Fixing tests

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

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

@@ -331,7 +331,6 @@ 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

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

@@ -21,6 +21,8 @@
 package org.deeplearning4j.nn.conf.layers;
 
 import java.util.List;
+
+import lombok.NoArgsConstructor;
 import lombok.experimental.SuperBuilder;
 import net.brutex.ai.dnn.api.LayerType;
 import org.deeplearning4j.nn.api.ParamInitializer;
@@ -29,7 +31,7 @@ import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.params.EmptyParamInitializer;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.learning.regularization.Regularization;
-
+@NoArgsConstructor
 @SuperBuilder
 public abstract class NoParamLayer extends LayerConfiguration {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/convolutional/Cropping3D.java

@@ -40,6 +40,7 @@ import java.util.Collection;
 import java.util.Map;
 
 @Data
+@NoArgsConstructor
 @EqualsAndHashCode(callSuper = true)
 @SuperBuilder(builderMethodName = "innerBuilder")
 public class Cropping3D extends NoParamLayer {

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

@@ -827,7 +827,7 @@ public class MultiLayerNetwork extends ArtificialNeuralNetwork
   }
 
   /**
-   * This method allows you to specificy GradientsAccumulator instance to be used with this model
+   * This method allows you to specify GradientsAccumulator instance to be used with this model
    * <br>
    * <br>
    * PLEASE NOTE: Do not use this method unless you understand how to use GradientsAccumulator &