Fixing tests

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

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

@@ -27,6 +27,7 @@ import com.fasterxml.jackson.databind.ObjectMapper;
 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.INeuralNetworkConfiguration;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
@@ -89,6 +90,7 @@ import java.util.*;
 @Slf4j
 // The inner builder, that we can then extend ...
 @SuperBuilder // TODO fix access
+@Jacksonized
 @EqualsAndHashCode
 public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetworkConfiguration {
 
@@ -895,7 +897,6 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
      *
      * @param distribution Distribution to use for weight initialization
      */
-    @JsonIgnore
     public B weightInit(Distribution distribution) {
       this.weightInit$value = new WeightInitDistribution(distribution);
       this.weightInit$set = true;

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

@@ -24,8 +24,12 @@ import com.fasterxml.jackson.annotation.JsonIgnore;
 import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
 import com.fasterxml.jackson.core.JsonProcessingException;
 import com.fasterxml.jackson.databind.JsonNode;
+import com.fasterxml.jackson.databind.MapperFeature;
 import com.fasterxml.jackson.databind.ObjectMapper;
+import com.fasterxml.jackson.databind.SerializationFeature;
+import com.fasterxml.jackson.databind.annotation.JsonSerialize;
 import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
+import com.fasterxml.jackson.databind.json.JsonMapper;
 import com.fasterxml.jackson.databind.node.ArrayNode;
 import lombok.*;
 import lombok.experimental.SuperBuilder;
@@ -165,7 +169,8 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    * @return {@link NeuralNetConfiguration}
    */
   public static NeuralNetConfiguration fromJson(String json) {
-    ObjectMapper mapper = NeuralNetConfiguration.mapper();
+    //ObjectMapper mapper = NeuralNetConfiguration.mapper();
+    JsonMapper mapper = JsonMapper.builder().build();
     try {
       return mapper.readValue(json, NeuralNetConfiguration.class);
     } catch (JsonProcessingException e) {
@@ -439,7 +444,11 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    * @return JSON representation of NN configuration
    */
   public String toJson() {
-    ObjectMapper mapper = NeuralNetConfiguration.mapper();
+    JsonMapper mapper = JsonMapper.builder()
+            .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
       //when writeValueAsString is used by multiple threads. This results in invalid JSON. See issue #3243

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

@@ -90,6 +90,7 @@ public abstract class InputType implements Serializable {
      *
      * @return int[]
      */
+    @JsonIgnore
     public long[] getShape() {
         return getShape(false);
     }
@@ -431,7 +432,7 @@ public abstract class InputType implements Serializable {
             return height * width * depth * channels;
         }
 
-        @Override
+        @Override @JsonIgnore
         public long[] getShape(boolean includeBatchDim) {
             if(dataFormat == Convolution3D.DataFormat.NDHWC){
                 if(includeBatchDim) return new long[]{-1, depth, height, width, channels};

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

@@ -40,6 +40,7 @@ import org.nd4j.linalg.factory.Nd4j;
 
 @EqualsAndHashCode(callSuper = true)
 @SuperBuilder(builderMethodName = "innerBuilder")
+@NoArgsConstructor
 public class CapsuleLayer extends SameDiffLayer {
 
     private static final String WEIGHT_PARAM = "weight";

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

@@ -92,6 +92,7 @@ public abstract class LayerConfiguration
    *
    * @return activation function
    */
+  @JsonIgnore
   public IActivation getActivationFn() {
     if (activation == null)
       throw new RuntimeException(

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/variational/LossFunctionWrapper.java

@@ -30,12 +30,12 @@ import com.fasterxml.jackson.annotation.JsonProperty;
 @Data
 public class LossFunctionWrapper implements ReconstructionDistribution {
 
-    private final IActivation activationFn;
+    private final IActivation activation;
     private final ILossFunction lossFunction;
 
-    public LossFunctionWrapper(@JsonProperty("activationFn") IActivation activationFn,
+    public LossFunctionWrapper(@JsonProperty("activation") IActivation activation,
                     @JsonProperty("lossFunction") ILossFunction lossFunction) {
-        this.activationFn = activationFn;
+        this.activation = activation;
         this.lossFunction = lossFunction;
     }
 
@@ -59,17 +59,17 @@ public class LossFunctionWrapper implements ReconstructionDistribution {
         //NOTE: The returned value here is NOT negative log probability, but it (the loss function value)
         // is equivalent, in terms of being something we want to minimize...
 
-        return lossFunction.computeScore(x, preOutDistributionParams, activationFn, null, average);
+        return lossFunction.computeScore(x, preOutDistributionParams, activation, null, average);
     }
 
     @Override
     public INDArray exampleNegLogProbability(INDArray x, INDArray preOutDistributionParams) {
-        return lossFunction.computeScoreArray(x, preOutDistributionParams, activationFn, null);
+        return lossFunction.computeScoreArray(x, preOutDistributionParams, activation, null);
     }
 
     @Override
     public INDArray gradient(INDArray x, INDArray preOutDistributionParams) {
-        return lossFunction.computeGradient(x, preOutDistributionParams, activationFn, null);
+        return lossFunction.computeGradient(x, preOutDistributionParams, activation, null);
     }
 
     @Override
@@ -82,11 +82,11 @@ public class LossFunctionWrapper implements ReconstructionDistribution {
     public INDArray generateAtMean(INDArray preOutDistributionParams) {
         //Loss functions: not probabilistic -> not random
         INDArray out = preOutDistributionParams.dup();
-        return activationFn.getActivation(out, true);
+        return activation.getActivation(out, true);
     }
 
     @Override
     public String toString() {
-        return "LossFunctionWrapper(afn=" + activationFn + "," + lossFunction + ")";
+        return "LossFunctionWrapper(afn=" + activation + "," + lossFunction + ")";
     }
 }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java

@@ -118,7 +118,7 @@ import org.nd4j.linalg.workspace.WorkspaceUtils;
  */
 @Slf4j
 // @JsonIdentityInfo(generator = ObjectIdGenerators.IntSequenceGenerator.class, property = "@id")
-@JsonIgnoreProperties({"helper", "net", "initCalled", "iupdater", "activationFn"})
+@JsonIgnoreProperties({"helper", "net", "initCalled", "iupdater"})
 public class MultiLayerNetwork extends ArtificialNeuralNetwork
     implements Serializable, Classifier, Layer, ITrainableLayer {