dataType\" : null,\n" +
+                "      \"dropOut\" : null,\n" +
+                "      \"gainInit\" : 0.0,\n" +
+                "      \"gradientNormalization\" : \"None\",\n" +
+                "      \"gradientNormalizationThreshold\" : 1.0,\n" +
+                "      \"hasBias\" : true,\n" +
+                "      \"hasLayerNorm\" : false,\n" +
+                "      \"name\" : null,\n" +
+                "      \"nout\" : 30,\n" +
+                "      \"regularization\" : [ ],\n" +
+                "      \"regularizationBias\" : [ ],\n" +
+                "      \"type\" : \"UNKNOWN\",\n" +
+                "      \"variables\" : [ ],\n" +
+                "      \"weightConstraints\" : null,\n" +
+                "      \"weightInit\" : null,\n" +
+                "      \"weightNoise\" : null\n" +
+                "    }\n" +
+                "  }, {\n" +
+                "    \"org.deeplearning4j.nn.conf.layers.OutputLayer\" : {\n" +
+                "      \"activation\" : [ \"org.nd4j.linalg.activations.Activation\", \"IDENTITY\" ],\n" +
+                "      \"allParamConstraints\" : null,\n" +
+                "      \"biasConstraints\" : null,\n" +
+                "      \"biasInit\" : 0.0,\n" +
+                "      \"biasUpdater\" : null,\n" +
+                "      \"constrainWeights\" : [ ],\n" +
+                "      \"constraints\" : null,\n" +
+                "      \"dataType\" : null,\n" +
+                "      \"dropOut\" : null,\n" +
+                "      \"gainInit\" : 0.0,\n" +
+                "      \"gradientNormalization\" : \"None\",\n" +
+                "      \"gradientNormalizationThreshold\" : 1.0,\n" +
+                "      \"hasBias\" : true,\n" +
+                "      \"lossFunction\" : {\n" +
+                "        \"@class\" : \"org.nd4j.linalg.lossfunctions.impl.LossMSE\"\n" +
+                "      },\n" +
+                "      \"name\" : null,\n" +
+                "      \"nout\" : 0,\n" +
+                "      \"regularization\" : [ ],\n" +
+                "      \"regularizationBias\" : [ ],\n" +
+                "      \"type\" : \"UNKNOWN\",\n" +
+                "      \"variables\" : [ ],\n" +
+                "      \"weightConstraints\" : null,\n" +
+                "      \"weightInit\" : null,\n" +
+                "      \"weightNoise\" : null\n" +
+                "    }\n" +
+                "  } ],\n" +
+                "  \"inputPreProcessors\" : { },\n" +
+                "  \"inputType\" : null,\n" +
+                "  \"iterationCount\" : 0,\n" +
+                "  \"maxNumLineSearchIterations\" : 5,\n" +
+                "  \"miniBatch\" : false,\n" +
+                "  \"minimize\" : true,\n" +
+                "  \"name\" : \"Anonymous INeuralNetworkConfiguration\",\n" +
+                "  \"netWideVariables\" : [ ],\n" +
+                "  \"optimizationAlgo\" : \"STOCHASTIC_GRADIENT_DESCENT\",\n" +
+                "  \"overrideNinUponBuild\" : true,\n" +
+                "  \"regularization\" : [ ],\n" +
+                "  \"regularizationBias\" : [ ],\n" +
+                "  \"stepFunction\" : null,\n" +
+                "  \"tbpttBackLength\" : 20,\n" +
+                "  \"tbpttFwdLength\" : 20,\n" +
+                "  \"trainingWorkspaceMode\" : \"ENABLED\",\n" +
+                "  \"updater\" : {\n" +
+                "    \"@class\" : \"org.nd4j.linalg.learning.config.Sgd\",\n" +
+                "    \"learningRate\" : 0.001\n" +
+                "  },\n" +
+                "  \"validateOutputLayerConfig\" : true,\n" +
+                "  \"validateTbpttConfig\" : true,\n" +
+                "  \"weightInit\" : {\n" +
+                "    \"@class\" : \"org.deeplearning4j.nn.weights.WeightInitIdentity\",\n" +
+                "    \"scale\" : null\n" +
+                "  },\n" +
+                "  \"weightNoise\" : null\n" +
+                "}";
+        final var conf = NeuralNetConfiguration.builder()
+                .weightInit(WeightInit.IDENTITY)
+                .layer(DenseLayer.builder().nIn(100).nOut(30).build())
+                .layer(OutputLayer.builder().lossFunction(LossFunctions.LossFunction.SQUARED_LOSS).build())
+                .seed(12345)
+                .build();
+        NeuralNetConfiguration conf2 = NeuralNetConfiguration.fromJson(json);
+        assertEquals(conf.toJson(),conf2.toJson());
+        assertEquals(conf, conf2);
+    }
+
+}

cavis-ui/cavis-ui-vertx/src/main/java/org/deeplearning4j/ui/module/train/TrainModule.java

@@ -42,6 +42,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.graph.GraphVertex;
 import org.deeplearning4j.nn.conf.graph.LayerVertex;
 import org.deeplearning4j.nn.conf.layers.*;
+import org.deeplearning4j.nn.conf.serde.CavisMapper;
 import org.deeplearning4j.nn.conf.serde.JsonMappers;
 import org.deeplearning4j.ui.VertxUIServer;
 import org.deeplearning4j.ui.api.HttpMethod;
@@ -909,7 +910,7 @@ public class TrainModule implements UIModule {
         } else {
             try {
                 NeuralNetConfiguration layer =
-                        NeuralNetConfiguration.mapper().readValue(config, NeuralNetConfiguration.class);
+                        CavisMapper.getMapper(CavisMapper.Type.JSON).readValue(config, NeuralNetConfiguration.class);
                 return new Triple<>(null, null, layer);
             } catch (Exception e) {
                 log.error("",e);